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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume 2, Issue 4

1

Grid Architecture and Implementation for ALICE Experiment253

Jin HUANG*, Pablo SAIZ**, Latchezar BETEV**, Federico CARMINATI**, Costin GRIGORAS**, Steffen SCHREINER***, Jianlin ZHU***** China Shipbuilding Industry Corporation, Wuhan, China, ** CERN, Geneva, Switzerland, *** Technische Universitaet Darmstadt, Germany, ****South-Central University for Nationalities, Wuhan City, China

2

Performances Analysis of Algebraic Space Time Code under Correlated and Uncorrelated Channels

262

Ines BEN HASSINE *, Ridha BOUALLEGUE
National Engineering School of Tunis, Tunis, Tunisia

3

Distributed Mobility Control Schemes in the HIP-based Mobile Networks

269

Sang-Il Choi, Seok-Joo Koh
Kyungpook National University, Daegu, Korea

4

Comparative Study on Cooperative Communications in the Upper Layers of Ad Hoc Networks

276

Jaeshin Jang*, Sunghong Wie**
INJE University, Pusan, Korea, The Cyber University of Korea, Seoul, Korea

5

Design of Small-Area Transimpedance Optical Receiver Module for Optical Interconnects
Jamshid Sangirov, Ikechi Augustine Ukaegbu, Nga T. H. Nguyen, Tae-Woo Lee, Mu-Hee Cho, and Hyo-Hoon Park
KAIST, Daejeon, Korea

283

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

253

Grid Architecture and Implementation
for ALICE Experiment
Jin HUANG*, Pablo SAIZ**, Latchezar BETEV**, Federico CARMINATI**, Costin GRIGORAS**,
Steffen SCHREINER***, Jianlin ZHU*****Research Institute 709, China Shipbuilding Industry Corporation, Wuhan, 430070, China** CERN,1211 Geneva,Switzerland*** Technische Universitaet Darmstadt,Germany**** South-Central University for Nationalities, Wuhan City, 430074, China
derick0320@gmail.com, pablo.saiz@cern.ch , latchezar.betev@cern.ch, federico.carminati@cern.ch, costin.grigoras@cern.ch,steffen.schreiner@cern.ch, dczhou.iopp@gmail.com, zhujianlin1120@gmail.com
Abstract —According to the requirement of the Physics Data
Challenge and many years of experiences from the actual
operations, we propose the Grid Architecture for the ALICE
Experiment. This architecture builds on AliEn2 framework andAliEn2 is the grid framework for the whole system’s
management and also provides the client interface for accessing
the ALICE Grid platform. On the base of AliEn2, we adopt somemiddlewares to make the platform more flexible, availability andefficiency. At the end, the whole system has been worldwide
deployed at around 88 sites for distributed production of raw
data and Monte Carlo data, detector simulation, reconstruction
and real data analysis. We observe the activities of the platformfrom different point of view and report the performance of thewhole ALICE grid platform. The whole Grid Architecture for
ALICE experiment plays an important role in High Energy
Physics. It successfully manages the resources distributed allover the world and has a great efficiency system to make all
peoples from and in different countries working together. The
experiments show the whole system works fine and efficient.
Keyword— Grid, AliEn,
Management, Middleware

Job

Management,

Transfer

Manuscript received May 15, 2013. This work was sponsored by the
National Natural Science Foundation of China with Key Grant
11020101060, and the “973” Grant of MOST of China
2013CB837803.
Jin HUANG is with Research Institute 709, China Shipbuilding
Industry Corporation, Wuhan,
China(Tel:86-13476165997;
email:derick0320@gmail.com)
Pablo SAIZ is with CERN, Geneva, Switzerland(email:pablo.saiz@cern.ch)
Latchezar BETEV is with CERN, Geneva, Switzerland(email:latchezar.betev@cern.ch)Federico CARMINATI is with CERN, Geneva, Switzerland(email:federico.carminati@cern.ch)
Costin GRIGORAS is with CERN, Geneva, Switzerland(email:costin.grigoras@cern.ch)
Steffen
SCHREINER
iswithTechnische
Universitaet
Darmstadt,Germany(email: steffen.schreiner@cern.ch)
Jianlin ZHU is with South-Central University for Nationalities,
Wuhan, China(email:zhujianlin1120@gmail.com)

I.

INTRODUCTION

Geneva, 30 March 2010, Beams collided at 7 TeV in the LHC
at 13:06 CEST, marks the start of the Large Hadron
Collider(LHC) research programme. The A Large Ion Collider
Experiment(ALICE) is one of the main experiements in LHC,
which is dedicated to the study of nucleus-nucleus collisions.
The ALICE experiment produces large amount of data
(peta byte data each year), which requires huge computing
power to compute and analysis the data, and Peta Byte orderof storage to store raw and analysed data. ALICE uses the grid
platform[1] to distribute and analyze all raw data and Monte
Carlo(MC) data among the 88 sites that participate in the
ALICE Collaboration[2].
In this paper, we give the architecture for the whole grid
platform and describe the main components in the system fora better understanding the system. At the final, we give theperformance analysis of the platform in different views relatedwith services, jobs, transfers and network since the first LHC
run.
II.

GRID ARCHITECTURE

Grid middleware distributions are often large software
artifacts, which include a set of components providing a basic
functionality. Such capabilities include (but are not limited to)job management, storage management, authentication andauthorization, and resource monitoring. Considering the
ALICE experiment:
1) The resources are complex which are distributed all over
the world and the reources are increasing every year .
2) The large amount of data (PB) produced by the ALICE
experiment every year which is related with DataManagement, Transfer Management,etc;
3) Each data analysis job has different requirements such as
different softwares and datasets which requires Package
Management;

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

4) The security framework has to be considered to ensure
Information Security, Monitoring, Authentication andAuthorization;5) Supporting other Virtual Organizations andmiddilewares.
With the considerations mensioned above, the Grid
Architecture designed for ALICE experiment is described in
Figure 1. In Figure 1, there are three main layers for the Grid
Architecture：1) External Software: These are the basic softwares which
are used to build the grid platform. The main frameowork isbased on AliEn2[3] which uses the perl language. AliEn2 is aframework that has been developed by the Alice Collaboration
with the aim of offering to the end user a transparent access to
distributed computing and storage resources all over the world.
LDAP is used to keep the static information about each site,user, service,etc. The relational database is adopted such as
mysql, postgresql, oracle,etc. The softwares required by the
Jobs are also supported such as AliRoot, Root, etc.

Figure 1. The Grid Architecture for ALICE Experiment2) AliEn Core Components and Services: The corecomponents of AliEn infrastructure[4] are Web Services,which communicate with each other via XML-based messages
using SOAP (Simple Object Access Protocol) protocol. The
services at AliEn2 are categorized into two aspects:• Central services- Authen, IS, Job/Transfer/JobInfo
Manager, Job/Transfer Broker,Job/Transfer/Catalogue
Optimizer,SE/ PackMan/Messages Master,Logger, API,
FTD.
• Site serivces- Cluster Monitor,PackMan, CE, SE,
MonaLisa. JobAgent service is running on each worker
node in the site.
All the public information about the Central Services andSite Services are published through the LDAP.
3) Interface: The Grid Architecture provides different
interfaces for the users to use the resources with different
purpose. Such as through the client of AliEn2, users could
submit/cancel/resubmit the jobs, add/delete/backup the files.

254

Though the web portal of monitoring system, users could
check the system running information in any place any time.
With the plugin mechanism, the middlewares could be
supported easily.
The required Grid functionality is provied via a
combination of the open source softwares, the common
middlewares offered by many Grid projects, and the ALICEspecific services from AliEn2.
A. Middlewares
There are some important middlewares that the ALICE
Grid Architecture adopts and have already been deployed in
the sites all over the world.
1) VOBOX[5]: It focus on the managing the services
deployed inside it and softwares for the data analysis,
providing renew proxy service for providing long-life proxies.
The mechanism in VOBOX is that a proxy renewal service
runs for automatic renewal or registering proxies via the
MyProxy mechanism to provide long-lived agents andservices. the proxy renewal service on the VOBOX uses a
setuid root cron job to generate a proxy for the machine, thenuses that proxy to authenticate to the MyProxy server in orderto renew the registered proxies.
2) CREAM(Computing Resource Execution And
Management service)[6,7]: It is a service for job management
at the Computing Element(CE) level. to which the CE hasinterface for supporting and managing different batch system.
Moreover, a CE implements additional features with respect
to the ones provided by the underlying batch system, such as
Grid-enabled user authentication and authorization,accounting, fault tolerance and improved performance andreliability.
3) Xrootd[8,9]: The xrootd system is a high-performancedata server. It is useful to build scalable, fault tolerant andhigh performance storage systems for POSIX-like data access.
Unlike NFS, the xrootd protocol includes a number of featureslike:• Communication optimizations, which allow clients toaccelerate data transfers.
• An extensive fault recovery protocol that allows datatransfers to be restarted at an alternate server.
• A comprehensive authentication framework.
• Peer-to-peer elements that allow xrootd servers tocluster together while still providing a uniform namespace.
4) CERN Advanced STORage manager(CASTOR)[10,11]:
It is a Hierarchical Storage Management system developed at
CERN used to store physics production files and user files.
Files can be stored, listed, retrieved and accessed in CASTOR
using command line tools or applications built on top of thedifferent data transfer protocols like RFIO (Remote File IO),
ROOT libraries, GridFTP and XROOTD. CASTOR manages
disk cache(s) and the data on tertiary storage or tapes. Xrootd
is one of the protocols supported for data transfer and access.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

B. Central Services and Site Services
The
LHC
experimenthas
fourexperiments(ALICE,ATLAS,CMS and LHCb) and each experiment is one
Virtual Organization(VO). One VO only has one set ofCentral services and could have many site services.
A typical deployment example in one VO is given in Figure
2 to show how the platform works.
The Central services along with LDAP server, File
Catalogue, TaskQueue and TransferQueue take responsible
for the management of the whole system and they are
deployed at CERN. In the section, we will discuss detailed
relationship between these services.
In Figure 2, to manage the local resources in one site, thefollowing components are needed:
1) Site Services (Cluster Monitor, PackMan, CE,
MonALISA) for communicating with the central services to
ask for jobs to execute;Figure 2. Central services and Site services in a VO

2) VO-BOX for providing a reliable mechanism to the site
services to accomplish various tasks(Start/Stop services
deployed at each site, renew proxies, installed software
management, etc).
3) CREAM-CE for getting the JobAgents from CE anddistributing them to worker nodes.
4) JobAgent service running on each worker node for doingsome tasks to make sure that the analysis jobs could beexecuted automatically and successfully, monitoring the statusof the analysis jobs, reporting the information about therunning jobs and asking more jobs to execute when the job isfinished.
5) The Storage Element is used for managing the file-based
data. Each site provides certain storage space for theexperiment and storage could be tape and disk. In Figure 2,
the xrootd protocol is chosen as the transfer protocol for datatransfer and data access.
III.
A. Jon Management

IMPLEMENTATION255

In the system, Job Management is mainly related with Job
Manager, Job Broker, Job Optimizer, JobInfo Manger , Task
Queue and Authen from Central Services, CE, Cluster
Monitor, JobAgents in one site.
The whole workload management system is based on acentral job queue which holds all tasks that have to be
executed. The Job Broker model is based on pilot jobs: thesystem submits generic pilots to the computing centres batch
gateways, and the assignment of a real job is done only when
the pilot wakes up on the worker node. The model facilitates a
flexible fair share user job distribution.
A Computing Element(CE) is some set of computing
resources localized at a site. A CE includes a Grid Gate(GG),
which acts as a generic interface to the cluster; a Local
Resource Management System(LRMS), and the cluster itself,a collection of Worker Nodes(WNs) where the nodes the jobs
run. The Grid Gate in ALICE is CREAM-CE, which is
responsible for accepting jobs and dispatching them forexecution on the WNs via the LRMS. The LRMS types are
OpenPBS/PBSPro,LSF,Maui/Torque,BQS and Condor.
The jobs in ALICE experiment are described by the Job
Description Language(JDL). The users submit the jobs to theCentral services , the related services processes the JDL,
inserted into the Task Queue and distrbuted the jobs to theproper sites. Figure 3 shows the Job Flow Model of theplatform. The main components in the Job Flow Model is asfollows:
1) TaskQueue is the database with all the Jobs that have to
be done. The heart of the job management system is thecentral TaskQueue which calculates the priorities of the jobs.
SITE_CE is the database with all the jobs detail information
running in each site.
2) Job Manager service is responsible for accepting jobs
submitted from the users and inserting jobs into TaskQueue,
checking the job quota of the user, managing each status of
JobAgent and each file generated by the jobs.
3) Job Broker service is in charge of distributing the jobs
to each site matching the requirement of the jobs. If the
TaskQueue is the heart of the job model, the Job Broker is themind. The Job Broker is responsible for deviding which job
will be executed on site. The decision will be made dependingon the data that the job has to analyze, the software packages,
time needed by the job and any specific user
requirements(name of the site, partition, disk space, etc). The
coversation between Job Broker and CE use pull model, and itmeans that CEs periodically ask the Job Broker whether it has
jobs to be executed. If the JDL of the CE matches the JDL ofJobs, Job Broker will send the JDLs of the jobs to that CE.
Otherwise, the CE will sleep for a while and then ask the Job
Broker again.
4) Job Optimizer service does JDL parameter optimization
for all the jobs waiting to be executed. According to theinformation gathered from the whole system, the Job
Optimizer could change the content of JDL and make the jobs
easier be matched with the JDL of CE. Along the same lines,

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one can also implement policy monitors to enforce VO
policies by altering job priorities.the Broker can modify the
job's JDL entry by adding or elaborating requirements basedon the detailed information it can get from the system like theexact location of the dataset and replicas,client and service
capabilities. The job execution model AliEN uses a pull model.
In a pull implementation,the Broker does not need to maintainthe status of the system.
From the Figure 3, we could see that after the CE in one site
get JDL of jobs from the Central services, it will cooperatewith CREAM-CE to send the jobs to the worker nodes for
execution.
The execution of jobs is based on central services, and thesite services act as the interface to the local resources. The
site services announce their capabilities, and based on that, thecentral services will decide which job can be assigned there.
Acorrding to Figure 4, the whole status of job execution could
be described in four stages:

256

whether it has the suitable jobs for this site. The Job Broker
Service checks the queue status of this site whether it is open
or not, finds the suitable jobs which match this site and sends
them to the ClusterMonitor Services, and updates the job
status to “ASSIGNED”. After receiving the jobs from
ClusterMonitor, The JobAgent starts to run the jobs.
According the jobs’ JDL, it creates directories , starts themonitoring process to report the status to ClusterMontior
which will send the message to the Job Manager Service.
During the execution of the job, the job status could be“STARTED”, “RUNNING”, “SAVING”, ”SAVED”. At thesame time, the JobAgent also includes starting monitor,
sending JobAgent status, executing the job, checking process,executing ValidationScript, registering logs, etc. The
JobAgent
statuscouldbe“JOB_STARTED”,“RUNNING_JOB”, “SAVED”.

Figure 4. General status job execution diagram

Figure 3. Job Flow Model

1) Submission: The user submits jobs to the CE service.
The CE service gets the JDL for this job, then reports this job
to the Job Manager Service. The Job Manager Service firstly
checks whether the user has enough job quota to execute thejob, then does some extra preparement work for this job, andupdate the job status to “INSERTING”.
2) Optimizer: The Job Optimizer Service is used to
optimize the jobs with the “INSERTING” status. The Job
Optimizer Service firstly inits the execution environment in
AliEn2 client shell for the job, prepares the job token, inserts
the job into TaskQueue and updates the job status to“WAITING”.
3) Broker/Agent: The JobAgent Service asks the
ClusterMonitor Service for jobs to execute.
The
ClusterMonitor sends messages to the Job Broker Services4) Output registration: The Job Optimizer Service finds
the jobs marked with “SAVED” and “SAVED_WARN” from
TaskQueue, then creates the output information in the user’s
AliEn client, registers the files in the catalogue, and updates
the job stauts to “DONE”.
B. Transfer Management
With the consideration of safety of the data, resource usage,
ALICE group uses different strategies to deal with data takenfrom the detectors, data from the users, and data from the jobs.
In ALICE experiment, one copy of the raw data from theexperiment will be stored at CERN(Tier 0) and a second copy
will be distributed among the external Tier 1 centres outsideof CERN, thus providing a natural backup. Reconstruction to
the Event Summary Data(ESD) level will be shared by theTier1 centres, with the CERN Tier 0 responsible for the firstreconstruction pass. Subsequent data reduction to the Analysis
Object Data(AOD) level, all Tier 1 and 2 centres will

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participate analysis and Monte Carlo production. The Tier 1swill perform reconstruction and scheduled ananlysis, while theTier 2s will perform Monte Carlo production and end-user
analysis. For all procedures and data analysis mensioned
above, the system faces a large mount of data transfer andneeds an efficiency Transfer Management to handle all thesituations.
The Transfer Management in ALICE experiment includes
file catalogue, Transfer Manager, Transfer Broker, Transfer
Optimizer, Transfer Queue, FTD and other Storage
Management middlewares( Xrootd, CASTOR, etc). The grid
platform provides the basic command to store, copy anddelete files. Futher more, ALICE also provides functions
dealing with the multi-copies of the data, the data transfer
between different data server and the data access to the files.
The files are identified by a Logical File Name(LFN) in thegrid platform. The files aslo are identified by a Globally
Unique Identifier(GUID),which is a fixed-format string
generated by the middleware and guaranteed to be absolutely
unique. When the user requests a transfer, the LFN, thedestination Storage Element(SE) and the type of transfer haveto be specified. The JDL syntax for transfer jobs has thefollowing fields: LFN of the file to be replicated; SE where
the file has to be replicated; size of the file; Type of
replication (Cache,mirror or masterCopy); Requirements ofthe transfer. The meaning is as follows:
1) Cache: the transferred file will not be registered in the
catalogue.
2) Mirror: the new PFN will be inserted in the
catalogue,and any user from the same site will get this new
PFN instead of the original.
3) masterCopy: the PFN will be registered as the master
PFN, and the previous PFN will be mirrors.
In Figure 5, The main components in the Transfer Flow
Model is as follows:
1) A database( TransferQueue ) with all the transfers thathave to be done; the file catalogue stores the connections
between the LFN and pointers to any replicas.
2) Transfer Broker service keeps a description in JDL foreach FTD. The description includes name of the FTD; SEclose to the FTD; free space in the cache. Each FTD sends its
JDL to the Transfer Broker, and the Broker tries to match it
against the list of transfers that have to be done. If there is anaction that the FTD could do, the Broker sends a JDL with thedescription of what the FTD is supposed to do. If there was no
action, the FTD will sleep for a while and request anothertransfer when it wakes up.
3) Transfer Manager service is responsible for insertingtransfer in the system, changing their status and measuring thetime each transfer spent in each state.
4) Transfer Optimizer service is responsible for taking all
the new requested transfer, and specifying the PFN and SE
that currently has that LFN.
FTD
supports
many
protocols,suchas
xrd3cp,xrdcp,bbftp,cp,GridFTP,GSIFTP,etc. Most of the sites

257

support xrdcp and xrd3cp. Xrd3cp and xrdcp are able totransfer data files at high speed by using a large number ofparrallel TCP connections.
According Figure 6, the transfer status could be categories
into four stages.
1) Mirror: When the system receives the “mirror”
command, firstly checks the permission of the user, checks thepath whether the user has permission to use, gets theinformation about where the source data is kept, reports thetransfer to the Transfer Management Service. The Transfer
Management Service registers this transfer information into
the Transfer Database.

Figure 5. Transfer Flow Model

2) Optimizer: The Transfer Optimizer Service changes thestatus of this transfer into “INSERTING”. The Transfer
Optimizer Service got the information such as SE, guid,
LFN,etc, then finds the common protocols for this transfer,and updates the status of transfer into “WAITING”.

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Figure 6. General Status Transfer diagram

3) Broker: The Service FTD requests the transfer from theBroker Manager Service, and updates the status of transfer
into “ASSIGNED”.
4) FTD: The Service FTD got the transfer JDL , asks theTransfer Manager Service changes the status of transfer to“TRANSFERRING”. The Service FTD starts to prepare theenvelope information for transferring the files. First the FTDgets user and access information in both source SE and target
SE from the Authen Service. The FTD starts the transfer withthe setting protocol and asks the Transfer Manager Service tochange the status tranfer to “DONE” when the transfer isfinished.
C. Packages Management
AliEn provides a service for automatic installation of thesoftware packages required by the job. The installation,
upgrade, configuration and removal of application software
packages are done by the AliEn Package Manager (PackMan)
service. This service is a helper service that automates theprocess of installing, upgrading, configuring, and remove
software packages from a shared area (software cache ) on a
Grid site. The Package Manager Service does not manage theinstallation of middleware software; it manage the installationof middleware software; it manages the packages that arecommon for all users of a VO and possibly packages provides
by individual users.
The packages are installed on demand, when requested by
the Job Agent running on a worker node or upon an explicit
request by the VO Software Manager. The Package Manager
checks if the requested package is already installed in thecache and if that is not the case, it proceeds with the
installation. The right version of the package is downloaded
from the File Catalogue and installed in the directory specified
as a service configuration parameter. It will also install all thedependencies required by the package. The Package Manager
returns a string with a command (a shell script) that the client258

has to execute to configure the package and all its
dependencies. This script performs actions like setting
environment variables. Figure 7 shows which commands use
could use to install the packages in the client interface of
AliEn2.
The package installation can be triggered by a process
running on the worker node or by a person with appropriate
privileges (identified by the certificate subject). In both cases
the requestor obtains the lease for the package for a specified
time. The Package Manager manages the local disk cache andwill clear the disk space if it needs the disk space to installnewer packages but it will not remove the packages for which
someone holds the lease. The maximum lease time for the
packages is a configurable parameter. While any user orprocess can list already installed packages, only the VO
administrator can remove a package from the local cacheregardless of its current lease status and in that case thecurrently running jobs requiring that package might fail.
Removing a package does not remove the packages that
depend on it. If any of those packages are used, the removed
package dependency will be automatically installed again.

Figure 7. The process of PackMan

D. Security Framework
Considering that the resources for the grid platform in
ALICE experiment distribute all over the world, the Security
Infrastructure for AliEn2 can be divided into five layers.
General overview of these levels is described in Figure 8.

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259

For ALICE experiment, we use MonALISA to monitor thewhole grid platform. MonALISA[14] is a globally scalable
framework of services to monitor and help manage andoptimize the operational performance of Grids, networks andrunning applications in real-time. The information starts fromMarch 20,2010. The information provided by MonALISA
could be seen from the statistics listed as follows.
Figure 9 is the entrance of MonALISA monitoring system
for ALICE experiment and it shows the MonALISA
Repository for ALICE experiment and the MonALISA collect
more than 1700 metrics.

Figure 8. The Security Framework for AliEn21) Layer 1: Message Format: the message exchanged
between different services is based on Simple Object AccessProtocol;2) Layer 2: Message protection: the protection for thecommunication channel for AliEn2 adopts SSL/TLS protocol.
SSL/TLS protocol ensures the transport level security. It
provides for both integrity protection and privacy (viaencryption);
3) Layer 3: Delegation/Authentication: The credentials in
AliEn2 follows X.509 specification. X.509 EECs are for
global authentication. They provide each entity with a unique
identifier and a method to assert that identifier to another party
through the use of an asymmetric key pair bound to theidentifier by the certificate;X.509 proxy certificates areextension to X.509 certificates. GSI proxy certificates and
VOMS proxy certificates as a means of authenticating userand delegating authority from users to remote servers. They
could bridge disparate security mechanisms, basic support fordelegation, policy distribution, single sign-on;4) Layer 4: The Authorization uses AuthZ mechanisms in
AliEn2. AuthZ mechanisms are based on the File Catalogue
and Quota to authorize the uses to access a certain set ofresources.
5) Layer 5: Management: there could be many different
and effective managements integrated into this layer, such as
Trust and Reputation management could be designed for
improving the efficiency of the AliEn2. Monitor system is tomonitor the resource usage. Trust management system tocreate, negotiate, and manage trust between other systems or"strangers".
The detail implementation of the information security andAuthentication could see the paper[12]. The authorization forfile catalogues, files transfer could check paper[13].

IV.

Figure 9. MonALISA Repository for ALICE

Figure 10 is the number of running jobs every day in eachsite. The peak number of jobs which are running at the sametime in the grid system is 32538.

Figure 10.

The total number of running jobs per day

Figure 11 describes the RAW data collected from thedetectors of ALICE experiment. This is the real data which
was recorded during the beam-beam collision and the
maximum size of the RAW data is close to 6PB. From the
Figure 11, the update of the softwares has been done during
the closure of detectors.

EXPERIMENTAL RESULTS

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

260

There are 54 disk storage elements and 8 tape storages ingrid plarform of the ALICE experiment. In Table 1, total sizeof all the storage elements are 43.67PB and 18.344 PB is used.
There are 187,720,276 files saved in the SE.
TABLE 1. DIFFERENT TYPE OF STORAGE ELEMENTS

StorageType
Disk storage
Tape storage
Figure 11.

Raw data taken from detectors of ALICE

Figure 12 shows the network traffic of some SEs. The
maximum incoming traffic is 87.51 MB/s and the maximum
outgoing traffic is 2.341 MB/s. The central services willarrage the transfer jobs according to the traffic size of each SE
which could avoid the traffic bottleneck.

Total size
12.87 PB
30.8 PB

Incoming Traffic and Outgoing Traffic of

SEs
There are 8 tape storage elements(ALICE::CCIN2P3::TAPE,
ALICE::CERN::CASTOR2,
ALICE::CERN::TOALICE,
ALICE::CNAF::TAPE,ALICE::FZK::TAPE,
ALICE::NDGF::DCACHE_TAPE,ALICE::RAL::TAPE,ALI
CE::SARA::DCACHE_TAPE) which are used for RAW
replication. Figure 13 shows the transfer rate for each tapestorage element and the maximum rate is 220.5 MB/s.

6.454 PB
11.89 PB

Number offiles
165,434,307
22,285,969

There are many other functions to provide information tosite administrators to understand the system and tuning theirsystem to get a better performance. Such as there is a
Bandwidth test function in the network traffic, choosing one
site, you could get the related information (i.e. site name,speed, hops, RTT, etc) in traffic in and out views with othersites ranked by speed.
Until now, about 88 sites join the ALICE grid platform andcontributes the resource to the experiment. Around 43 highperformance servers provide central services. We have about
54 disk storage elements and 8 tape storages elements the sizeof which are around 43.67 PB.
V.

Figure 12.

Used size

CONCLUSION

The whole Grid Architecture for ALICE experiment plays
an important role in High Energy Physics. It successfully
manages the resources distributed all over the world and has agreat efficiency system to make all peoples from and indifferent countries working together.
A lot of experiences are got from operating the grid system.
As the increasing number of users and resources, there aremany works on-going:• Developing interface to more middlewares.
• Developing the Java version AliEn2 platform[15].
• Developing Trust Model for reliability and efficiency.
• Optimizing the whole grid platform.
ACKNOWLEDGMENT
I would like to thank the entire members of ALICE
experiments and CERN who do the contribution to thedevelopment and deployment of the Whole system. Thanks to
the ALICE offline group for giving me the chance to join thedevelopment of AliEn2 project and helping me in many areas.
The work is supported by the NSFC ( Key Grant
11020101060,
IRG11221504
and11005044),
the CCNU11Z01001(Key Grant), the Program of Introducing
Talents of Discipline to Universities of China (B08033) .
REFERENCES[1]

Figure 13.

The transfer rates of tape storage elements

The ALICE Collaboration, ALICE: Technical Design Report of theComputing, Printed at CERN June 2005, ISBN 92-9083-247-9.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013[2]

(2011)The ALICE Collaboration website. [Online]. Available:
http://aliweb.cern.ch/[3](2011)The AliEn2 website. [Online]. Available: http://alien2.cern.ch/[4]
S Bagnasco et al, “AliEn: ALICE environment on the GRID”, Journalof Physics: Conference Series, Volume 119,Part 6, 062012, 2008
[5]
P.M. Lorenzo, IT/GS, CERN,”VOBOXes and LCG Services” in AliceT1/T2 Tutorial for Site Admins,CERN,Geneva, 26-27 May 2009.
[6]
Cristina Aiftimier, etc, “Design and implementation of the gLite
CREAM job management service”, Future Generation Computer
Systems, Volume 26, Issue 4, April 2010, Pages 654-667.
[7](2011)The CREAM website. [Online]. Available:
http://grid.pd.infn.it/cream/[8]
Dorigo A, Elmer P, Furano F and Hanushevsky A,“XRootD/TXNetfile: a highly scalable architecture for data access in the
ROOT environment”, Proceedings of the 4th WSEAS International
Conference on Telecommunications and Informatics (World Scientificand Engineering Academy and Society (WSEAS)), Wisconsin, USA,
2005.
[9]
(2011) The Xrootd website. [Online]. Available:
http://xrootd.slac.stanford.edu/[10] J. P. Baud, B. Couturier, C. Curran, J. D. Durand, E. Knezo, S.
Occhetti, O. Barring, “CASTOR status and evolution”, Computing inHigh Energy and Nuclear Physics, 24-28 March 2003, La Jolla,
California.
[11] (2011) The CASTOR website. [Online]. Available:
http://castor.web.cern.ch/[12] J. Zhu, P. Saiz, F. Carminati, L. Betev, D. Zhou, P. M. Lorenzo, A. G.
Grigoras, C. Grigoras, F. Furano, S. Schreiner, O. V. Datskova, S. S.
Banerjee and G. Zhang, Enhancing the AliEn Web Service
Authentication, 2011 J. Phys.: Conf. Ser. 331 062048.
[13] S. Schreiner, S. Bagnasco, S. S. Banerjee, L. Betev, F. Carminati, O.
V. Datskova, F. Furano, A. G. Grigoras, C. Grigoras, P. M. Lorenzo, A.
J. Peters, P. Saiz and J. Zhu, Securing the AliEn File Catalogue –Enforcing authorization with accountable file operations, 2011 J. Phys.:Conf. Ser. 331 062044.
[14] (2011)The MonALISA website. [Online]. Available:
http://alimonitor.cern.ch[15] (2011)The JAliEn website. [Online]. Available: http://jalien.cern.ch/

Dr. Jin Huang, obtained his Ph.D degree in Software
Engineering from Huazhong University of Science andTechnology, China. He obtained his BSc from
Huazhong University of Science and Technology, MSc
from Huazhong University of Science and Technology,
China. He got a University Scholarship for one year
study at CERN from 2009 – 2011. He was a visiting
student of HKUST from September, 2008 to March,
2009.

Mr. Pablo Saiz. He is a fellow of CERN. His researcharea is grid systems. The published papers include“AliEn: ALICE Environment on the Grid”, “AliEn
resources brokers”, “Alienfs-a linux file system for thealien grid services”, “Experiment Dashboard: themonitoring system for the LHC experiments”.

Dr. Latchezar Betev. He is working in the offline
team of the ALICE collaboration at CERN and is
responsible for the operation of the grid infrastructureof the experiment.His main interests include large-scale
distributed computing and monitoring and control ofremote systems. The published papers include

261“Monitoring and control of large systems with MonALISA”, “Monitoring,accounting and automated decision support for the alice experiment based on
the MonALISA framework”.

Dr. Federico Carminati. He obtained the Italian
doctors degree in High Energy Physics at the Universityof Pavia in 1981. After working as an experimental
physicist at CERN, Los Alamos and CalTech, he was
hired at CERN were he has been responsible for thedevelopment and support of the CERN Program
Library and the GEANT3 detector simulation
MonteCarlo. From 1994 to 1998 he has participated in
the design of the Energy Amplifier under the guidance of Prof. C.Rubbia(1984 Nobel Physics Laureate) in the development of innovative MonteCarlotechniques for the simulation of accelerator driven fission machines, and of
the related fuel cycle. In January 1998 he has joined the ALICE collaboration
at LHC assuming the leadership of the ALICE software and computing
project. Since January 2001 he is holding the position of Work Package
Manager in the European DataGRID project. He is responsible for the HighEnergy Physics Application Work Package whose aim is to deploy large scale
distributed HEP applications using the GRID technology.

Dr. Costin Grigoras. He obtained his bachelor andmaster degree in computer science from Politehnica
University of Bucharest, Romania in 2004. He obtained
his PhD degree in computer science from Politehnica
University of Bucharest, Romania in 2009. He is a fellow
at CERN from March, 2006 to present. He is currently
focusing on distributed systems monitoring, automateddecision taking and optimizing databases and code tohandle high volumes of data. He is also interested in parallel processing,multicore-aware applications and new technologies in general.

Mr. Steffen Schreiner. He is a PhD candidate inTechnische Universitaet Darmstadt,Germany. His
research area is security of the distributed system. The
published papers include “Enhancing the AliEn Web
Service Authentication”, “Securing the AliEn File
Catalogue”, “A tool for optimization of the production
and user analysis on the Grid” at CHEP2011, “A
security architecture for the ALICE grid services” at
ISGC2012, “A Mediated Definite Delegation Model
alowing for Certified Grid Job Submission”, etc.

Jianlin Zhu, Yancheng City, Jiangsu Province, China.
November 20, 1981. She obtained her Bachelor degree in
Computer Science from School of Computer Science andTechnology, Huahzong University of Science andTechnology, Wuhan City, China in 2004. She obtained her
Master degree in Computer Architecture from School ofComputer Science and Technology, Huahzong Universityof Science and Technology, Wuhan City, China in 2007.
She is currently a Ph.D student in College of Physical Science andTechnology, Central China Normal University, Wuhan City, China.
Her research work focuses on grid platform, security of distributed
system, program analysis.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

262

Performances Analysis of Algebraic Space Time
Code under Correlated and Uncorrelated Channels
Ines BEN HASSINE *, Ridha BOUALLEGUE ** SUPCOM, InnovCOM Laboratory, National Engineering School of Tunis
Tunis, Tunisia
ines.benhassine@yahoo.fr, ridha.bouallegue@supcom.rnu.tn

Abstract— With their very Algebraic-construction based on
Quaternionic algebra, Algebraic Space Time Codes (ASTC),
called the Golden codes, have a full rate, full diversity and nonvanishing constant minimum determinant for increasing spectral
efficiency. They have also uniform average transmitted energy
per antenna and good shaping, readily lend themselves to highdata rate situations. In this paper, we first analyze theperformances of the ASTC codes in correlated Rayleigh channel.
We consider a coherent demodulator using different decoding
schemes and we analyze the Bit Error Rate (BER). In order toincrease the spectral efficiency and to maximize the coding gain,
ASTC have been proposed for MIMO flat fading channels. To
deal with the frequency selectivity, we use the OFDM
modulation. So we analyze the performances of an ASTCMIMO-OFDM system in terms of BER. Finally, we investigate
the impact of spatial correlation on the ASTC code design interms of BER and capacity.
Keywords— ASTC code, OFDM, MIMO, Rayleigh Channel,
spatial correlation, capacity, Bit Error Rate

I. INTRODUCTION
Orthogonal frequency division multiplexing (OFDM) has
been attracting considerable attention because of its robustness
against frequency-selective fading [1]. OFDM system has been
adopted as a standard for digital audio broadcasting, digital
video broadcasting, and broad-band indoor wireless systems
thanks to his efficiency combat inter-symbol interference (ISI).
In fact, it is considered as an effective method for high-rate
communication systems [2]. On the other hand, information
theory indicates that a multi-input–multi-output (MIMO)
system is able to support enormous capacities [3], provided themultipath scattering of a wireless channel is exploited withappropriate
space–timesignal-processing
techniques.
However, the MIMO system requires a complicated channelequalization technique in a frequency-selective broad-bandchannel, in order to eliminate the ISI. The use of an OFDM
technique for MIMO systems would be desirable to alleviate
this problem. Recent studies have shown that highperformance transmission can be provided by combining the
OFDM technique with a MIMO system [4].
Manuscript received February 13, 2013
I. BEN HASSINE and R. BOUALLEUE are with National Engineering
School of Tunis, Innovation of COMmunicant and COoperative Mobiles
Laboratory, Tunis, Tunisia. (corresponding author, I. BEN HASSINE; e-mail:ines.benhassine@yahoo.fr

By providing a temporal and a spatial multiplexing
modulation, the space-time codes are used to improve MIMO
performances. The Alamouti code [5] and the Golden code [6]represent the most known and used Space-Time Block codes(STBCs). The Golden code, which has been proposed in 2004
for 2*2 MIMO system, is a full-rate and full-diversity spacetime code that has a maximal coding gain. Thanks to its
algebraic construction, it will be shown in this paper that the
ASTC codes outperforms the Alamouti codes in flat fading
channels. In a frequency-selective channel, the ASTC codes
lose their proprieties due the inter-symbol interference (ISI).
The orthogonal frequency division multiplexing (OFDM)
modulation can overcome this problem.
MIMO is used for transmitting multiple data streams orincreasing the reliability (in terms of BER) assuming that the
propagation channels between each pair of transmit and receive
antennas are statistically independent and identically
distributed. Or in practice, the channels between different
antennas are often correlated which is called spatial correlation,interpreted as a correlation between a signal's spatial direction
and the average received signal gain. The spatially correlated
MIMO channels can substantially reduce system performances.
So we propose to study the impact of spatial correlation on the
ASTC code design performances.
In this work, we first propose a coded ASTC system in flat
fading channel. Then, we analyze the ASTC-MIMO-OFDM
system in Rayleigh selective-channel. We use a data aided
channel estimation method based on the pilot symbol insertionin the detector to deduce the channel transfer function. Finally,
we analyze ASTC-MIMO-OFDM system performances under
spatially correlated channel.
This paper is organized as follows. In section II, we present
the main criteria of the proposed coded ASTC chain. The third
section focuses on ASTC-MIMO-OFDM system. In section
IV, we analyze the ASTC-MIMO-OFDM system
performances under spatially correlated channel. In section V,
we present simulation results. Finally, a conclusion is given in
section VI.
II. CODED ASTC SYSTEM
In this section we first propose the system model of a coded
ASTC chain, then the channel model is introduced, and finallythe decoder structure is described.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

A. System ModelAt first, equally probable random numbers (the data to betransmitted) are created in Matlab and mapped onto a 4-QAM
constellation such that the possible symbol values are 1+ j, 1- j,-1+ j, and -1- j. Each information sequence S is encoded by the
ASTC encoder.

263

 (α (a + θb)) (t ,1)  α (c + θ d )) (t +1,1) 
Xt = 
(α (c + θd )) ( t , 2)  (α (a + θ b))

(
t
+
1
,
2
)



(4)

B. Channel ModelIn this work, we suppose that the encoded signal istransmitted over a non selective correlated Rayleigh fading
channel. We consider here the Clarke channel model. The
resulting sequence X will be transmitted over a non selective
channel H. We can express the elementary matrix Ht at time
(t,t+1) as:

Fig. 1. Block diagram of Coded ASTC System

The algebraic construction yields code-words of the Golden
code of the form

θ=

ht21+1
ht22+1
0
0

0
0
ht11
ht12

0 

0 
ht21+1 
ht22+1 

(5)

(1)

We note here that the encoder can transmit 4 symbols oneach antenna at the same time, whereas the Alamouti [5]encoder can only code 2 symbols at a time.

1− 5 , α = 1 + i − iθ , α = 1 + i − iθ ,1+ 5 ,
θ=
2
2

C. Decoder structure
We will consider two structures of decoders: We decode
the received signal using Brute Force ML Decoding andSphere Decoding Algorithm.

C=
where

 ht11 12h
Ht =  t 0
 0

1  α (a + θb) α (c + θd ) .5  α (c + θ d ) α ( a + θ b ) a, b ,c and d are the 4-QAM modulated symbols.
In MIMO systems, the general transmission model is(2)

Y = HX + W

where X is the transmitted codeword, H is the channel matrix
and W is the i. i. d. Gaussian noise matrix.
To have full-rate square codes using QAM constellation, we
consider square (2*2) linear dispersion. We can express thecode word X as the result of multiplication of each four
consecutive symbols of information sequence S by the matrixΦt.

 α αθ

0 0
Φt = 
0 0
 α αθ


0
iα

α
0

0 

iαθ 
αθ 

0 

(3)

So at time (t,t+1), we can express the vector Xt as follow,
where the first two lines are transmitted over antenna 1, andthe rest two ones are transmitted over antenna 2.

1) ML decoding
The best performance is given by the brute force ML
decoder which searches for the matrix X which minimizes theoverall noise power. i.e. an ML decoder computes an estimateof the transmitted matrix as
2Xˆ = arg min Y − HX

(6)

X

But the ML decoder has a very high complexity in MIMO
channels. To lower the complexity, a new type of decoding
method called sphere decoding can be used. The spheredecoding algorithm has near ML performance with reasonably
low complexity [7].
2) The Sphere Decoding Algorithm
The principle of sphere decoding algorithm is to search theclosest constellation point to the received signal with in a
sphere of some initial radius. If a point is found and if the
distance between the centre and the point is less than theradius, the radius is updated to that distance and the process is
continued till only one point is left in the sphere. That will be
the closest constellation point to the received point. If a point

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

is not found initially, then the sphere radius is incremented
and the same process is followed [8].
III. ASTC-MIMO-OFDM SYSTEM
In this section we first describe the system model of ASTCMIMO-OFDM chain, then the channel model is introduced,
and finally the decoder structure is described.

264

zero mean and the same variance σ h and the autocorrelation2function{[]}( m, p ) ( n,q )E hkp ,q (l ) hkm−,kn' (l ' )* = ρ Rx
ρ Tx J 0 (2πf m k ' )δ (l − l ' )where J 0 is the Bessel function with zero order, fm is thenormalized Doppler shift, ρ Rx

( m, p )A. System Model
We consider a coherent system over a frequency-selective
correlated Rayleigh fading MIMO channel with two transmit
and received antennas (Nt=Nr=2). The overall schematic
diagram of ASTC-MIMO-OFDM transceiver is depicted inFig. 2.

, ρ Tx( n,q )

refers respectively

to the correlation coefficient between the received antenna(m,p) and the transmitted antennas (n,q). To obtain acorrelated Rayleigh fading channel, the autocorrelationp ,qfunction of { hk(l ) } process is given by

Rh = σ h2 exp( j 2πf c k ) J 0 (2πf m k )(8)

We can thus express the MIMO-OFDM received signal in amatrix notation as
yk = hkxk + wk

(9)Where xk is kth MIMO-OFDM symbol, wk represents the
AWGN at time k with NrNfft i.i.d. elements and hk is theequivalent channel matrix represented as
Fig. 2. Block diagram of Coded ASTC-MIMO-OFDM system

The same stages applied to information data in the firstsystem model (section II) will be applied to this system.Before
transmission over the two antennas, a conversion to a serial
stream of the ASTC output is done. The Nt streams are thenfed to Nt OFDM modulators, which uses an IFFT module,with Nfft subcarriers and a cycle prefix (CP) of length Nc.
The Nt vectors of length Nfft+Nc are transmitted over afrequency and time selective MIMO channels. In order toavoid ISI, the CP length Nc is assumed to be longer than thelargest multipath delay spread.
B. Channel model

We assume that the ASTC-MIMO-OFDM symbols aretransmitted over a time and frequency selective Rayleighchannel and that the channel taps remain constant during apacket transmission. Consequently, the channel impulse
response (CIR) between qth transmitting antenna and pth
receiving antenna is modeled by a tapped delay line as
L −1

hkp ,q = ∑ hkp ,q (l )δ (k − l )

(7)

l =0

where hkp ,q (l ) is the lth path from the qth transmitting
antenna to pth receiving antenna at time k and L is the largest
order among all impulse responses. The channel taps sequence
p ,q

{ hk

(l ) } is a correlated complex Gaussian process withL0 
 hk (0)
hk = 
MOM  h ( L − 1) L h (0) 
k
 k


(10)

where hk (l ) = [hkp,q (l )] are the Nt*Nr matrices for l=0…L-1.
C. Decoder structure
At the receiver, after removing the CP, the signal istransformed back to the frequency domain by the mean of a
DFT process. We can express the received frequency-domain
signal as
Y= HX+W(11)

where W is the frequency domain noise with zero mean
and variance σw2, X is the frequency domain data matrix and H
is a block diagonal matrix with Nt*Nfft frequency response of
the channel matrix given by0 H (0) L
H = MOM
 0
L H ( Nt * Nfft − 1) (12)Where nth block H(n), n=0…Nt*Nfft-1, represents the
MIMO-OFDM channel gain at the nth subcarrier and can be
written as

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

L

H (n) = ∑ hk (l ) exp(− j 2π
l =0

nk
)
Nfft

(13)

We can express the restored useful data X as

wheredenotes the pseudo-inverse operator.
As mentioned in equation 11 the restitution of signal needs
the knowledge of the channel response which is generally
unknown. In this section, we present a channel estimation
method for OFDM systems using pilot symbols [9]. For
MIMO-OFDM systems, pilots are inserted in both time and afrequency domain as it is shown in Fig. 3. Let us denote XP
the vector of length P whose elements are the pilot symbols.
Based on the LS criterion, channel estimation method atpilot location, is given by

Then channel frequency response estimation at non-pilot
positions can be done by interpolating the channel estimates atneighboring pilot symbol positions. Several efficientinterpolation techniques for OFDM channel estimation havebeen investigated in [9]. In this work, we use the linear
interpolation for its simplicity.

265

can be substantially reduced for spatially correlated MIMO
channels. It has been also shown that more the spatial
correlation increases, more the BER decreases for the same
SNR. So what is the impact of spatial correlation on the ASTC
code design performances?
The capacity analysis of an ASTC-MIMO-OFDM system,
under correlated Rayleigh frequency-selective channel, hasbeen evaluated in [10]. It has been shown that the capacity
increase almost linearly when the normalized Doppler
frequency fm decrease, and the match between the analytic andthe simulated capacity, is more perfect for high Eb/No.
In this part we determine the analytic capacity expressionof ASTC-MIMO-OFDM system under spatially correlatedchannel. To introduce the correlation effect in the systemmodel, a correlated matrix should be generated according to[12]vec( H c ) = C * vec( H ) = R1 / 2 * vec( H )(17)

R = CC T
where Hc represent the correlated matrix of the MIMO
channel. The correlation matrix of the MIMO channel, R, isobtained by the Kronecker product of the transmit and receive
correlation matrices as follow [13]R = RTx ⊗ RRx(18)

Using the properties of the Kronecker product, we have
1/ 2
1/ 2 TH c = RTx
H ( RRx)(19)

We can thus re-express the received frequency-domain
signal in a matrix notation as
Y = HcX + W(20)0 H c (0) L



O
M
Hc =  M 0
L H c ( Nt * Nfft − 1) (21)Fig. 3. Frequency and time domain insertion of pilot symbol

Once the channel effect is compensated, the decisionvariable Xˆ is passed for decoding. Zero Forcing suboptimum decoder is used in this work, to reduce the numericalcomplexity without significant performance loss.
A serial to parallel module, at each DFT output, is used toreshape the signal X̂ . Then, we provide the sequences
and finally restitute the information sequence Ŝ given by

X̂ t

L

1/ 2
1/ 2
H c (n) = ∑ RTx,l hk (l ) RRx ;l exp(− j 2π

l =0

nk
) (22)
Nfft

First of all we should determine the mutual information(16) between transmitted code word X and received vector Y. Then
we can deduce the analytic expression of the information
IV. PERFORMANCE OF ASTC-MIMO-OFDM SYSTEM
capacity of ASTC under spatially correlated MIMO channel
UNDER SPATIALLY CORRELATED MIMO CHANNELS
for a given channel matrix Hc. The mutual information isgiven
by
In practice, the MIMO channels between different antennas
are often correlated which is called spatial correlation. As aresult shown by information theory [11], the channel capacity−1Sˆ = Φ t Xˆ t

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

I ( X , Y ) = Η (Y ) − Η (Y X ) = Η (Y ) − Η (W ) (23)
Where Η (Y ) denotes the entropies of multivariate
distribution Y. The capacity is obtained by maximizing themutual information

C = max(I ( X , Y )) = max(Η (Y )) − Η (W )(24)

So we have to determine the exact expression of Η (Y ) toobtain the capacity expression. It has been shown in [10] thatthe expression of the capacity can be expressed as

C = log 2 det( I Nfft*Nt +

1

σ

2
W

H

Where ρL2

1/ 2 1/ 2= ∑ RTx,l RRx ;l266

2

and Ph denotes the power tol =0

each channel coefficient in time domain.
At this stage, the last capacity expression can be bounded
by


1
2
C = log 2 det( I Nfft*Nt 1 + 2 H c  )
 σw


1
2
= log 2 ( 1 + 2 Nfft.Nt. H c  σwNfft * Nt)(30)

QX H c H c ) bits/ s / Hz (25)
1≤ log 2 ( 1 + 2 Nfft.Nt.ρ 2 .L.Ph2  σw
The covariance matrix Q X can be expressed as [10]{}Q X = ΦΦ H I Nfft*Nt(26)

where Φ is a copy of Φ t (Nfft*Nt/4) times.
Then the capacity expression will be [10]C = log 2 det( I Nfft*Nt +

1

σ

2
w

H

H

(27)



1
≤ Nfft.Nt. log 2 ( 1 + 2 Nfft.Nt.ρ 2 .L.Ph2  )
 σw




1
C ≤ log 2 ( 1 + 2 Nfft.Nt.ρ 2 .L.Ph2  ) bits/ s / Hz (31) σw
we get I Nfft * Nt .

Exploiting the fact that H is a block diagonal matrix, we
can write H c (0) 2 LH
HcHc = 
M
O


0
L


)

By averaging over the capacity of Nfft*Nt narrowband
channels we derive the following capacityΦΦ H H c H c )If we derive the exact expression of ΦΦ

Nfft * Nt

V. SIMULATION RESULTS
To investigate the performance of the proposed space timecode, a series of Monte Carlo simulations were carried out.
We first present coded ASTC system performances overcorrelated Rayleigh fading channel. Then we present theperformance of the ASTC MIMO-OFDM system.

 (28)M
2
A. BER performance for coded ASTC system
H c ( Nt * Nfft − 1) Fig. 4 presents a comparison of Brute Force ML and Sphere22decoding techniques. For Brute Force ML decoding technique,= H c = Nfft * Nt H c (n) ∀0 ≤ n ≤ Nfft * Nt − 1-10

the BER is close to 10 for about 10 dB, however, in case of
the Sphere decoding technique at 8 dB, the BER is about 10-1
so the gain is around 2dB for the Sphere decoding technique.
The ML decoder suffers from a high complexity. These
drawbacks can be addressed by the Sphere decoder technique,which helps reducing the BER.

WhereasL

H c (n) = ∑ R h (l ) R
2

l =0

1/ 2
Tx ,l k

1/ 2
Rx ;l

L

≤ ∑ R h (l ) R
l =0
L

1/ 2
Tx ,l k

1/ 2
Rx ;l

nk
exp(− j 2π
)
Nfft
nk
exp(− j 2π)
Nfft

≤ ∑ ρ 2 hk (l ) = ρ 2 .L.Ph2
2

2

2

(29)

l =0

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

0

267

0

10

10

Alamouti-MIMO-OFDM system
ASTC-MIMO-OFDM system
-1

10
-1

10

-2

Bit Error rate

Bit Error rate

10

-2

10

-3

10

-4

10

10

-5

sim (nTx=2, nRx=2, Golden Code)using ML
sim (nTx=2, nRx=2, Golden Code) using Sphere Decoder

-3

0

2

4

6

8

10

12

10

14

16
-6

10

SNR(dB)

0

2

4

6

8

Fig. 5 compares the BER performances of the Golden codeand the classical Alamouti code. We can see that for a high
SNR, the Golden code have a good performance in terms of
BER. In the case of 2×1 transmission, the Golden BER
remains close to Alamouti BER; for BER equal to 10-1 thegain is around 1dB for Golden code. By comparing this caseto the 2×2 transmission case, the Golden code provides a gainof about 4dB for a BER equal to 10-1. This gain comes fromthe fact that we are coding 4 symbols at the same time with
Golden code, however we code only 2 symbols with Alamouti,
then the gain still significant in terms of rate.
These results lead us to deal with real channel conditions
mainly if we use a selective Rayleigh channel with unknown
channel coefficients.
0

10

10

12

14

16

18

20

SNR(dB)

Fig. 4. Comparison of Brute Force ML and Sphere decoding techniquesFig. 6. ASTC-MIMO-OFDM versus Alamouti-MIMO-OFDM

Fig. 6 shows that the gain obtained by ASTC code is about
2 dB for a BER of 10-3. The same raison for this gain: it comesfrom the fact that we are coding 4 symbols at the same timewith ASTC code, however we code only 2 symbols with
Alamouti.
C. Performance for ASTC-MIMO-OFDM system under
spatially correlated MIMO channels
In this sub-section, we illustrate the results of the effect of
spatial correlation of MIMO channel on the performances of
ASTC-MIMO-OFDM architecture in terms of BER andcapacity.
The BER Performances of ASTC code with respect todifferent spatial correlation is depicted in Fig. 7.
0

10

-1

10
-1

Bit Error rate10

-2

Bit Error rate

10

-2

10

-3

10

-4

10
sim (nTx=2, nRx=1, Alamouti Code)sim (nTx=2, nRx=1, Golden Code)sim (nTx=2, nRx=2, Golden Code)

-3

10

0

2

4

6

8

SISO QAM4ASTC with correlation Rtx=Rrx=0.99
ASTC with correlation Rtx=0.99,Rrx=0.01
ASTC with correlation Rtx=Rrx=0.01
ASTC without correlation

-5

10
10

12

14

16

SNR(dB

-6

10

0

2

4

6

8

10

12

14

16

18

20

Fig. 5. Golden code versus Alamouti code

SNR(dB)

B. BER performance for ASTC-MIMO-OFDM system
In this sub-section we present a comparison of the BER
performances between the ASTC code and the classicalAlamouti code combined with MIMO-OFDM system.

Fig. 7. BER Performances of ASTC code with respect to different spatial
correlation

We can see that the correlation between sub channels
degrade considerably the performances of ASTC-MIMOOFDM system. In fact, for a fixed SNR of 10 dB for example,
the BER of (RTx=RRx=0.99) is much important than the BER
of (RTx=RRx=0.01). For a fixed value of correlation, the BER
decrease when the value of the signal to noise ratio is

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

increased. We also notice that for (RTx=RRx=0.99), the BER is
almost similar to uncoded SISO QAM-4 system. This can be
explained by the fact that with strong correlation, the use of
ASTC codes become less efficient and hence the importanceof the spatial correlation on the hole performances.
The ergodic capacity as a function of SNR per receive
antenna, for both the analytic capacity expression, derived in(30) and the simulated expression using (24), is shown in Fig.
8.
It can be observed that the strong correlation decrease
considerably the ergodic capacity of the ASTC-MIMOOFDM system compared to uncorrelated system. It can beobserved also that the match between the analytic and thesimulated capacity without or with low correlation, is more
perfect for high SNR, mainly when SNR ≥ 16 dB.

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]
45
Analytic Capacity without correlation
Simulated Capacity with correlation Rtx=Rrx=0.99
Simulated Capacity with correlation Rtx=Rrx=0.01
Simulated Capacity without correlation

Ergodic Capacity (bits/s/Hz)

40
35

[10]

[11]

30

[12]

25
20

[13]

15

268

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10
5

2

4

6

8

10

12

14

16

18

20

SNR(dB)

Fig. 8. Ergodic capacity of ASTC-MIMO-OFDM systen over spaciallycorrelated and uncorrelated channels

VI. CONCLUSIONSIn this paper, we have proposed a MIMO transmission
system, based on Algebraic space time coding which has good
properties. Numerical results show that this code has areasonable BER that outperforms the classical Alamouti codeover correlated Rayleigh fading channel. In realistic multipath
channel, frequency selectivity can be solved by the use of
OFDM modulation. Numerical results show that ASTC codes
maintain their properties and achieve good BER performances
compared to the classical Alamouti MIMO-OFDM system.
Finally, we have demonstrated that spatial correlation has an
impact on ASTC code design: the strong spatial correlation
decrease considerably the BER and capacity performances of
the ASTC-MIMO-OFDM system.
REFERENCES[1]

S. Hara and R. Prasad, Multicarrier Techniques for 4G Mobile
Communications, Artech House, June 2003.

Ines BEN HASSINE received the engineering degree
and the M.Sc respectively in Telecommunications in2008 and in Communications System in 2009 fromthe National Engineering School of Tunis (ENIT),
Tunisia. From 2010 to 2013, she was a Research
Associate with the Laboratory of InnovCOM(Innovation of COMunicant and COperative
Mobiles), High School of Communication of Tunis,
Tunisia. She is currently an Assistant at the High
Institute of Computer Science Mahdia. His research interests include MIMO,OFDM systems, Space Time Code, UWB.

Ridha Bouallegue received the M.S degree in
Telecommunications in 1990, the Ph.D. degree in
Telecommunications in 1994, and the Habilitation a
Diriger des Recherches (HDR) degree in
Telecommunications in 2003, all from the National
Engineer School of Tunis (ENIT), Tunisia. He iscurrently Professor in the National Engineer Schoolof Tunis (ENIT) and Director of Research Laboratory
Innov’COM / Sup’Com. His current research interests
include mobile and satellite communications, Accesstechnique, intelligent signal processing, CDMA, MIMO, OFDM and UWB
system.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

269

Distributed Mobility Control Schemesin the HIP-based Mobile Networks
Sang-Il Choi, Seok-Joo Koh
School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea
overcycos@gmail.com, sjkoh@knu.ac.kr
Keyword—HIP, Rendezvous Server, Distributed management

are processed by a central RVS. However, the centralized
scheme is vulnerable to several problems [4]. First, a single
point of failure of central RVS may affect severe degradation of
overall system performance and also the increased cost of
network engineering. In addition, the centralized RVS has therisk of overhead by increasing of host that is stored in the RVS’s
HIT-IPv4 mapping table.
In this paper, we proposed the two distributed mobility
management schemes in HIP-based mobile networks. The
proposed schemes can be used to effectively provide themobility support in HIP-based wireless/mobile HIP networks,compared to the existing centralized control schemes.
This paper is organized as follows. Section II describes theexisting centralized schemes for HIP mobility control. In
Section III, we propose the two distributed mobility control
schemes. Section IV compares the existing and proposed
schemes in terms of the HIP connection setup delays, and theprocessing overhead at the central RVS server. Section V
concludes this paper.

I. INTRODUCTION

II. EXISTING HIP SCHEME
In the existing scheme for HIP mobility control, all HIT-IPv4
mapping entries are stored at centralized RVS. Figure 1 shows
the operation of existing HIP data transmission.

I1
2.
-I,
IT
,H

IP

IP ,

RV
S

IP ,

]
AC
M
-H
VS
,R
: I IP
m
ro
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-R
IT
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IT
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,R

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IT
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]

VS
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RVS

[I

W

ITH emergence of new types of wireless/mobile
networks and wide popularity of smart phones, thenumber of mobile Internet users has been rapidly
increasing. This mobile trend has caused a rapid growth of BGP
routing table, as known as the routing scalability problem. [1].
To solve this problem the Host Identity Protocol (HIP) has
been proposed in IETF [2], which splits the current IP address
space into Host Identity (HI) and routing locator (IP address).
An HI is encoded to Host Identity Tag (HIT) and both HI andHIT are not changed. If an end host wants to communication toanother, it finds out other host’s IPv4 address using the HIT ofthe other host. In HIP, the centralized Rendezvous Server(RVS) has the responsibility of this search process [3]. In thecentralized scheme, all binding and first Initiate message (I1)1.
I1

Abstract— The Host Identity Protocol (HIP) has been proposed
as an identifier-locator (ID-LOC) separation scheme, in which the
128-bit Host Identity Tag (HIT) is used as an ID and the IP
address of the host is used as a LOC. In HIP, the mobility control
operations are performed based on a centralized Rendezvous
Server (RVS) that acts as a mobility anchor for mobile nodes, inwhich all the HIP control messages are passed through the RVS
server. However, this centralized mobility scheme has some
limitation, such as the service degradation by a point of failure andthe overhead of centralized anchor. In this paper, we propose thetwo schemes for distributed mobility management (DMM):
HIP-DMM-Push and HIP-DMM-Pull. From the numericalanalysis, it is shown that the proposed DMM schemes can provide
better performance than the existing centralized scheme, and thatthe pull-based distributed control scheme (HIP-DMM-Pull)provides the best performance among the candidate mobility
schemes in terms of the processing overhead at the central RVS
server and the HIP connection setup delays.

3. R1 [RIP, IIP, HIT-R, HIT-I, VIA:RVS]

Manuscript received February 26, 2013. This research was supported by
Basic Science Research Program of NRF(2011-0026529) and ITRC support
program of NIPA(NIPA-H0301-12-2004).
Sang Il Choi is with the School of Computer Science and Engineering,
Kyungpook National University, Sangyeok 3-dong, Buk-gu, Daegu 702-701,
Korea. e-mail: (overcycos@gmail.com).
Seok Joo Koh is with the School of Computer Science and Engineering,
Kyungpook National University, Sangyeok 3-dong, Buk-gu, Daegu 702-701,
Korea. e-mail: (sjkoh@knu.ac.kr).

4. I2 [IIP, RIP, HIT-I, HIT-R]
Initiator

5. R2 [RIP, IIP, HIT-R, HIT-I]

Responder

Fig. 1. Existing HIP data transmission
If Initiator wants to communicate with Responder, it sends
the I1 message that contains the IP address of Initiator, IP
address of RVS, HIT of Initiator, and HIT of Responder tocentralized RVS, as described in Step 1. Next, RVS will search
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

the HIT-IPv4 mapping table. Then, RVS will forward I1
message that adds the RVS-HMAC for authentication to
Responder (Step 2). On reception of I1 message, Responder
responds with a R1 message that contains the IP address of
Responder, IP address of Initiator, HIT of Initiator, and
VIA:RVS for authentication (Step 3). After that, Initiator andResponder exchange the I2 message and R2 message (Step 4, 5).
Now, communication between Initiator and Responder ispossible, because Initiator knows the IP address of Responder.
In this paper, we will focus on only the inter-domain mobility
control within a HIP-based mobile network, rather than theintra-domain mobility control.
Figure 2 describe the existing HIP data transmission schemesin the inter-domain case. First, Responder sends the Binding
Update message to RVS for adding the HIT-IPv4 entry of
Responder. Then, RVS responds with Binding ACK message to
Responder. In this stage, if Initiator wants to communicate with
Responder, it sends the I1 message to centralized RVS throughAccess Router (AR) and Gateway (GW) of Initiator. Then,centralized RVS will search the HIT-IPv4 table and forward to
IP address of Responder through GW and AR of Responder. On
reception of I1 message, Responder responds with R2 message
to Initiator directly. After that, Initiator and Responder
exchange the I2 message and R2 message through each AR and
GW of Initiator and Responder.

AR

AR

GW

270

that contains the HIT and IP address of Initiator to the other
D-RVS by multicast. On reception of this Binding Update
message, each D-RVS updates their HIT-IPv4 table and thenresponds with Binding ACK message. This is called the ‘push’
operation, which is similar to the legacy routing protocol (e. g.,
OSPF) mechanism.
In HIP-DMM-Pull, on the other hand, each Binding Update
message of Responder is completed by the D-RVS of Initiator,and only the D-RVS of Responder updates the binding entry.
After that, if Initiator wants to communicate with Responder,
the GW of Initiator sends the I1 message that is the first message
of mapping query to the other GW through multicast for
HIT-IPv4 mapping entry. Then, only the GW of Responder
responds with R1 message that contains the IP address of
Responder. From this R1 message, the D-RVS of Initiator can
update the Responder’s information in the HIT-IPv4 table.
The existing HIP scheme is a centralized scheme, in which all
Binding Update and Mapping Query messages are processed bycentralized RVS.
The proposed HIP-DMM-Push scheme is a distributed
scheme, in which each GW performs the RVS functionality. In
this scheme, the Binding Update with RVS is not performed.
Instead, each D-RVS will send its Binding Update message toother D-RVS by multicast, when a new Responder is attached to
the network. From this process, this scheme does not need toquery for HIT-IPv4 mapping to other D-RVS, because the
D-RVS already knows the associated information.
The proposed HIP-DMM-Pull scheme is also a distributed
scheme with GW acting as RVS. The Binding Update isprocessed at D-RVS of the host. Then, each D-RVS sends an I1
message to other D-RVS to find the IP address of host.
B. HIP-Push
Figure 3 show the HIP-DMM-Push operations.

I1

I1

RVS

R2

AR

I1

I1 R1
I2

I1

R1
I2
R2

GW
AR

BindingAck

Binding
Update

AR

GW
I1

R1 R2
I2

AR
GW/D-RVS

BindingAck

Binding
UpdateARAR

R2
I1 R1 I2

I1 R1 I2 R2BindingAck
Binding
UpdateResponder
I1

Initiator

AR

GW/D-RVS
I1 R1 I2
R2

I1

Fig. 2. Existing HIP schemes

I1
R1
I2
R2

GW/D-RVS
I1 R1

R2
I2

AR

III. PROPOSED DMM SCHEMES
A. Overview
In the proposed schemes, each GW has the Distributed
Rendezvous Server (D-RVS) functionality, and stores theinformation on HIT and IPv4 for hosts.
Each of the proposed schemes updates the HIT-IPv4 table inthe different way. In HIP-DMM-Push, if initiator is attached to adomain, the GW of Initiator sends the Binding Update message

AR

AR

I1 R1 I2 R2

I1 R1 I2 R2Initiator

Responder

Fig. 3. HIP-DMM-Push operations

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

In the figure, when Responder is attached to a new domain, its
HIT will be bound to its AR and GW/D-RVS. Then, GW of
Responder will send (or push) the Binding Update message that
contain the HIT and IP address of Responder to other
GW/D-RVS by multicast. Every GW/D-RVS will update its
HIT-IPv4 address, based on the Binding Update received fromthe GW/D-RVS of Responder. When Initiator wants tocommunicate with Responder, Initiator sends the I1 message to
GW/D-RVS of Initiator. Then GW/D-RVS looks up the
HIT-IPv4 table to find the IP address of Responder. If the IP
address is found, GW/D-RVS forwards the I1 message to
GW/D-RVS of Responder. Then, I1 message is forwarded to
Responder. On reception of I1 message, Responder sends the
R1 message to Initiator through GW/D-RVS of Responder and
GW/D-RVS of Initiator. Next, Initiator sends the I2 message to
Responder and receives the R2 message from Responder in thesame way. Now, Initiator can send the data message to
Responder directly.

IV. PERFORMANCE ANALYSIS
To evaluate the performance of the proposed mobility
schemes, we analyze the delay of the total transmission of firstdata and the overhead of GW/D-RVS. We compare the totaltransmission delay cost and the number of HIT-IPv4 table entryof GW/D-RVS for the existing scheme (HIP) and the proposed
schemes (HIP-DMM-Push, HIP-DMM-Pull).
A. Analysis Model
Initiator and Responder are located within the different
domain (i.e., Initiator is a mobile host and Responder is a statichost), as illustrated below in the Figure 5.

RVS

C. HIP-Pull
Figure 4 shows the HIP-DMM-Pull operations.

AR

271

AR

GW(D-RVS)

GW(D-RVS)

AR

AR

GW(D-RVS)

GW(D-RVS)

AR

AR

GW/D-RVS
Initiator

I1

I1

AR

R2

I1

I1
R1
I2
R2

GW/D-RVS
I1 R1 I2

Responder

Fig. 5. Network model for numerical analysis

GW/D-RVS
I1 R1 I2 R2ARBinding
UpdateARAR

I1 R1 I2 R2

I1 R1 I2 R2Initiator

Responder

BindingAck

The binding update delay and the data delivery delay are
denoted by BUD and DDD, respectively. Then the total delay(TD) is represented as TD=BUD+DDD. Then, for theprocessed signal at each D-RVS, we calculate the Signal
Overhead (SO) that is sum of processed signal when Binding
Overhead (BO) and Query Overhead (QO) process isperformed.
B. Delay and Overhead Analysis
We define the parameters used for the analysis in Table 1.
TABLE IPARAMETERS USED FOR DELAY AND OVERHEAD ANALYSIS

Fig. 4. HIP-DMM-Pull operations

Parameter

In the figure, when Responder enters a domain, it isconnected to AR and the Binding Update is processed at the
GW/D-RVS of Responder. Now, the Initiator sends the I1
message to the GW/D-RVS of Initiator. Then, GW/D-RVS
forwards the I1 message to other GW/D-RVS to find the IP
address of Responder. Then, only the GW/D-RVS of Responder
will forward the I1 message to Responder, the other
GW/D-RVS will discard this message. On reception of I1
message, Responder sends the R1 message to Initiator throughGW/D-RVS of Responder and GW/D-RVS of Initiator. Next,
Initiator sends the I2 message to Responder and receives the R2
message from Responder in the same way. Now, Initiator cansend the data message to Responder directly.

Description

TsetupNode setup and HIT encoding delay

Sb

Size of a ‘b’ packet

NGW

Number of GW in the Network

Ha-b

Hop count between node a and b in the networkγ (HGW-GW/HGW-RVS)

Ratio of HGW-GW over HGW-RVS

Pc

Processing cost of node c for binding update or
lookupCopyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 20132721) HIP

2) HIP-DMM-Push

If the Responder enters a network, the HIT encoding process
begins. We assume that this operation takes roughly Tsetup. Then,
Responder sends the Binding Update message to centralized
RVS through GW and AR of Responder and receives the
BindingAckmessage.
This
operation
takes
2Scontrol{HAR-GW+HGW-RVS+(Hhost-AR/2)}. On reception of
Binding Update message, the centralized RVS updates their
HIT-IPv4 table. This operation takes PRVS. We assumed that theprocessing cost of RVS is proportional to the total number ofactive hosts in the domain (NARNHost/AR) in the log scale byusing a tree-based data structure to implement the database.
Accordingly, the binding update delay of HIP can be
represented as follows.

If the Responder enters a network, HIT encoding process
begins. We assume that this operation takes roughly Tsetup. Then,
Responder sends the Binding Update message to D-RVS of
Responder and D-RVS will forward the Binding Update
message to other D-RVS by multicast. This operation takes
2Scontrol{HAR-GW+HGW-GW+(Hhost-AR/2)}. On reception of
Binding Update message, each D-RVS updates their HIT-IPv4
table. This operation takes log2(Nhost), since each D-RVS has all
host’s mapping information from fowarded Binding Updated
message. Accordingly, the binding update delay of
HIP-DMM-Push can be represented as follows.

BUDHIP = Tsetup + 2Scontrol  {HAR-GW + HGW-RVS+ (Hhost-AR / 2)} + log2(Nhost/NRVS)(1)

In HIP, the data delivery delay for Initiator to Responder canbe calculated as follows. First, Initiator sends the I1 message tocentralized RVS through AR and GW of Initiator. Then,centralized RVS will look for the IP address of HIT of
Responder in the HIT-IPv4 table, which takes log2(Nhost/NRVS).
After that, RVS will forward the I1 message to Responder
through AR and GW of Responder. This operation takes
2SI1(Hhost-AR+HAR-GW+HGW-RVS). After that, Initiator andResponder exchange R1, I2, and R2 message through AR and
GW of Initiator and Responder. Then, the first data packet will
be forwared from Initiator to Responder in the same way. This
operation takes (SR1+SI2+SR2+Sdata) (Hhost-AR+HAR-GW+HGW-GW+HGW-AR+HAR-host). Thus, the data delivery delay of
HIP can be represented as follows.
DDDHIP = 2SI1(Hhost-AR + HAR-GW + HGW-RVS) + (SR1
+ SI2 + SR2 + Sdata)  (2Hhost-AR + 2HAR-GW+ HGW-GW) + log2(Nhost/NRVS)(2)
So, we obtain the total delay of HIP as
TDHIP = BUDHIP + DDDHIP
Now, to analyze the overhead of RVS, we will calculate thenumber of signals processed by RVS. For simplicity, we assume
that the hosts are equally distributed in each domain. Whenhosts enter the network, they send a Binding Update message tothe centralized RVS to update the HIT-IP address mapping
entry. From this operation, the number of processed signal of
RVS is NHost. Then, if NHost want to communicate with otherhosts, they sends an I1 message to the other host through RVS.
From this operation, the RVS shall process NHost. From BO and
QO, we can calculate the SO of HIP.
SOHIP = BOHIP + QOHIP = 2NHost

BUDHIP-DMM-Push = Tsetup + 2Scontrol  {HAR-GW + (Hhost-AR/2)+ HGW-GW} + log2(Nhost)(3)
In the HIP-DMM-Push, the data delivery delay for Initiator
to Responder can be calculated as follows. First, Initiator sends
the I1 message to D-RVS of Initiator through AR and GW of
Initiator. Then, D-RVS will look for the IP address of HIT of
Responder in their HIT-IPv4 table, which takes log2(Nhost).
After that, D-RVS of Initiator will forward the I1 message to
Responder through GW/D-RVS and AR of Responder. On
reception of I1 message, Responder sends the R1 message to
Initiator directly. Then Initiator and Responder exchange the I2
and R2 message in the same way. This operation takes (SI1 + SR1+ SI2 + SR2 + Sdata)  (2Hhost-AR + 2HAR-GW + HGW-GW). Thus, thedata delivery delay of HIP-DMM-Push can be represented asfollows.
DDDHIP-DMM-Push = (SI1 + SR1 + SI2 + SR2 + Sdata) (2Hhost-AR + 2HAR-GW + HGW-GW) + log2(Nhost)(4)

So we obtain the total delay of HIP-DMM-Push as
TDHIP-DMM-Push = BUDHIP-DMM-Push + DDDHIP-DMM-Push
Now, we will calculate the number of signals processed by
one D-RVS. When hosts enter the network. they send a Binding
Update message to D-RVS that located at same domain to
update the HIT-IP address mapping entry. In this case, thenumber of processed signal of one D-RVS is NHost/NGW. Then,each D-RVS forward the I1 message of host to other D-RVS bymulticast. In this case, the number of processed signal of one
D-RVS is NHost-NHost/NGW. From these operations, BO of
HIP-DMM-Push is NHost. After that, if NHost want tocommunicate with other hosts, they sends a I1 message to
D-RVS of same domain. Since each D-RVS knows the locationof other host, the I1 message is passed directly. From thisoperation, QO of HIP-DMM-Push is NHost/NGW. From BO and
QO, we can calculate the SO of HIP-DMM-Push.
SoHIP-DMM-Push = BOHIP-DMM-Push + QOHIP-DMM-Push= NHost+(NHost/ND-RVS)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

3) HIP-DMM-Pull
The Binding Update operations are performed as follows.
When responder enters a network, HIT encoding process starts.
We assume that this operation takes roughly T setup. Then
Responder sends the Binding Update message to D-RVS of
Responder. This operation takes 2Scontrol  {HAR-GW +(Hhost-AR/2)}. At that time, the D-RVS of Responder updates
their HIT-IPv4 mapping table. This operation takeslog2(Nhost/ND-RVS), since each D-RVS stored only HIT-IPv4
mapping information of host that belongs to D-RVS’s domain.
Accordingly, the binding update delay of HIP-DMM-Pull canbe represented as follows.
BUDHIP-DMM-Pull = Tsetup + 2Scontrol  {HAR-GW + (Hhost-AR/2)}+ log2(Nhost/ND-RVS)(5)
In the HIP-DMM-Pull, the data delivery delay for Initiator to
Responder can be calculated as follows. First, Initiator sends theI1 message to D-RVS of Initiator through AR and GW of
Initiator. Then, D-RVS of Initiator forward the I1 message toother D-RVS by multicast. This operation takes SI1(Hhost-AR +HAR-GW). After that, each D-RVS look for the IP address of HITof Responder in their HIT-IPv4 table, which takeslog2(Nhost/ND-RVS). If D-RVS find out that entry, then it will
forward the I1 message to Responder through AR of Responder,and other D-RVSs discard the I1 message. This operation takes
SI1(Hhost-AR + HAR-GW + HGW-GW). On reception of I1 message,
Responder sends the R1 message to Initiator directly. Then,
Initiator and Responder exchange the I2, R1, and I2 message,and next, Initiator sends the data packet to Responder directly.
This operation takes (SR1 + SI2 + SR2 + Sdata)  (2Hhost-AR +2HAR-GW + HGW-GW). Thus, the data delivery delay of
HIP-DMM-Pull can be reprented as follows.
DDDHIP-DMM-Pull = SI1(2Hhost-AR + 2HAR-GW + HGW-GW)+ (SR1 + SI2 + SR2 + Sdata)  (2Hhost-AR + 2HAR-GW+ HGW-GW) + log2(Nhost/ND-RVS)
(6)

273

C. Numerical Results
Based on the cost analysis given in the previous section, we
now compare the numerical results. For numerical analysis, we
set the parameter values, as shown in Table 2, which are partly
obtained from the results given in [5].
TABLE IIPARAMETER VALUES USED FOR DELAY AND OVERHEAD ANALYSIS
Parameter

Default

Minimum

Tsetup100

SI1, SR1, SI2, SR2

1

Scontrol, Sdata

1

Maximum

ND-RVS

4

1

256

Nhost

400

100

8000

Hhost-AR

1

HAR-GW

1

HGW-GW

3

HGW-RVS

10

γ

0.3

0.1

1.5

Figure 6 shows the impact of hop count ratio GW-GW over
GW-RVS on total transmission delay. From the figure, we cansee that the distributed mobility control schemes have lower
total transmission delay until the ratio is equal to 0.8 and the
HIP-DMM-Pull scheme shows the lower delay than
HIP-DMM-Push. When the ratio is higher than 1.2, delay of
distributed mobility control schemes are higher than existing
HIP scheme. However, it is not a critical weakness. In general
network, average distance between centralized server andgateway is farther than the distance between two gateways. So
we are focus on the value that is lower than 1. In this generalsituation, we are sure that two distributed mobility control
schemes can show the better performance than existing HIP
scheme.

So, we obtain
TDHIP-DMM-Pull = BUDHIP-DMM-Pull + DDDHIP-DMM-Pull
Now, we will calculate the number of signal processed by one
D-RVS. When hosts enter the network, they send a Binding
Update message to D-RVS that located at same domain to
update the HIT-IP address mapping entry. From this operation,
the BO is NHost/ND-RVS. Then, if NHost want to communicate withother hosts, they sends a I1 message to D-RVS of same domain.
In this case, processed signal of each D-RVS is NHost/ND-RVS.
After each D-RVS receive the I1 message, they forward thismessage to other D-RVS by multicast. This process is takes
NHost-NHost/ND-RVS. From this operation, the QO is NHost. So we
can calculate the SO of HIP-DMM-Pull.
SOHIP-DMM-Pull = BOHIP-DMM-Pull + QOHIP-DMM-Pull= (NHost/ND-RVS)+NHost

Fig. 6. Impcat of hop count ratio on total delay

Figure 7 and 8 show the impact of number of host and D-RVS
on total transmission delay. From this figure, we can see that thetwo distributed mobility schemes show lower total transmission
delay than existing HIP scheme. In HIP-DMM-Push, althougheach D-RVS has the same entry with RVS of existing HIP, there
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013274

is no query operation to get the Responder’s IP address. So, thisscheme can perform with low delay. In HIP-DMM-Pull, the I1
message’s route is shorter than that of existing HIP. In addition,
this scheme’s HIT-IPv4 table has less entry than existing HIP
scheme and HIP-DMM-Push. From these reasons, we can seethat the two distributed mobility control schemes shows better
performance than existing scheme and the HIP-DMM-Pull has
the best performance.

Fig. 9. Impact of number of host on search time of RVS

Fig. 7. Impact of number of host on total delay

Fig. 10. Impact of number of D-RVS on performanceFig. 8. Impact of number of D-RVS on total delay

Figure 9 and 10 show the impact of number of host and
D-RVS. In HIP-DMM-Push, HIT-IPv4 table update of D-RVS
is completed at binding update operation. So, the entry numberof two schemes (HIP and HIP-DMM-Push) are the same. From
this reason, the existing scheme and HIP-DMM-Push show thesame performance. However, in HIP-DMM-Pull, each D-RVS
has the MIP-IPv4 mapping entry of hos that is located in domain
of GW. Accordingly, HIP-DMM-Pull has the best performance.
Figure 11 and 12 show the impact of number of host and
D-RVS on processed signal in each D-RVS. From this figure,
we can see that the proposed schemes shows better performance.
In HIP, all Binding Update message and I1 message isprocessed at centralized RVS. However, in HIP-DMM-Push,
only all Binding Update message is processed at D-RVS andeach I1 message is processed at each D-RVS that located insame domain. In HIP-DMM-Pull, only all I1 message isprocessed at D-RVS and each Binding Update message isprocessed at each D-RVS that located in same domain. So, thetwo proposed schemes shows lower number of processed signal
than existing HIP scheme.

Fig. 11. Comparison of processed signal at each D-RVS

Fig. 12. Impact on the processed signal at each D-RVS

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

Figure 13 shows the impact of number of D-RVS in terms ofgenerated signals in the network. In HIP, each host sends a
Binding Update message and the I1 message to the centralized
RVS. So, there is no affected by D-RVS. However, in
HIP-DMM-Push, if host enters the network, it sends a Binding
Update message to D-RVS that located in the same domain. On
reception of Binding Update message, D-RVS forwards themessage to other D-RVS and receives Binding ACK message
from each D-RVS. From this operation, in Binding Update
operation, the signals are generated by twice number of D-RVS.
In HIP-DMM-Pull, if host want to communicate with
Responder, it sends the I1 message to D-RVS that located in thesame domain. Then, D-RVS forwards this message to other
D-RVS. On reception of I1 message, D-RVS that is located insame domain with Responder responds with R1 message. From
this operation, in Binding Query operation, the signals are
generated by the number of D-RVS. Because of this, existing
HIP scheme shows best performance.

[4]

[5]

275

H. Chan, Problem statement for distributed and dynamic mobility
management,
IETF
Internet
Draft,
draft-chan-distributed-mobility-ps-05.txt, October 2011.
Y. H. Choi and T. M. Chung, “Using Correspondent Information for
Routing Optimization Scheme on Proxy Mobile IPv6,” Journal of
Networks, Vol. 5, No. 8, pp. 984-989, August 2010.

Sang Il Choi received B.S and M.S. degrees inEngineering from Kyungpook National University in2010 and 2012, respectively. Since March 2012, heenters to the Ph.D. program. His current researchinterests include mobility control and Future Internet
such as Locator Identifier Separation Protocol (LISP)
and Host Identity Protocol (HIP). Now, he studies the
distributed mobility management in Future Internet.

Seok Joo Koh received B.S and M.S. degrees inManagement Science from KAIST in 1992 and 1994,
respectively. He also received Ph.D. degree in Industrial
Engineering from KAIST in 1998. From August 1998 to
February 2004, he worked for Protocol Engineering
Center in ETRI. Since March 2004, he has been with theschool of Electrical Engineering and Computer Sciencein the Kyungpook National University as an Associate
Professor. He has published over 25 international
journal papers with IEEE, Elsevier, and Springer-Verlag.
His current research interests include mobility control in Future Internet,
mobile SCTP, and mobile multicasting. He has also participated in theInternational Standardization as an editor in the ITU-T SG13 and ISO/IEC
JTC1/SC6.

Fig. 13. Impact on the number of generated signal

V. CONCLUSIONSIn this paper, we propose the two schemes for distributed
mobility management (DMM): HIP-DMM-Push and
HIP-DMM-Pull. By numerical analysis, we compare theexisting centralized RVS scheme and the two proposed
distributed schemes, in terms of processing overhead at thecentral RVS server, and the HIP connection setup delays.
From the numerical results, on total transmission delay andprocessed signal at each RVS, we can see that the distributed
mobility control scheme is better than the existing centralized
RVS scheme. In particular, HIP-DMM-Pull scheme gives thebest performance among all schemes. This is because
HIP-DMM-Pull scheme’s D-RVS has less HIT-IPv4 mapping
entry than HIP-DMM-Push and Existing-HIP scheme.
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MorganStanley.
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2008.
J. Laganier, et al., Host Identity Protocol (HIP) Rendezvous Extension,
IETF RFC 5204, April 2008.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

276

Comparative Study on Cooperative
Communications in the Upper Layers of Ad Hoc
Networks
Jaeshin Jang*, Sunghong Wie***Department of Information & Communications Engineering, INJE University, Korea**Second Division of Information Technology, The Cyber University of Korea, Korea
icjoseph@inje.ac.kr, satwie@gmail.com

Abstract—In this study, we compare the cooperative MAC
protocols, the routing protocols, and the transport protocols, all ofwhich support cooperative communications. Numerical results
and comparisons of three cooperative MAC protocols withreactive helper node selection mechanisms are presented.
Research trends, as well as the advantages and disadvantages ofrelated research, on cross-layer cooperative MAC protocols,
cooperative routing, and transport protocols are described.

Mbps. As a result of this helper node’s contribution, systemthroughput has been increased. In addition, since thedestination node receives data from both the source node andfrom the helper node, it gains space diversity [1].

Index Terms—Ad hoc, cooperative, cross-layer, helper node,
MAC

T

I. INTRODUCTION

HE goal of wireless communications is to attain a hightransmission rate and end-to-end communication reliability
by combating wireless channel impairments and a limited
radio spectrum. By employing space diversity, cooperative
communications were introduced in the physical layers ofmobile ad hoc networks to overcome these limitations. The ideaof space diversity being used in cooperative communications,also called cooperative diversity, is slightly different from thetraditional space diversity (e.g., MIMO) because in cooperative
communications single-antenna mobile nodes transmit
cooperatively as a virtual antenna array. Figure 1 explains theconcept of cooperative communications. As shown in Figure 1,
there are three communication nodes in a sample network: thesource, the destination, and the helper node. The source node
sends data to the destination node; however, due to a badwireless channel, the source node can only send data at 2 Mbps.
If any communication node that is located between the source
node and the destination node accepts a role as a helper node,
then the source node and the helper node can send data at 11
Manuscript received March 7, 2013. (Write the date on which you submitted
your paper for review.) This work was supported in part by the 2012 NRF
research grant (2012-R1A1A2041831).
J. Jang is with the Inje University, Gimhae, Gyeongnam, 621-749 KOREA(corresponding author to provide phone: 82-55-320-3520; fax: 82-55-322-6275;
e-mail: icjoseph@inje.ac.kr).
S. Hong is with The Cyber University of Korea, Seoul, 110-800 KOREA(e-mail: satwie@gmail.com).

Fig. 1. Example of a cooperative communication.

Since the first studies on cooperative communications inphysical layers, numerous studies have examined the physicallayers’ aspect. However, study results on MAC protocols andupper layers are scant, particularly when compared to researchon physical layers. Therefore, a systematic study of these areas
is required [12]. Instead of examining physical layers, thispaper will conduct a trend survey of related research oncooperative communications in upper layers.
Section II is a survey of related studies, while Section III
provides a comparative analysis of cooperative MAC protocols,routing protocols, and transport protocols that are used forcooperative communications. Section IV provides theconclusion.

II. RELATED WORKTo date, the majority of cooperative MAC protocols thathave been studied have been based on the IEEE 802.11 wireless
LAN standard with various transmission rates. Determining
how to decide which helper nodes to use in cooperative MAC
protocols is one of the most important issues. According towhen source nodes select helper nodes, the helper node
selection mechanisms can be classified into two schemes:
proactive and reactive schemes. The rDCF scheme [3] uses a
proactive helper node selection scheme. Every mobile node

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

should maintain its relay table by overhearing control frames.
The source node, which has data to send, first searches for one
proper helper node from the relay table, and then transmits data
to the destination node using two-hop communication throughthe helper node. The CoopMAC scheme [4] has many points ofsimilarity to the rDCF scheme and maintains its CoopTable forselecting helper nodes in a proactive manner. However, these
two schemes only use a merit of MAC protocol, but notcooperative diversity from the physical layer.
References [5]-[7] demonstrate cooperative MAC protocols
with a reactive helper node selection scheme; in particular,
references [5][6] are particularly cross-layer cooperative MAC
protocols that receive support from the physical layer. The
reactive scheme fulfils its role only after source and destination
nodes exchange their RTS and CTS frames. In a cross-layer
triple busy tone multiple access (CTBTMA) scheme [5],candidates for helper nodes calculate the utility function after
overhearing the RTS and the CTS frame, at which point they
send a busy tone. The utility function implies the effective
transmission rate through the current wireless channel; thus, thebigger the utility function, the longer the busy tone. Therefore,
the candidate node whose utility function is the largest is
chosen as the final helper node and the node sends the ready tohelp (RTH) frame to both the source node and to the destination
node. Reference [6] proposed a cross-layer cooperative MAC
protocol where the candidate helper nodes calculate thecomposite cooperative transmission rate (CCTR) and then areclassified into several groups according to the value of the
CCTR. The contention procedure to select the best helper node
is a two-step procedure: the first step takes place among
different groups and the second step takes place among
different members of one group. This competition procedure is
known as the timer-based selection: the bigger the CCTR, theshorter the timer. Therefore, the candidate whose CCTR is thelargest is defined as the helper node. After overhearing the RTS
and the CTS frame, the cooperative relay-based auto rate
(CRBAR) MAC protocol [7] uses a p-persistent back-off
scheme to choose the helper node.
Numerous projects have researched cross-layer design.
Reference [8] extended the CoopMAC to the cross-layer
cooperative MAC protocol. The source node divides its channel
slot in two and transmits only half the data in the first half. If thehelper node receives the correct information, then it re-encodesand sends the rest of the data in the second half. Thus, thedestination node gains space diversity by receiving half thecoded data from the source node and the remaining half fromthe helper node. Reference [9] uses the CoopMAC as thefundamental access mode. According to the transmission rates
between the source node, the helper node, and the destinationnode, there are four different transmission modes: basic access,
RTS/CTS direct transmission, source-helper-destinationtransmission, and receiver maximal ratio combining. The
receiver maximal ratio combining scheme is based on the
cross-layer design between the physical layer and the MAC
protocol. Reference [10] proposed the sender initiatingconcurrent cooperative MAC (SI-CCMAC) protocol for
wireless LANs. This scheme is designed for the downlink of the277

AP’s one-hop region and supports concurrent transmissions
with the help of two-hop relaying. As is demonstrated inreferences [3][4], AP is required to maintain the helper table.
The basic idea is that AP sends multiple data packets, which areused for different destinations, to multiple helper nodes; after
this, the helpers simultaneously relay the data to thecorresponding destinations.
In addition to studies on the cross-layer design of thephysical layer and the MAC protocol, there have also beenstudies on cooperative routing and transport protocols.
Reference [11] suggested a cooperative path access control(PAC) scheme that reserves the whole path, from the source
node to the distant destination node, and then begins a pipeline
communication. Reference [12] insisted that, in order tosupport various kinds of cooperation, network and transportlayers are also needed to support cooperative communications.
Reference [13] proposed a cross-layer multilayer approachfor supporting virtual multiple input single output (MISO) links.
It includes a MAC layer any-cast protocol to find appropriate
relay nodes, between which virtual MISO links are established.
The primary path is constructed with the traditional ad hocrouting protocol, such as DSR or AODV, and relay nodes forestablishing virtual MISO links are selected to shorten the
primary path.

III. ANALYSIS AND COMPARISONThis section presents a comparative analysis of previous
related works, which are described as three different topics. For
each topic, the problems and benefits of each proposed scheme
are explained in detail.
A. Helper node selection mechanism
There are two classifications of helper node selection
mechanisms: a proactive mechanism and a reactive mechanism.
The proactive mechanism is complex, causes lots of traffic in
the network, and does not guarantee that the selected helper
node, which is determined before the RTS transmission, is thebest option for a time-varying wireless channel. In order towork properly, every mobile node should share the informationin its helper table with its neighbours. However, nocommunication protocol has been suggested to make such
sharing a possibility and any communication protocol thatenables the possibility of this sharing may unnecessarily causea heavy traffic load. Thus, the reactive mechanism is chosen as
our study topic, and three cooperative MAC protocols using
reactive mechanism are chosen for performance comparison.
The first protocol is the CTBTMA scheme [5]. It modifies
the traditional dual busy tone multiple access (DBTMA)
proposed for ad hoc networks into a cooperative MAC protocol
by adding one additional busy signal. After exchanging the
RTS and the CTS frame, the helper node candidates computethe proper channel transmission rate from the received SNRvalue in the channels between itself and the source or thedestination node. If they find they can reduce the frametransmission time, then they participate in the selection of thehelper node. Every participating candidate calculates the

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

following utility function U and sends a busy signal during aperiod that is proportional to its utility value.
⁄
⁄

(1)
(2)

⁄

After these helper node candidates finish sending the busy tone,
they should monitor whether any other candidate continues
sending a busy tone. If there is any continuing busy signal in thewireless channel, then these candidates are excluded from thishelper node selection contest. In the end, the candidate whose
utility value is the largest survives and this node sends the RTH
frame to the source and the destination node. If the number ofmobile nodes increases, then there will be more than one
mobile node with the same utility value. Then there will be
continual collisions in the helper node selection contest, which
will consume both resources and time.
The cross-layer cooperative MAC protocol [6] also proposeda timer-based reactive helper node selection scheme. Afteroverhearing the RTS/CTS frame transmitted by the source andthe destination node, respectively, helper node candidates
check whether they can contribute to increasing the effective
transmission rate or not. When it is concluded that they can be
helpful, they send a helper indication (HI) signal. They
calculate CCTR R using equation (3), and they are thendivided into several groups according to the value of the CCTR.
⁄

(3)

⁄

This scheme uses a two-step contention mechanism. The firstcontention is inter-group and the second contention is
intra-group or among the members within that group. This
contention is carried out with mobile nodes’ timers whose
values are inversely proportional to their CCTR. Each helper
node candidate triggers its timer after the CCTR calculation isfinished. As soon as its timer expires, the candidate checks
whether there is any signal, such as a group indication (GI) or amember indication (MI) signal. If any signal is found, then thecandidate is excluded from this helper node selection contest.
Otherwise, it sends a GI or an MI signal. If this transmission is
successful, the candidate sends the RTH frame. In order toavoid a possible collision with other RTH frames, the related
candidates retransmit their RTH frames in a mini-slot chosenrandomly among succeeding K mini-slots.
The CRBAR scheme proposed in Reference [7] uses areactive helper node selection mechanism in order to decide theproper helper node. First, after overhearing the RTS/CTS frame,
all helper node candidates calculate the received SNR. After
this, they begin the helper node selection process in each of thefollowing time slots, using the p-persistent back-off scheme,
where the probability p is decided using the following equation:
⁄

⁄

(4)

In the above equation, δ is the system parameter and is closely
related to system performance. However, a p-persistent

278

back-off scheme does not guarantee that the selected helper
node will be the best mobile node.
Reference [9] proposed a reactive helper node selection
scheme, which is similar to the CTBTMA. The difference isthat helper node candidates, which are determined to havesmaller expected transmission times than those in the directtransmission, participate in the helper node selection procedure
with an RTH frame rather than a busy signal. Therefore, thismechanism has the same problem, i.e. continuous collisions,when there is more than one candidate with the same expectedtransmission time.
Three different cooperative MAC protocols, suggested inreferences [5]-[7], are compared using a computer simulation.
The simulation code is implemented in C++ language with the
SMPL simulation tool [15]. For this performance evaluation,
the IEEE 802.11b wireless LAN with an ad hoc mode is used asa sample network, and the system parameters used in theperformance evaluation are described in Table 1. All mobile
nodes are assumed to be randomly distributed in an
100m × 100m square communication area, and every mobile
node is assumed to move independently in a random waypoint
model [14]. It is also assumed that every source node always
has packets to transmit in its buffer, which is called thesaturated traffic model. This saturated traffic model iscommonly used to derive the maximum system performance. In
this paper, it is assumed that there is no frame transmission
error for all frames, including the control and data frames. As
performance measures, system throughput and channel accessdelay are used; system throughput is defined as the number ofbits transmitted successfully during the whole simulation timeand channel access delay is defined as the time period from theinstant when the source node sends the CRTS frame to theinstant when the source node receives the ACK frame
successfully. The relation between the rates and the ranges isshown in Table 2 [5].
TABLE ISYSTEM PARAMETERS

Parameters
ValuesParameters
ValuesRTS
160 bits
SIFS
10 s
CTS
112 bits
DIFS
50 s
RTH
164 bits
CWmin
32 slots
ACK
112 bits
CWmax
1024 slots
DATA
1024 bytes
Basic rate
1 Mbps
MAC header
28 bytes
Slot time
20 s
GI (MI)
3 (3)
K
4
0.06
CRBAR mini-slot
6
Simulation time1500 sec
Transmission rate
1, 2, 5.5, 11 Mbps
TABLE 2
TRANSMISSION RATES AND RANGES

Data rate
Range
(
10

11 Mbps

5.5 Mbps

2 Mbps

1 Mbps48.2 m67.1 m74.7 m100 m

Figure 2 and Figure 3 show the system throughput and thechannel access delay of the CTBTMA scheme, respectively. It
is shown in Figure 2 that the CTBTMA scheme has the best

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

performance when the number of source nodes is five. This isbecause five source nodes with the saturated traffic model areenough to make this communication area completely saturated,thus when there are more than five source nodes the probabilityof transmission collisions increases, which results a thedecrease of system throughput. Figure 2 also shows that the
CTBTMA scheme has the best system throughput when thenumber of helper nodes is two. Since the number of supportabletransmission rates is limited (ex. 1, 2, 5.5, and 11 Mbps here),utility function defined in equation (1) for helper node
candidates has a limited number of values. Therefore, as thenumber of helper nodes increases, the probability that there ismore than one helper node candidate with the same value of
utility function increases. This causes repeated collisions ofhelper node candidates in a helper node selection contest,
which results in reduced system throughput.
It is shown from Figure 3 that channel access delay is closely
related to the number of source nodes but not to the number ofhelper nodes. This means that the contention among source
nodes with CRTS frames to seize a wireless channel is a more
dominant factor to channel access delay than that among helper
node candidates with RTH frames.

Fig. 2. System throughput of CTBTMA

Fig. 3. Channel access delay of CTBTMA

Figure 4 and Figure 5 show the system throughput and thechannel access delay of the cross-layer cooperative MAC
protocol (for the sake of simplicity, called the cross-layer MAC
from here on) suggested in Reference [6], respectively. It isshown in Figure 4 that this cross-layer MAC protocol has the279

best system throughput performance when the number ofhelper nodes is between 5 and 20, which is greater than thevalue 2 in the CTBTMA scheme. In addition, systemthroughput performance is greater than that of the CTBTMA
scheme over its whole gamut. It is shown in Figure 5 that thechannel access delay of the cross-layer MAC performance isslightly lower, but it has a pattern similar to the CTBTMA
scheme.

Fig. 4. System throughput of the cross-layer MAC [6]Fig. 5. Channel access delay of the cross-layer MAC [6]Figures 6 ~ 8 show the system throughput and the channel
access delay of the CRBAR MAC protocol suggested in
Reference [7], respectively. The system parameter δ inequation (4) used to derive numerical results in Figure 6 andFigure 7 is 0.02 . It is shown in Figure 6 that the systemthroughput of the CRBAR scheme tends to decrease rapidly
compared to Figure 2 and Figure 4. It is also shown in Figure 7
that the channel access delay of the CRBAR scheme tends toincrease a little more rapidly than the CTBTMA scheme andthe cross-layer MAC scheme. Since the CRBAR scheme makes
use of the p-persistent back-off scheme to select an appropriate
helper node, its performance is greatly dependent on the systemparameter δ in equation (4). The relationship between thesystem throughput and the system parameter δ is shown in
Figure 8 when the number of helper nodes is 5 and 20,
respectively. In this figure, no cooperation means the
traditional IEEE 802.11 wireless LAN protocol without
cooperation. It is shown in Figure 8 that the CRBAR has thebest system throughput when the system parameter δ 0.06.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

Therefore, the system parameter with δ 0.06 for the CRBAR
scheme will be used for performance comparison of these three
cooperative MAC protocols.

Fig. 6. System throughput of CRBAR (δ

0.02

280

node candidates with the same value in equation (1) or equation(3) increases.

Fig. 9. System throughput of three schemes as a function of the number ofhelper nodes

Figure 10 shows the system throughput performance forthree cooperative MAC protocols and the IEEE 802.11b MAC
protocol without cooperation when the number of helper nodes
is 10. It is shown that all three cooperative MAC protocols havebetter system performance than the MAC protocol without
cooperation, and the throughput gap between Reference [6] andthe others is large. Since, with the saturated traffic model,
source nodes always have data frames in their buffers, thisfigure shows that the total system throughputs for the
CTBTMA and the MAC protocol without cooperation reachthe maximum value when there are approximately five source
nodes.
Fig. 7. Channel access delay of CRBAR (δ0.02

Fig. 10. Comparison of system throughput as a function of the number of source nodesFig. 8. System throughput of CRBAR for various δ

Figure 9 and Figure 10 show system throughputs for three
cooperative MAC protocols in a different environment. First,
system throughputs in Figure 9 are derived when there are fivesource nodes and δ=0.06, as is the case with the CRBAR
scheme. This figure shows that the cooperative MAC protocolsuggested in Reference [6] has the highest system performance,
while the next highest is the CTBTMA scheme [5], and thethird highest is the CRBAR scheme [7]. As the number ofhelper nodes increases, the success probability in choosing thecorrect helper node decreases because the number of helper

B. Cross-layer design
Since the approach using only the physical layer or the MAC
protocol has a limit for maximizing a merit of cooperative
communications, the cross-layer design between two layers is
required. There have been many studies on the cross-layer
design, especially with a physical layer and the MAC protocol.
Reference [8] extended the CoopMAC [4] to the cross-layer
cooperative MAC protocol in order to enhance system
performance. Each channel block, or packet, contains data (B)and parity bits (r), leading to the equation N B r coded bits.
The source node divides its channel block in two and transmits
only half of its coded bits in the first half time slot. These bits

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

are received both by the destination node and the helper node. If
the helper node has the correct information, then it re-encodesand sends the remaining half in the second half time slot. Thus,
the destination node receives half the coded bits from thesource node and the remaining bits from the helper node,creating space diversity. But there are two questions that result
from using this approach. The first question is related to the factthat the first half of coded bits, which the helper node receives
from the source node, may not contain enough parity bits
because it is assumed that r B, as is shown in Reference [8].
Thus, the first question is how can the helper node decide
whether the received bits are erroneous or not without parity
bits in the extreme case? The second question is that separating
the assigned transmission period in two time slots is opposite to
the design goal of the MAC protocol, the goal of which is to
reduce transmission time by raising channel transmission rate.
Reference [9] also extended the CoopMAC scheme to thecross-layer MAC protocol. By using three transmission rates,
one for the direct path and two for the two-hop path, thisscheme uses four different transmission modes. This approachseems to be as good as the cross-layer design between thephysical layer and the MAC protocol; however, mobile nodes
should share lots of information with their neighbours. The
SI-CCMAC protocol proposed in reference [10] for wirelessLANs is designed for the downlink of the AP’s one-hop regionand supports concurrent transmissions with the help of two-hop
relaying. However, it is questionable whether this concurrent
transmission in an AP’s region is possible, especially when thehelper nodes are located near one another.
C. Cooperative approach in upper layers
Compared with current studies about the physical layers andthe MAC protocols, studies on cooperative routing protocols ortransport protocols are relatively scant. Reference [11]investigated the overheads and challenges that emerge from
implementing cooperative communications in a realistic
network. Cooperative PAC, which is proposed in Reference[11], is a CSMA-based scheme where the source node sends an
RTS-like control frame and then the destination node respondswith a CTS-like control frame. This exchange reserves thechannel along the entire route for a specified period of time. For
route selection in the cooperative PAC, a modified DSR routing
protocol, which needs a cross-layer design between the routing
and the MAC protocol, is proposed.
Reference [12] insisted that, in order to maximize the meritof cooperative communications, systematic studies on the
network and transport layers are required. Compared to
traditional one-link based communications, cooperative
communications generate a new concept for links, which is amulti-path link between two neighbouring mobile nodes, andthis is called a cooperative link. Therefore, cooperative routing
protocols handling a sequence of cooperative links should bestudied systematically. In this reference, to increase theachieved throughput with cooperation, aggregating the offeredresources from different cooperating entities is considered. Thiskind of cooperative communication can be achieved through281

cooperative strategies in the network and transport layers. In
this scenario, source and destination nodes use several IP
addresses. As a result, a new transport layer approach formerging several IP addresses into one network address at eachend point should be required, and the typical solution may be atransport layer protocol with a multi-homing feature.

IV. CONCLUSION
In order to maximize the benefits from cooperative
communications, systematic research into whole layers is
required. At the beginning of this research, a survey oncooperative communications in the upper layers, rather thanphysical layer, has been described. It includes the helper node
selection mechanisms, the cross-layer cooperative MAC
protocols, the network layer, and the transport layer supportingthe cooperation. Their problems and solutions are described inorder to decide future research directions. The performances ofthree cooperative MAC protocols with reactive helper node
selection mechanisms are evaluated and compared to eachother using a computer simulation.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013282

[14] David B. Johnson and David A. Maltz, “Dynamic source routing in ad
hoc wireless networks,” in Mobile Computing, Chapter 5, pp. 153~181.
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Jaeshin Jang (M’90) received a B.S. degree inElectrical Engineering from Dong-a University, Korea,
in 1990, and M.S. and Ph.D. degrees in Electric andElectrical Engineering from KAIST, Korea, in 1992
and 1998, respectively. From July 1997 to February
2002, he worked for Samsung Electronics Company.
From August 2008 to July 2009, he was a visiting
scholar at Iowa State University. Since March 2002, he
has worked for INJE University, Korea. His majorinterests are wireless QoS, MAC, routing, andcooperative communications in wireless communications networks,
including mobile WiMAX, ad-hoc networks, and mesh networks.

Sunghong Wie received B.S., M.S. and Ph.D. degrees
in electrical engineering from KAIST, South Korea, in
1995, 1997, and 2001, respectively. From 2001 to 2008,
he was with Samsung Electronics as a senior engineer.
Currently, he is with the Division of Information
Technology at The Cyber University of Korea. His
recent research interests include mobility management
and the future Internet.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

283

Design of Small-Area Transimpedance Optical
Receiver Module for Optical Interconnects
Jamshid Sangirov, Ikechi Augustine Ukaegbu, Nga T. H. Nguyen, Tae-Woo Lee, Mu-Hee Cho, andHyo-Hoon Park
Photonic Computer Systems Laboratory, Electrical Engineering Department, KAIST, Daejeon, Korea 305-701
jamshid@kaist.ac.kr, aus20@kaist.ac.kr, nganth@kaist.ac.kr, twlee@ee.kaist.ac.kr, chophy@kaist.ac.kr,
pakrhh@ee.kaist.ac.kr
Abstract—The development and miniaturization of electronic
devices and components is pushing the system devices and theirinterconnecting interfaces to become even smaller. Thus, reducing
the size of receiver (Rx) and transmitter (Tx) chips plays an plays
an important role in designing a small-size optical modules utilized
in o/e and e/o converters. Therefore, designing a small-area optical
Rx may require intuitive solutions, such as building single-ended
Rx and utilizing some of the advantages of differential Rx. Optical
Rx should convert optical input signal to voltage output signal andprovide sufficient gain and frequency operation for feeding tosubsequent blocks including clock and data recovery circuit (CDR)
and/or Serializer and Deserializer (SerDes). Therefore, we havedesigned a small-area transimpedance optical receiver (TIORx)
using regulated-cascode (RGC) as an input stage which converts
input photocurrent to voltage signal. The RGC block is connected
to post amplifying stages to increase the overall transimpedance
gain of the TIORx. The post amplifying gain stages utilizes twointersecting active feedback in order to increase the frequency
operation in addition to increasing the gain of the proposed TIORx
chip. The TIORx module is designed in a 0.13μm CMOS
technology and works up to 10 Gbps data rate. The TIORx chipcore occupies an area of 0.051mm2 with power consumption of 16.9mW at 1.3 V. A measured 3-dB bandwidth of 6.9 GHz was
obtained for the TIORx module with a transimpedance gain of 60
dBΩ.
Keyword—Optical receiver, small-area circuit design,
bandwidth improvement, optical interconnections

I. INTRODUCTIONA

S the data rate of internet traffic is increasing the electricalinterconnection cannot provide reliable data at higher
frequencies due to its limitations at high frequency operation [1].
Thus, optical interconnection is dominating the market becauseof its advantages such as low electromagnetic

Manuscript received March 12, 2013. This work was supported by the IT
R&D program of MKE/KEIT [10039230, Development of bidirectional 40
Gbps optical link module with low power in Green Data Center for Smart
Working Environment] and it was also supported by the Center for Integrated
Smart Sensors funded by the Ministry of Education, Science and Technology as
Global Frontier Project (CISS-2012366054191).
J. Sangirov, I. A. Ukaegbu, N. T. H. Nguyen, T.-W. Lee, M.-H. Cho, and
H.-H. Park are with Electrical Engineering Department of Korea Advanced
Institute of Science and Technology (KAIST), Daejeon, Korea, 305-701(phone: +82-42-350-5453; e-mail: jamshid@kaist.ac.kr).

interference (EMI), negligible insertion loss with inner-channel
crosstalk for multi-channel optical link and light weightcomparing to electrical interconnection at high data rates.
Hence, optical interconnection system is being widely utilizedin high data rate video and data transmission systems [2].
However,
opticalinterconnectionalso
requireselectrical-to-optical (e/o) and optical-to-electrical (o/e)
converters. Designing a small-size o/e and e/o converters isimportant. Thus, reducing the size of receiver (Rx) andtransmitter (Tx) chips plays an plays an important role indesigning a small-size optical modules utilized in o/e and e/oconverters. Since, the development and miniaturization of
CMOS technology is pushing the design limits to design evensmaller electronic devices and their interconnecting interfaces.
Thereby, requiring the reduction of optical modules of o/e ande/or converters utilized in applications such as small form-factor
pluggable (SFP) and system in package (SiP) systems forboard-to-bard, chip-to-chip and rack-to-rack opticalinterconnection [3-4].
Designing of a compact optical Rx circuits for high-speed
operation is important to reduce the signal propagation loss andreduce the active are used for chip design. Hence, designing of
small-area optical Rx modules may require intuitive solutions,such as utilizing some of the advantages of differential Rx in
designing of a single-ended Rx modules. Optical Rx chip should
also be able to provide a sufficient gain and frequency operation
with reduced size for feeding the signal to post-processing
blocks such as CDR and/or SerDes [5]. In our previous work,
we have analyzed the performance of single-ended anddifferential Rx in terms noise, power, size, and gain [6]. Fromcomparison of single-ended and differential topology in table 1,
the single-ended Rx chip size showed much smaller size. Hence,the size of Rx module can be significantly reduced by using asingle-ended Rx. However, the data rate of single-ended Rx
module was relatively small comparing differential topology.
Therefore, we have tried to improve the performance
single-ended Rx by increasing the data rate of Rx using he Rx
chip [7].
In an optical Rx design, the transimpedance amplifier (TIA)
plays the role of a front-end amplifier for amplifying the weak
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

current signals generated from the photodiode (PD) andconverting to voltage signal which would be fed to subsequent
blocks (limiting amplifier or clock and data recovery circuit).
The conventional Rx module consists of a TIA preamplifier
block, limiting amplifier, and an output buffer. TIA design using
inductive peaking for bandwidth enhancement and high data
rate operation has been proposed in [8]. However, the excessive
size of the inductor makes the total chip size big. An
inductorless TIA has been designed in [9], where several shuntfeedback TIAs connected in parallel were suggested forbandwidth improvement and chip size reduction. However, thisdesign method has high power consumption due to the several
TIAs deployed. In conventional Rx circuits, the TIA may nothave enough gain and bandwidth to drive other circuits and as aresult, may require the use of LA, equalizer [10-11], and otherperformance enhancement circuitry. However, due to the needfor high data-rate and high performance operation, the need forthe additional circuitry (LA, equalizer, etc.) will result to theincrease in circuit complexity and thus, lead to increase in total
chip area and increase in overall power consumption.
In this work, we propose a small area, high data-rate andinductorless TIORx which combines some of the advantages ofboth the TIA and Rx. While acting as a front-end amplifier for
the weak signals from the PD, it also incorporates the gain andbandwidth enhancement advantages of an LA, in a conventional
Rx. Thus, due to the absence of a dedicated LA stage, the
TIORx module reduces the total chip area, while improving thegain and bandwidth performance. In our proposed design, the
TIORx is made up of a regulated cascode (RGC) input stage; aninter-stage buffer; a gain stage with intercepting active
feedbacks; and an output stage (which acts as a buffer). The
RGC input stage has been utilized to reduce the input
capacitance effect from the PD while improving the bandwidth[12]. The inter-stage buffer used between RGC and gain stage
reduces parasitic capacitance at the output of RGC block,thereby improving bandwidth performance. The post
amplifying stages utilizes active feedback only, making the chipless sensitive to process changes compared to a passive
feedback system. The fabricated TIORx achieves atransimpedance gain of 60 dBΩ and occupies an area of 50.6
µm2, which is quite small when compared to other high data-rate
optical receivers of similar CMOS technology.
II. THE TIORX CIRCUIT DESIGNThe TIORx is designed in a 0.13μm CMOS technology andthe schematic of the TIORx chip is shown in Fig. 1. The input
stage, which is the RGC, consists of transistors M 1 and M2.The
RGC input stage reduces the input impedance by the amount of
its own voltage gain, which prevents the input pole fromdominating the TIORx bandwidth and reduces capacitive effectof the PD [5]. Thus, the RGC circuit can be used effectively for
CMOS integration as a front-end amplifier. The input
impedance of RGC stage is given as [12]:Z IN  RGC 

284

1
,
gm2 (1  gm1 R1 )(1)

where 1+gm1R1 is the gain of the local feedback and with the
product of the common gate stage, it behaves as a largetransconductor Gm=gm2(1+gm1R1). Thus, the size of local
feedback decides the amount of reduction of input parasitic
capacitance effect for bandwidth determination. The RGC
peaking frequency in frequency response is given as:
Inter stage

RGC

R 1 R2M2IPD

B

Gain stage

R5
Af1

R4

RF

C

R6

M1
Rs1

M4

M5

R8
D

Vout

Af2

M3

A

R7

M6

M7

M8

Rs2

Fig. 1. (a) Schematic of TIORx.

f peak 

1.
2 R1 (Cgs 2  Cgd 1 )(2)

At low frequency, the open-loop transimpedance gain of the
TIORx is given as:

Z IN GAIN  ( R2 RF )gm3 Rs 2
 gm4 gmf1  (3)(1  gm3 Rs 2 )R4  gm5 gmf 2  R5 gm6 R6 gm7 R7 gm8 R8The TIORx is made up of four stages, namely, an input stage(which consists of the RGC); an inter-stage; a gain stage withintercepting active feedbacks; and an output stage (which acts as
the buffer). The RGC block is important in the TIORx circuit as
it affects the input noise and the stability of the whole TIORx
circuit while delivering the input photo current to the output
with increased gain. Thus, the design parameters have to becarefully chosen not to interfere with the input impedance forhigh frequency operation.
M3 and M4 make up the inter-stage stage of the TIORx, wherehigh frequency operation should be maintained for delivering
the converted input current to output voltage for the gain stage.
The impedance at the drain of M 2 (of RGC stage) and M4 (ofinter-stage) are reduced by factor of 1+A by RF shunt feedback
and correspondingly, the poles are sped up by the factor, 1+A. A
common-drain (CD) is placed at the drain of M 2 because thecapacitive effect on bandwidth is small [12]. The inter-stage
isolates the RGC input stage from the gain stage and also adjusts
the input dc level from the RGC stage.
To increase the overall gain of the TIORx to higher output
voltage levels, several common source (CS) amplifying stages(M5 – M7) have been utilized. However, placing these CS gainstages may reduce the bandwidth. Thus, to increase thebandwidth while maintaining a high gain level, two intersecting
active feedback stages have been utilized [13]. The negative
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013active feedback utilized in the gain stage is different from theconventional resistive feedback which avoids the direct resistive
load to the preceding transimpedance stage. Moreover, active
devices suffer less process variations than passive devices
during fabrication. Hence, adding the active feedback stages,
Af1 and Af2, compensate by peaking at high frequencies. The
high-frequency peaking occurs at Af1 and Af2 active feedback.
The peaking of the first and second active feedbacks is given as:

f peak , f 1 1
2 R4 (Cgd 6  Cgdf 1 )f peak , f 2 1
2 R5 (Cgd 7  Cgdf 2 )

(4)

(5)

As the circuit is more complex, we separate the circuit frompoint A to B, B to C and C to D as shown in Figure 1. Firstly, thepoit from Iin to IB is the core RGC block, front-end of TIA circuit,which has an important effect in the input noise and stability of
the whole Rx circuit and delivers the input photo current to the
output with increased gain. Thus, the design parameters have to
be carefully chosen to interfere with the input impedance forhigh frequency operation. The transfer function can be derived
as follows:
sC
1 1
IB
gm1
(s) 
s (Cgs1  Csb1 ) 
I in1  (1  gm R ) gm  1  sR1  C1  Cgs 2  Cgd 2  
1 1
2


(6)

The next point is from IB to VC is inter-stage block, where thehigh frequency operation should be maintained for delivering
the converted input current to output voltage for the gain stage.
The impedance of drain M2 and M4 are reduced by factor of(1+A) by RF shunt feedback and correspondingly the poles has
been speed up by the factor (1+A). The common-drain (CD) isplaced at the drain of M2 because the capacitive effect on
bandwidth is small [12]. The transfer function from B to C iswith simplification of R1 and Rf parallel resistors,
is given as follows:1'
RF  ( R1 || RF )

 gm3 Rs 2 1  gm4 R4
 1  gm3 Rs 2 VC
gm3 gm4 Rs 2 R4 RF'  sCgs3 
(s) 
1 

IB
1  gm3 Rs 2 
gm3 

(7)
1
1
 s( RF' Cgs 3  gm3 Rs 2 RF' C1*   s  Cgs 3  Cgs 4  1  1 1  gm3 Rs 2
gm3


 
The intersecting active feedback from VC to Vout is the gain stage,
where the voltage input has been increased to higher output
voltage levels. Adding the negative active feedback increases
the 3-dB bandwidth. The active feedback effects of Af1 and Af2
has been included in the transfer function and the equation is
given as:

285

Vout
G5 ( s)G6 ( s )G7 ( s )VC 1  G5 ( s )G6 ( s )Gf1 ( s )  G6 ( s )G7 ( s )Gf 2 ( s )


(8)

3

G ( s)
1  2G 2 ( s)Gf(s)with the assumption G (s)  G ( s)  G ( s)  GmR , and567

1  sRC

GmfR .
Gf1 ( s)  Gf 2 ( s)  Gf ( s) 1  sRCThe last stage capacitance effect is loaded by input capacitanceof output buffer. After combining the equations (6) to (8) we canwrite the transfer function of Rx as:
Vout I B VC Vout
 * 

I in
I in I B VC
sC
1 1
gm1
s (C gs1  Csb1 ) 
(9)1  (1  gm R ) gm  1  sR1  C1  C gs 2  C gd 2  
1 1
2 

gm3 gm4 Rs 2 R4 RF'  sCgs3 
1
1 

'
1  gm3 Rs 2
gm3   s ( RF C gs 3  gm3 Rs 2 RF' C1* 1 
1  gm3 Rs 2
G 3 (s) s  C gs 3  C gs 4   1  2G 2 ( s )Gf(s)
1 

gm3


1

The transimpedance gain of the TIORx can be obtained fromequation (9) and is written as follows:

ZT (0) 

gm3 gm4 Rs 2 R4*
1  gm3 Rs 2





RF
 R ||
 1

 gm3 Rs 2 1  gm4 R4 
 1  gm3 Rs 2 (10)

The 3-dB bandwidth of the TIORx is affected by dominantpoles at amplifying stages of gm3, gm5 and gm6. Thus, thedominant poles can be described by the frequency response of
the gain stages with transconductance of the dominant poles
given by the time constant equations of (11) to (15): eq   3   5   6

 3   RF ||

1  Cs 3  Cgs 4  1  gm4 R4  Cgd 4 
gm3  
1

(11)

(12)


1 
  RF ||
 1
gm3 


Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

 5    R4 


1 
1 
 Cs 5  Cgs 6  (1  gm6 R6 )C gd 6  ||gm4  gmf1  

(13)

2


1 
1 
   R4 
2
 ||

gm
gm
f1 
4 


286

transistor sizes have to be chosen carefully not to increase thenoise of TIORx or reduce bandwidth performance. Thus, thetransconductance of M3 and M4 have been chosen to be
gm3=2gm4.
III. EXPERIMENTAL RESULTS



1 
1 
 Cs 6  C gs 7  (1  gm7 R7 )C gd 7  ||gm5  gmf 2  

 6    R5 


(14)

3


1 
1 
   R5 
3
 ||

gm
gm
f 2 
5 


1 
1 
1 

   R4  ||gm3   
gm4  gmf1  eq   RF ||


(15)


1 
1 
  R5  1 2 3
 ||
gm
gm
f 2 
5 


The 3-dB response of the TIORx can be obtained with τeq from
f3dB=1/(2πτeq). Writing in terms of three dominant poles of
TIORx, the three major poles would be P1=τ1; P2=τ5; P3=τ6.
The input-referred noise of TIORx is described as follows:
The input referred noise of Rx can be described as follows: s  Cdb 2  C gs 3  
 I d23  I R2 3



gm3



The TIORx was fabricated in a 0.13µm CMOS technology
and has a core size (without pads) of 0.051mm2. The TIORx
chip is mounted on wire-bounded chip-on-board (COB) for
frequency response, eye-diagram and integrated output noise
measurements. The die photograph of TIORx chip and TIORx
module are shown in Fig. 2. The TIORx module consists of the
TIORx chip, photodiode, and 100 nF single layer capacitor
connected between the PD and voltage supply to reduce theripples from supply voltage. The transimpedance gain (ZT) of
the TIORx chip was calculated from the measured S-parameterdata using an Agilent 8703B lightwave component analyzer.
Frequency response is measured from 1 GHz to 16 GHz. With a
1KΩ shunt passive feedback, RF, and 240 fF photodiodecapacitance, Cpd, a measured transimpedance gain of 60 dBΩ
with a 3-dB bandwidth of 6.9 GHz are achieved for the TIORxchip, as shown in Fig. 3.

2

I n ,eq  I  I  I
2
s1

2
Rf

2
R2









 R1 1  s  Cin  C gs1  Csb 2  
 I12



1  gm2 R1  Rs2

 s  C gs 2  C gd 1   I d22  I R2f  I R2 2



gm1


2





 1
1
1  4kT   Cdb 2  C gs 3 
 4kT 

 
R
R
R
gm32
f
2 
 s1
2

(a)
2


1  4kT   C gs 2  C gd 1  
1
1 
 
   
 
2
R3 
gm1
Rf R2 
2

2

2
4kTR12 1   2  Cin  C gs1  Csb 2      1 



2
2
R1 
1  gm1R1  Rs1


(16)

where k is Boltzmann’s constant; T is the absolute temperature; is the channel-noise factor of MOSFET; Cin is the input
parasitic capacitance which includes the photodiodecapacitance; bond-pad parasitic capacitance, and electrostatic
discharge capacitances (Cin=Cpd+CESD+Cpad). From eq. (16), it
can be observed that low frequency noise is dominated byresistor thermal noises and high frequency dominant noise occur
due to input parasitic capacitances. The dominant
high-frequency noise is divided by (1+gm1R1) gain of the local
feedback, and hence, the size of local feedback has been
increased to reduce total equivalent noises. To reduce the noise
generated from the inter-stage, the size of M3 should bedecreased to reduce its gate-source parasitic capacitance, Cgs3,
and gm4 should be increased as large as possible. However,increasing the size of M4 leads to reduction in bandwidth andincrease in the input-referred noise. Therefore, the inter-stage(b)
Fig. 2. (a) Die photograph of the fabricated TIORx chip, and (b) TIORx
module.

Fig. 4 shows the integrated output noise measured from theoutput of the TIORx chip with no input connected. The standard
deviation of 0.52 mV and by subtracting the oscilloscope noiseof 0.1 mV, the corrected integrated noise is 0.42 mV. Fig. 5
shows the graph of input-referred noise based on equation in [8]and measurement data. The estimated noise at 3-dB is
21pA/√Hz.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

Fig. 3. The measured ZT (dBΩ) of the proposed TIORx chip.

A 3-dB bandwidth of 6.9 GHz was obtained for the TIORx
module as shown in Fig. 6. The eye-diagram have beenmeasured using Anritsu MP1736 pulse-pattern generator with
231-1 pseudorandom binary sequence input signal, and Agilent
8610A oscilloscope. The eye-diagram is shown in Fig. 7 for the
TIORx module. For an applied input light power of -9dBm, an
output of 60 mV was achieved for the TIORx module.

Fig. 4. Measured integrated noise output of the proposed TIORx.

287

TABLE ICOMPARISON OF THE PROPOSED TIA PERFORMANCE WITH OTHER WORKS
This
Ref.
[8]
[9]
[14]
[15]
[16]
work
CMOS technology
0.13
0.13
0.13
0.18
0.18
0.18
(µm)
Supply
1.3V
1.2V
2V
1.8
1.8
1.8
Power dissipation
13.9
16.9
4.1
98
91.8
70.2
(mW)
7
Inductor
Yes
Yes
Yes
Chip core
0.051
0.071
0.061
0.8
0.14
size(mm2)
Data rate (Gbps)
10
10
10
10
10
10
3-dB BW (GHz)

6.9

7.5

6

7.2

7.2

8.5

Gain (dBΩ)
Sensitivity 10-12(dBm)
GBP/PDC

60

50
10
µA
578

62
22.4
µA
78

75
-18.
9
441

61
10
µA
114

51.7

-9
408

234

Gain bandwidth product per DC power (GHz Ω/PDC), bandwidth (BW).

Table I shows a comparison of the TIORx performance withother works. In our proposed TIORx, a relatively highgain-bandwidth product per DC power (GBP/P DC) of 408
GHzΩ/mW is achieved, compared to the state of the art TIAs. In
our proposed design, the addition of inter-stage and post
amplifying transconductance stages increases the total noise at
higher frequencies. Increase in input-referred noise leads todegradation of input sensitivity. Therefore, the sensitivity in ourdesign is slightly smaller comparing to other artworks. Our
proposed TIORx module combines the gain and bandwidth
improvement advantages of a conventional Rx module, while
acting as a front-end amplifier for low signals from the PD.
From Table I, it can also be seen that the size of the proposed
TIORx is smaller than the other TIA chips. Thus, the proposed
TIORx module consumes low power and occupies a small area
when compared to other high data-rate Rx modules of similar
CMOS technology.

Fig. 5. The measured input-referred noise of the proposed TIORx chip.

The root-mean-sqaure (rms) jitter of 11.57 ps and rise/fall timeof 86.7/85.6 ps was obtained (10% to 90%) at 10 Gbps for the
TIORx module. The bit-error-rate (BER) performance of the
TIORx module has been measured as shown in Fig. 8. A BER
performance of less than 10-12 was recorded for TIORx module
at 10 Gbps with -9 dBm input power. The power dissipation of
the TIORx is 16.9 mW at 1.3 V of voltage supply.

Fig. 6. Normalized S21 measurement result of the proposed TIORx module.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

288

[5]

Fig. 7. Eye diagram of the TIORx module with input power of -9 dBm (62µA
with 0.5 A/W responsivity of PD), 50 ps/div, 13 mV/div.
8-log(BER)

9

10

11

12

13
-16 -15 -14 -13 -12 -11 -10 -9
Input power (dBm)-8-7

Fig. 8. BER of the proposed TIORx module at 10 Gbps.

IV. CONCLUSIONA TIORx module has been designed and fabricated in
0.13μm CMOS technology for optical interconnect applications
which operates up to 10 Gbps data rate. The measured 3-dB
bandwidth of the TIORx is 6.9 GHz with a transimpedance gainof 60 dBΩ. While utilizing two intersecting active feedback
systems with RGC input stage, the TIORx chip core occupies asmall chip area of 0.051mm2 and has a power consumption of
16.9 mW at 1.3 V. Our proposed TIA-Rx can be applied as afront-end optical Rx to convert the input photo current to output
voltage signal, high enough to feed to the next stages such as the
De-serializer, PLL, and/or CDR circuits, and it is applicable forchip-to-chip optical interconnects. A GBP/PDC of 408
GHzΩ/mW was achieved. The TIORx module is applicable forchip-to-chip optical interconnects.
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Jamshid Sangirov received the M.S. degrees inInformation and Communication Engineering from
Yeungnam University in 2006. He is a Ph.D. student
in Electrical Engineering at Korean Advanced
Institute of Science and Technology (KAIST) since2008, he is staying with the Photonic Computer
Systems Laboratory, KAIST. He worked with the
RFIC Design Team, at Teltron Inc., Korea, from2010 to 2011. His research interests areanalog/RF/VLSI and high-speed electronics, optical interconnections, opticaltransceiver modules.

Ikechi Augustine Ukaegbu received the B.S.
degreeinElectricalEngineering,Electro-mechanics, and Electro-technology, theM.S. degree in electronics and microelectronics
from Moscow Power Engineering Institute,
Technical University, Moscow, Russia, in 2004
and 2006, respectively, and the Ph.D. degree in
information and communications engineeringfrom the Korea Advanced Institute of Science and Technology (KAIST),
Daejeon, Korea, in 2012. He was with the System Convergence Technology
Team, Convergence and Components Materials Research Lab, Electronics andTelecommunications Research Institute, Korea, from 2008 to 2009. He iscurrently with the Electrical Engineering Department, Photonics Computer

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 4, July 2013

289

System Laboratory, KAIST, as a Post-Doctoral Scholar. His current researchinterests include optical interconnection for high speed and low loss
multichannel data links in computer and communication systems, opticalinterconnections for chip-to-chip and board-to-board data links, chip design for
optical transceiver modules, integration, and packaging of optoelectronic
components.

Nga T. H. Nguyen received the BS degree in
Post
Telecommunication
InstituteofTechnology, Hanoi, Vietnam in 2003. She
received the M.S degree in August, 2005 andnow is Ph.D candidate in Korea Advanced
Institute of Science and Technology. Her
research interests are low-power and low-cost
optical circuit design, and building system for
optical interconnection applications.

Tae-Woo Lee received the Ph.D. degree inElectronic and Electrical Engineering from the
University of Sheffield, Sheffield, U.K., in 1992. He
was an Associate Researcher in the area ofcompound semiconductor devices with the
University of Sheffield, from 1992 to 1994. From1994 to 2000, he was with the Electronics andTelecommunications Research Institute, Daejeon,
Korea, working in the area of high-speed electronic
devices and circuits and MMIC. From 2000 to 2003,
he was a CEO/CTO with the Venture Company, producing RF PAM and
MMIC modules. Since 2003, he has been a Research Professor with the
Photonic Systems and Engineering Group, Korea Advanced Institute of
Science and Technology, Daejeon. His current research interests include
optical interconnections for chip-to-chip and board-to-board data links, opticaltransceiver modules, and optical switching components for high-speed opticalinterconnection.

Mu Hee Cho received the Ph.D. received the Ph.D.
degree in Physics from Dankook University,
Cheonan, Korea, in 1999. He was with theZenphotonics Company, Daejeon, Korea, from2000 to 2003, where he was engaged in the researchand development of polymer-based optical devices.
Since 2003, he has been researching on the
chip-to-chip optical interconnections using opticalprinted circuit boards (PCBs) as a Research
Professor with the Photonic Systems andEngineering Group, Korea Advanced Institute of Science and Technology,
Daejeon. His current research interests include high-speed optical modules,
optical interconnection, and optical PCBs.

Hyo-Hoon Park received the Ph.D. degree inMaterial Science and Engineering from the Korea
Advanced Institute of Science and Technology(KAIST), Daejeon, Korea, in 1985. He was a
Post-Doctoral Scholar in the area of compound
semiconductor devices with Stanford University,
Stanford, CA, USA, from 1985 to 1986. From 1986
to 1997, he was with the Electronics andTelecommunications Research Institute, Daejeon,
working in the area of high-speed electronic devices
and vertical-cavity surface-emitting lasers. Since 1998, he has been a Professor
with the Photonic Systems and Engineering Group, KAIST. His current
research interests include optical interconnections for chip-to-chip andboard-to-board data links, optical transceiver modules, and switchingcomponents for optical-link computer systems.

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