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Title

Intermediate Results of Wireless Access andMultiple Antennas Technologies

Author(s)Matth´e, Maximilian; Cheng, Meng; Hou, Jiancao;
Yi, Na; Tadashi, Matsumoto

Citation

ICT‑619555 RESCUE D1.4 Version 1.0: 1‑49

Issue Date

M12

Type

Research Paper

Text version

publisher

URL

http://hdl.handle.net/10119/13795

RightsThis material is posted here by permission of the
EU FP7 RESCUE Project. http://www.ict‑rescue.eu/
RESCUE is founded by the European Commission
under the 7th Framework Programme,Theme 3‑"ICT"call FP7‑ICT‑2013‑11,Work Programme Topic
1.1"Future Networks"

Description

Japan Advanced Institute of Science and Technology

ICT-619555 RESCUE
D1.4 Version 1.0
Intermediate Results of Wireless Access and Multiple Antennas Technologies

Contractual Date of Delivery to the CEC:
Actual Date of Delivery to the CEC:

M12

EditorMaximilian MatthéAuthor(s)Maximilian Matthé, Meng Cheng, Jiancao Hou, Na Yi, Matsumoto
Tadashi

Participants

TUD, JAIST, UNISWork package

WP1 - Theoretical analyses for limit and rate-disortion region

SecurityCO

NatureR

Version

1.0

Total number of pages

49

Abstract: In this deliverable intermediate results on multiple access and multiple antenna technologies for
RESUE are described. We start with an introduction summarizing the requirements on the physical layer
and multiple access technologies. The main part is dedicated to non-orthogonal multiple access, where we
investigate the suitability of GFDM and IDMA for RESCUE requirements. We can show that both techniques
are suitable for RESCUE. Initial considerations are carried out, how GFDM and IDMA can be combined to
combine the benefits of both schemes. Furthermore, we describe a reduced-complexity algorithm for solving
the power distribution for coordinated beamforming in a multi-relay scenario. It is shown that the describedalgorithm outperforms existing algorithms in terms of computational complexity and convergence speed.
Keyword list: unpredictable environment, multiple access, multi-antenna technologies, generalized frequency division multiplexing, interleaved division multiple access, coordinated beamforming
Disclaimer:

RESCUE

D1.4, Version 1.0

Executive Summary
This deliverable contains the description of current research status on multi-antenna and multiple access (MA)schemes for the considered RESCUE scenarios. Initially, a summary of the requirements posed by the RESCUE
scenarios on the employed waveform and MA scheme is given. Most important are robustness against asynchronicity and unpredictable environments, but also flexibility and latency need to be considered. Then, carrier
sense multiple access with collision avoidance (CSMA/CA) is shortly introduced as it is the MA scheme which isconsidered for implementation in RESCUE WP3.
Afterwards, we emphasize on non-orthogonal MA schemes being researched under RESCUE. In particular, thenon-orthogonal waveform generalized frequency division multiplexing (GFDM) is considered as a promising candidate for RESCUE scenarios. Baseband algorithm for GFDM are described in detail and is shown to outperform
orthogonal frequency division multiplexing (OFDM) in simulations. We also investigate dedicated MA schemesthat can be used with GFDM. There, it is shown that frequency-division multiple access (FDMA) and CSMA/CA
directly work out-of-the-box. Additionally, space-time-coding for GFDM is described in order to improve therobustness against mobility fading.
As a non-orthogonal MA scheme, interleaver-division multiple access (IDMA) is described in detail. It is shownthat IDMA can reach higher spectral efficiency than conventional orthogonal MA schemes such as FDMA ortime-division multiple access (TDMA). However, this comes at the price of increased computational complexity
when applying the multi-user detection (MUD) algorithm for IDMA. Furthermore, initial considerations on how
GFDM can be combined with IDMA to benefit from both approaches are described.
Finally, we study multi-relay coordinated multiple-input multiple-output (MIMO) beamforming as a mean to reduce power when relays are serving several users. A reduced complexity algorithm for the computation of beamforming vectors is also provided in this document. Our solution outperforms previous algorithms in terms ofcomputation convergence and calculation speed. It is shown that the proposed beamforming algorithm provides
equal beamforming vectors as previously published algorithms, however, with significantly reduced complexity.
The work in this deliverable directly contributes to the required RESCUE gains, which are increase in spectral
efficiency and capacity, reduced outage probability and transmit power reduction. With the adoption of nonorthogonal waveforms, we can increase the point-to-point spectral efficiency. Non-orthogonal multiple accessincreases overall system capacity and spectral efficiency compared to conventional orthogonal multiple access
schemes. With the application of transmit diversity per link, the transmit power and outage probability is reduced.
Further, by application of coordinated beamforming, both spatial diversity is exploited and outage probability is
reduced. This deliverable only describes intermediate research results regarding these topics. Final evaluation is
to be carried out in RESCUE D1.3.

Page 2 (49)RESCUE

D1.4, Version 1.0

Authors
PartnerName

Phone/Fax/e-mailTechnical University Dresden(TUD)

Maximilian MatthéPhone: +49 351 463 41071
Fax: –
EMail: maximilian.matthe@ifn.et.tu-dresden.de

University of Surrey (UNIS)

Jiancao Hou
Na Yi

EMail: jiancao.hou@surrey.ac.uk
EMail: n.yi@surrey.ac.uk

Japan Advanced Institute of
Science and Technology(JAIST)

Meng Cheng
Matsumoto Tadashi

EMail: chengmeng@jaist.ac.jp
EMail: matumoto@jaist.ac.jp

Page 3 (49)RESCUE

D1.4, Version 1.0

Table of Contents
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

List of Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

1.1
1.2
1.3

Requirements on Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Contribution of this Deliverable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

788

2. Nonorthogonal Multiple Access Schemes for RESCUE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1

2.2

2.3

Generalized Frequency Division Multiple Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1 GDFM system description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2 Synchronization for GFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.3 Channel Estimation for GFDM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.3.1 Estimation of Time and Frequency Misalignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.3.2 Compensation of Time and Frequency Misalignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.3.3 Estimation and Compensation of Constant Phase Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.4 Transmit Diversity Techniques with GFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.5 Multiple Access with GFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.5.1 Characterization of inter-user-interference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.5.2 Considered Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.5.3 Performance Evaluation of inter-user-interference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.6 Performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interleaved Division Multiple Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Introduction to IDMA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Detection Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2.1 Single-user Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2.2 Multi-user Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3 MAC Rate Region Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.4 Asynchronous IDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.5 Initial Measurement Data-based Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.6 Measurement Data-based Simulations and Evaluations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Combination of GFDM and IDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11
11
13
15
17
18
18
18
19
20
20
21
22
25
25
27
28
29
33
35
36
37
39

3. Reduced-Complexity Multi-Relay Coordinated MIMO Beamforming Technique 40
3.1
3.2
3.3

3.4

Motivation and Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Reduced-complexity Bi-Layer Iterative Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.1 Bi-layer Iterative Algorithm Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.2 Computational Complexity and Convergence Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

40
40
41
41
43
44

4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Page 4 (49)RESCUE

D1.4, Version 1.0

List of Acronyms and Abbreviations
Term
5G
ACK
AWGN
BER
BFDM
BICM-ID
BS
CA
CDMA
CFO
CHADE
CP
CSI
CSMA/CA
CSMA
CS
CTS
DACC
DFT
E2E
EBSA
EM
ESE
EXIT
FBMC
FDMA
FEC
FSCs
FSC
FT
GFDMA
GFDM
ICI
IDMA
ISI
IUI
LOS
LOTF
LTE
M2M
MAC
MAI
MA
MF
MIMO
MI
MM
MRC
MSE
MUD
NEF
NLOS

Description
5th generation
Acknowledgement
Additive white Gaussian noise
Bit error rate
Bi-orthogonal frequency division multiplexing
Bit-interleaved coded modulation with iterative detection
Base station
Collision avoidanceCode-division multiple access
Carrier frequency offset
Chained detection
Cyclic prefix
Channel state information
Carrier sense multiple access with collision avoidance
Carrier sense multiple access
Cyclic suffix
Clear-to-send
Doped accumulator
Discrete Fourier transform
End-to-end
EXIT-constrained binary switching algorithm
Extended mapping
Elementary signal estimator
Extrinisic information transfer chart
Filterbank multicarrier
Frequency-division multiple access
Forward error correction
Frequency-selective channels
Frequency-selective channel
Fourier transform
Generalized frequency division multiple access
Generalized frequency division multiplexing
Inter-carrier interference
Interleaver-division multiple accessInter-symbol interference
Inter-user-interferenceLine-of-sight
Links-On-The-Fly
Long Term Evolution
Machine-to-machine
Medium access control
Multiple access interference
Multiple access
Matched filter
Multiple-input multiple-output
Mutual information
Modulation mixing
Maximum ratio combiner
Mean squared error
Multi-user detection
Noise enhancement factor
Non line-of-sightPage 5 (49)RESCUE

NOMA
OFDM
OOB
PHY
PN
PTT
QAM
RC
RF
RRC
RSs
RS
RTS
SC-FDE
SDP
SER
SINR
SISO
SLNR
SNR
SPC-IrR
SPC
SSIC
STC
STO
SUD
TDMA
TR-STC
UCA
UFMC
ULA
V2V
WLAN
ZF

D1.4, Version 1.0

Non-orthogonal multiple access
Orthogonal frequency division multiplexing
Out-of-band
Physical layer
Pseudo-noise
Push-to-talk
Quadrature amplitude modulation
Raised cosine
Radio frequency
Root raised cosine
Relay stations
Relay station
Request-to-sendSingle-carrier frequency domain equalization
Semidefinite programs
Symbol error rate
Signal-to-interference-and-noise-ratio
Single-input single-output
Signal to leakage plus noise ratio
Signal to noise ratio
Single parity check and irregular repetition
Single-parity check
Soft successive interference cancellation
Space-time coding
Symbol timing offset
Single-user detection
Time-division multiple accessTime-reversal space-time code
Uniform circular array
Universal filtered multicarrier
Uniform linear array
Vehicle-to-vehicle
Wireless local area network
Zero-forcing

Page 6 (49)RESCUE1.

D1.4, Version 1.0

Introduction

The focus of research in RESCUE lies in the development of robust technologies for data transmission over networks in an unpredictable environment. There, the concept of Links-On-The-Fly (LOTF) aims at increasing network capacity and reducing outage probability by letting relay nodes forward frames even if they cannot decode itcorrectly. At the destination, the receiver needs to cope with decoding several, possibly erroneous, copies of thesame message and to estimate the original, error-free message.
Another important aspect in designing a robust end-to-end (E2E) data transmission over wireless networks is
how nodes communicate with each other in the physical layer (PHY) in general and, in particular, how coded
bits are transmitted from one node to another. In principle, this can be built on top of the PHY of Long Term
Evolution (LTE) or wireless local area network (WLAN). However, consideration of waveforms addressing thespecific requirements of RESCUE application scenarios is an important aspect.
Furthermore, with the LOTF technology, messages are expected to be transmitted by several copies from several
relays to the destination, increasing the load on the medium access control (MAC) layer to allocate resources foreach transmission. Therefore, the investigation of advanced multiple access (MA) schemes providing an efficient
distribution of radio frequency (RF) resources to different nodes is another important aspect of the research in
RESCUE.

1.1

Requirements on Physical Layer

In a previous research step, RESCUE applications scenarios have been developed [D11]. There, a public safety anda vehicle-to-vehicle (V2V) use case have been defined and functional requirements of the PHY were established.
In summary, the following features of a new PHY are required for the RESCUE scenarios:
Robustness against asynchronicity The constantly changing topology within the RESCUE scenarios cannot ensure a synchronous operation of all nodes within the ad-hoc network. Therefore, frequency and timing misalignments are likely to appear between several nodes. Furthermore, source and destination can have a constantdifference in frequency, i.e. carrier frequency offset (CFO) occurs. A robust PHY is required to cope with these
impairments in an efficient way.
Simultaneous access of users The transmission of several, copies of a message over multiple routes strongly
increases the load on the network. Since erroneous messages are not necessarily dropped, network load is evenfurther increased with the LOTF concept. Hence it is strongly required that the RESCUE MA protocol allows
several nodes to simultaneously transmit on a shared time-frequency resource in order to not increase transmit
latency.
Low latency transmission In RESCUE scenarios there exist several timing-critical messages that need to be delivered within a certain delay. For example, in the V2V use case, messages for the electronic break light or accident
alarms need to be delivered as fast as possible. Also, in public safety use cases, latency can become an issue, e.g.
in the push-to-talk (PTT) communication case. Therefore, both the RESCUE PHY waveform as well as the MA
technique need to consider latency as a performance metric.
Spectral efficiency Spectral efficiency is a main point for any wireless transceiver scheme as a low spectral efficiency increases delays and network load or required bandwidth. RESCUE is therefore also considering spectral
efficiency as a performance indicator.
Robustness against fading and dispersive wireless channels Especially with the non-stable changing network
topology and node mobility, channel fading becomes an issue for the PHY waveform. Severe multipath effects
are expected in urban areas and with high mobility, wireless channels can become fast faded and even frequencydispersive as is shown in [AI07]. A suitable transceiver scheme for RESCUE needs to be robust against these types
of fading and provide means to equalize channel effects. For example, multi-antenna techniques and distributed
coding provide means for exploiting channel diversity to increase the robustness against dispersive channels.
Flexibility to adapt to channel quality Within RESCUE, a rate adaptation protocol will be developed. Hence, aPage 7 (49)RESCUE

D1.4, Version 1.0

suitable RESCUE PHY needs to be able to adapt the modulation and coding schemes to the quality of the channel.
This can be achieved by either changing modulation or the code rate and needs to be supported by the waveformand coding algorithms. Another considerable aspect is, if the PHY can adapt itself to different channel conditions
by changing physical parameters such as pulse shaping filters, bandwidth or subcarrier distance.
It is notable, that the presented requirements overlap with the requirements proposed for the PHY of 5th generation (5G) networks [WJK+14; FA14; Fet14]. In particular, spectral efficiency, low latency and imperfect synchronization are important aspects in the development of 5G PHY. As in 5G networks, machine-to-machine (M2M)
traffic will be significantly increased [DGK+13], random access and the possibility to serve a massive number ofusers is a key point for multiple access techniques in 5G PHY. In the process of research, strict orthogonality ofunderlying waveforms is critically questioned, and the latest research considers non-orthogonal waveforms andmultiple access schemes to cope with the requirements of future networks [WJK+14].

1.2

Contribution of this Deliverable

The goal of this deliverable is the reporting of current state of reasearch on MA and multi-antenna techniques
within RESCUE. Since the research is still ongoing, according to the project proposal only intermediate results areshown.
Within research for 5G networks, several new waveforms are explored [WJK+14]. In particular, filterbank multicarrier (FBMC) [IVS+10], universal filtered multicarrier (UFMC) [WSC14], bi-orthogonal frequency divisionmultiplexing (BFDM) [KWJ+14] and generalized frequency division multiplexing (GFDM) are promising candidates that are being researched in the EU project 5GNOW [WKB+12]. Within RESCUE it is examined, how
GFDM can cope with the specified requirements. Furthermore, we investigate how GFDM can be extended by amultiple access technique and how this fits into the RESCUE system structure. The description of GFDM and its
possibilities for adaptation of multiple access techniques is provided in Sec. 2.1 of this document.
Another contribution of this deliverable resides in the investigation of the compatibility of interleaver-divisionmultiple access (IDMA) with RESCUE scenarios. We consider IDMA to be a promising technique due to its
ability of coping with severe user asynchronicity and its increased spetral efficiency compared to conventional MA
schemes. Furthermore, with IDMA no scheduling is necessary since every user is only identified with its usedinterleaver and each user can send a data block at any time. By using extended mapping and multi-user detection,
IDMA can achieve performance very close to the Shannon limit of a wireless channel. The details of IDMA are
described in Sec. 2.2 of this deliverable.
The distribution of transmit powers for coordinated beamforming in a multi-relay multi-user scenario as it appears
in RESCUE requires to solve the max-min signal-to-interference-and-noise-ratio (SINR) problem. Even thoughalgorithms for this problem exist, they are computationally complex due to their slow convergence within iterations.
This deliverable contributes a modified algorithm for the max-min SINR problem that converges significantly
faster and hence can reduce the power consumption and delays in the relay network. The detailed motivation andalgorithm description is contributed in Sec. 3.
Ongoing research in RESCUE WP3 has decided to use carrier sense multiple access with collision avoidance(CSMA/CA) and time-division multiple access (TDMA) as the multiple access technique used for implementation
to demonstrate the LOTF concept. Therefore, the general principle of carrier sense multiple access (CSMA) is
shortly described in Sec. 1.3. Additionally, Sec. 2.1 describes how CSMA can be combined with GFDM. But
also non-orthogonal multiple access schemes such as IDMA have been shown to provide a higher sum rate andhence higher spectral efficiency in a multi-user environment. Therefore, Sec. 2.3 describes initial considerations on
the combination of the non-orthogonal waveform GFDM with the non-orthogonal MA scheme IDMA. Potential
benefits and problems are identified and an initial system structure is proposed.

1.3

Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA)A MA technique is required for any system involving several nodes that share one physical medium. The MA
protocol defines, which node uses which resource on the medium at a particular time. Its task is to distribute thePage 8 (49)RESCUE

D1.4, Version 1.0
Start

Start

Prepareframe

Prepareframe

Ischannel
No
idle?

Is
channelidle? No

Yes

Yes

Transmitframe

Transmit
frame

ACK
received? No

Wait forrandombackoff time

Doublebackoff
range

Yes
End

End

(a) CSMA(b) CSMA/CA

Figure 1.1: Algorithm flow for CSMA and CSMA/CA.
resources so that every node can transmit and receive the data it needs. Furthermore, achieving fairness in theaccess to the shared resources is an essential role of access techniques. In literature several MA techniques exist[TM00], where non-orthogonal multiple access (NOMA) techniques [DYF+14] are very promising for unreliable
and unpredictable environments. NOMA schemes that are researched for RESCUE are treated in detail in Chapter
2. However, these schemes tend to be very complex regarding prototyping efforts and hence, they are only examined in simulations and theoretic considerations. The prototype of the RESCUE project will use a MA technique
named CSMA/CA [HHS+09]. The fundamental properties of CSMA/CA scheme are described in the following,whereas the required modifications specific for RESCUE scenarios are treated in detail in RESCUE Deliverable
D3.1.
As the RESCUE system can be both centralized and decentralized, in general the MA technique cannot rely
on a central scheduler that distributes transmission resources to the nodes. With the packet-switching MA scheme
CSMA/CA, every node can access the physical channel autonomously without requiring information from a central
control unit. The CSMA technique can be summarized by the simple “listen-before-talk” principle, but is explainedin more detail below.
Simplified algorithm flow diagrams for CSMA and CSMA/CA are shown in Fig. 1.1. In conventional CSMA,
before using the physical channel, each node senses the channel if it is idle and currently not used. Hence, thetransmitter requires feedback from the receiver of the node to determine if currently a transmission from anothernode is occuring on the channel. In case the channel appears unused, the node immediately transmits its data. In
case the channel appears to be used, the node waits until the channel becomes idle and then immediately transmits
its frame.
Several drawbacks arise with this approach. First, in pure CSMA, no collisions can be detected. It is assumed,

Page 9 (49)RESCUE

D1.4, Version 1.0

S1

S2

S1

S2

D
S2
S1

Figure 1.2: Hidden node problem with CSMA.
that collisions do not occur since the medium is sensed to be idle before transmission. However, in wirelessenvironments, the propagation delay between nodes renders this assumption incorrect. Additionally, all waiting
nodes start transmission as soon as the medium gets free, making collisions very likely to happen in highly loaded
scenarios. Another drawback comes with the hidden node problem in wireless environments (Fig. 1.2). Bothsources S1 and S2 want to transmit data to the destination D. However, they cannot hear each other and hence
assume the channel at the destination is free. Consequently, a collision happens at the destination and both sources
need to retransmit their data, since the destination cannot provide an acknowledgement (ACK).
CSMA/CA addresses the mentioned drawbacks by adding an acknowledgement protocol to detect, if a collisionat the receiver occured and introducing a delay after a channel use has been recognized. The node only again
tries to access the channel after this delay has passed. This way, as soon as the channel becomes idle, not every
node transmits at the same time, but the one with the smallest delay starts, hindering the others to use this slot.
Hence, collisions can be significantly avoided [DDT09]. Furthermore, the hidden-node problem is addressed in
CSMA/CA by the introduction of request-to-send (RTS) and clear-to-send (CTS) messages.
In case a collision appears in CSMA/CA which is detected by a missing ACK, nodes wait for a random time within
their wait backoff. If multiple collisions for the same frame appear, the backoff is increased exponentially witheach collision. This way, packet bursts are dilated in time and recurring collisions are avoided. The necessary
extensions to CSMA/CA for RESCUE scenarios are discussed within RESCUE Deliverable D3.1.

Page 10 (49)RESCUE2.

D1.4, Version 1.0

Nonorthogonal Multiple Access Schemes for RESCUEIn this section first generalized frequency division multiplexing (GFDM) is introduced and it is described, how
it can be combined with a MA scheme that is suitable for RESCUE scenarios. Furthermore, interleaver-divisionmultiple access (IDMA) is discussed and presented as a promising candidate for non-orthogonal multiple access(NOMA) with increased spectral efficiency. Finally, it is considered how GFDM and IDMA can be combined tobenefit from the advantages of both systems.

2.1

Generalized Frequency Division Multiple AccessThis section describes GFDM as a waveform which is suitable for the PHY of systems operating in RESCUE
scenarios. Initially, the transceiver is introduced and core concepts like channel estimation, synchronization, andmulti-antenna techniques for GFDM are described. MA techniques are described that can be combined with
GFDM to serve several users on the same wireless channel, leading to generalized frequency division multiple
access (GFDMA). Finally, system performance is analyzed theoretically and by simulations.
2.1.1

GDFM system description

The block diagram of the GFDM transceiver for a single user is provided in Fig. 2.1. The binary data vector ~b is~ whichencoded by a generic encoder to obtain ~bc and further mapped to complex-valued constellation symbols d,
can for example be taken from a 2µ quadrature amplitude modulation (QAM) constellation, where µ is the numberof bits per constellation point. d~ contains N = KM elements, which are distributed onto M subsymbols, where
each subsymbol transmits K different subcarriers according to

D = d~0

d~1. . . d~M−1

d~m = d0,m

and

d1,m

. . . dK−1,m

T

.

(2.1)

D represents d~ distributed onto K subcarriers and M subsymbols, d~m contains the data symbols that are to betransmitted in the mth subsymbol and dk,m describes the constellation symbol transmitted on the kth subcarrier andmth subsymbol. Note that d~ = vec(D), where vec(X) denotes the stacking of the columns of the matrix X on topof each other.
Fig. 2.2 illustrates the details of the GFDM modulator. A prototype pulse g[n] is circularly shifted in time andfrequency to the kth subcarrier and mth subsymbol, yielding gk,m [n] given by




k
gk,m [n] = g (n − mK) mod N · exp − j2π n ,
K

(2.2)

with n denoting the sampling index. gk,m [n] is then applied to transmit the constellation symbol dk,m . Finally, thecomplete transmit signal x[n] of a GFDM block is given by the summation over all transmitted symbols
K−1 M−1x[n] = ∑ ∑ gk,m [n]dk,m ,k=0 m=0n = 0, . . . , N − 1.

(2.3)

The linear equation (2.3) can be reformulated to a matrix equation~~x = Ad,
where A is a KM × KM transmitter matrix [MKL+12] with a structure according toA = ~g0,0 . . . ~gK−1,0 ~g0,1 . . . ~gK−1,M−1

(2.4)

(2.5)

where ~gk,m = (gk,m [n])n are column vectors containing the samples of gk,m [n] for n = 0, . . . , N − 1 and ~x = (x[n])n .
After modulation, at the transmitter side a cyclic prefix of NCP samples is added to produce ~x̃.

Page 11 (49)RESCUE

D1.4, Version 1.0

binarysourcebinary
sink

~b

~
b̂

~bc

encoder~b̂cdecodermapper~x

GFDM
modulator~dˆ

demapper~H̃, w̃

Ad~~x̃

cyclicprefix

H−1

B
GFDM
demodulator~zequalizer~y~ỹ

wirelesschannelremoveprefix~ỹs

synch.

Figure 2.1: Block diagram of the GFDM transceiver [MMG+14b].
d0,0

δ [n]

g[n mod N ]

..
.

..
.

..
.

δ [n − (M − 1)K]g[n mod N ]

d1,0

δ [n]

g[n mod N ]

..
.

..
.

..
.

d0 , . . . , dN −1

serial-to-parallel

d0,M −1

d1,M −1..
.
dK−1,0..
.
dK−1,M −1

δ [n − (M − 1)K]g[n mod N ]

..
.

..
.

δ [n]

g[n mod N ]

..
.

..
.

δ [n − (M − 1)K]g[n mod N ]exp [0]..
subcarrier0.
exp [0]1exp −j2π Kn..
subcarrier
1
.

exp





1
−j2π Kn..
.

X

x[0], . . . , x[N − 1]
exp −j2π K−1nK..
subcarrier. K −1exp −j2π K−1nK

Figure 2.2: Details of the GFDM modulator [MMG+14b].
The received signal after transmission through a wireless channel is modeled byỹˆ = H̃~x̃ + ~w̃,

(2.6)

where H̃ is a band-diagonal Toeplitz matrix of size N + NCP + Nch − 1 × N + NCP expressing a convolution withthe channel impulse response ~h of length Nch and ~w̃ denotes the additive white Gaussian noise (AWGN) at thereceiver. After time and frequency synchronization have been performed at the receiver yielding the signal ~ỹs , thecyclic prefix (CP) is removed to produce the signal ~y. Assuming perfect synchronization, due to the use of the CP
this whole process can be summarized by~y = H~x + ~w,(2.7)

where H is a circulant matrix that describes a circular convolution of the transmitted signal with the channel
impulse response.
The use of a CP hence transforms the linear convolution with the channel impulse response into a circular. Since
circulant matrices are diagonalized by the Fourier transform (FT), channel equalization can be efficiently achievedin the frequeny domain as it is the case for OFDM systems [Bin90]. Hence, the equalized GFDM signal is given
by
F~w
F~y
=~x + FH
~
Fh
F~h
¯
~
~z = Ad + ~w
~z = FH

(2.8)
(2.9)

where F denotes a normalized Fourier matrix and ~h is zero-padded to the correct length. ~w¯ denotes the colorednoise after channel equalization. ~z is then sent to the GFDM demodulator which for each subcarrier performs acircular convolution with a subcarrier receiver filter and subsequent downsampling at the symbol timings. This
procedure is most simply described byd~ˆ = B~z,

(2.10)

Page 12 (49)RESCUE

D1.4, Version 1.0

d1,0
d0,0

1
T

0

(a) OFDM

0

d0,0
T t

T
N

(b) SC-FDE

d0,M −1
T t

T
M

0

(c) SC-FDM and GFDM

Figure 2.3: Time-frequency partitioning of available resources for different schemes:(a) K = N, M = 1 (b) K = 1, M = N (c) K = 4, M = 3 [MMG+14b]
where B contains the subcarrier filters as its rows. There are standard receiver types for GFDM readily available inliterature: the matched filter (MF) receiver BMF = AH optimizes the signal to noise ratio (SNR) per data symbol,
however due to the non-orthogonality of transmitter filters it introduces self-interference between the data. On
the other hand the zero-forcing (ZF) receiver BZF = A−1 combats the self-interference by using bi-orthogonal
filters at the receiver, however at the cost of increased noise enhancement. It was shown in [MMF14a] that certain
configurations of K and M lead to singular transmitter matrices A for which no ZF receiver exists and MF receivers
perform worse with these configurations. In particular it was shown that odd M are advantageous for the GFDM
system performance. After GFDM demodulation, the estimated constellation symbols d~ˆ are demapped to bits ~b̂cwhich can be passed to a subsequent generic decoder to finally yield an estimate of the transmitted bits ~b̂.
GFDM can be considered as a generalization of both orthogonal frequency division multiplexing (OFDM) andsingle-carrier frequency domain equalization (SC-FDE) systems. Fig. 2.3 shows the relation between these threetransceiver schemes in terms of data distribution in time and frequency. OFDM distributes its data only in thefrequency domain whereas SC-FDE performs a time-spreading of the transmitted data. With GFDM, a moregeneric solution is proposed where data can be distributed in both time and frequency domains. GFDM reduces
to OFDM for M = 1 and g[n] = √1K and to SC-FDE when K = 1 and g[n] is the Dirichlet filter [MMG+14a].
This amplifies the great flexibility of the proposed GFDM scheme which can adapt its grid to different channel
conditions and furthermore can adapt the employed filter to different requirements.
2.1.2

Synchronization for GFDM

Since in RESCUE scenarios strict synchronization and synchronicity between users cannot be afforded or achieved
it is very important that the receiver of a GFDM block is able to estimate the symbol timing offset (STO) and CFOof the transmitter relative to the receiver. In this section we propose a simple synchronization method which isbased on a specific GFDM preamble.
We focus on single-shot synchronization, meaning that data and synchronization information are transmitted altogether, allowing two devices to directly communicate with each other without exchanging synchronization information before data transmission. For this purpose, the algorithm proposed in [AKE08] will be evaluated andadapted for GFDM. The preamble consists of 2 GFDM subsymbols, where dk,0 and dk,1 are filled with the same
pseudo-noise (PN) sequence, resulting in a signal composed with two identical halves.
To achieve very low out-of-band (OOB) radiation, both the preamble and the data block are multiplied by a window
which pinches the block boundaries. This is illustrated in Fig. 2.4, where different pinching lengths NWp and NWd
can be applied to preamble and data block which influences the emission mask.

Page 13 (49)

K subcarriers

M samples

N samples

M
T

1
T

T t

T
N

dK−1,0d0,0d0,1

N subcarriersdN −1,0f
B

N subsymbolsfB

d0,N −1

N samplesf
B

M subsymbols
K samples

RESCUE

D1.4, Version 1.0
NWp

NWd

preambledata block

p[n]
CP

dk,0

x[n]
dk,1

CP

dk,0

dk,1

dk,M −1

Figure 2.4: Windowed GFDM preamble preceding a windowed GFDM data block [MMG+14b].

V ar[n̂f ]

100
10−1
10−2

V ar[ˆ
]

10−3
10−4
10−5

5

10

15
Eb /N020

25

Figure 2.5: Simulated estimation performance of fine STO n̂f and CFO ε̂ for the GFDM preamble in a fading
multipath channel [MMG+14b].
Let r[n] be a set of received samples containing at least one complete GFDM preamble. Now, the two identicalhalves of the preamble are found by an autocorrelation metric. In order to remove the plateau which results from the
CP and cyclic suffix (CS) usage, the metric is integrated with a moving window average filter [SC97], [MZB00],
leading to the metricNµ[n] = ∑ ∑ r[n + ι + k] r n + ι + k +.
2
ι=−NCP k=0
0

N−1

∗

(2.11)

A coarse STO estimate n̂c is now given as the argument that maximizes |µ[n]|:n̂c = argmax|µ[n]|,(2.12)nand a CFO estimate ε̂ is acquired from the angle of µ[n̂c ]:ε̂ =arg µ[n̂c ].
π(2.13)

Subsequently, the CFO is corrected with the help of ε̂ and an auxiliary cross correlation metric c[n] is calculated
by
ε̂
1 N−1
c[n] =
r[n + k]∗ e j2π N (n+k) p[k],(2.14)∑
N k=0
where p[k] represents the known GFDM preamble. Side peaks that arise in c[n] due to the CP and CS of thepreamble are compensated by jointly considering c[n] and µ[n] to provide a more accurate STO estimate given byn̂o = argmax (|c[n]||µ[n]|) .

(2.15)

nFig. 2.5 presents the estimation performance of normalized STO and CFO with a GFDM preamble that follows theparameters from Tab. 2.3, except for M = 2. A raised cosine (RC) window function with rollof length 16 is used

Page 14 (49)RESCUE

D1.4, Version 1.0

and the time-variant frequency-selective channel (FSC) is set as described in Sec. 2.1.4. The figure shows that the
STO estimation becomes stable within tenths of a sample in higher SNR ranges above 5 dB. CFO can be estimatedwith an accuracy between 10−3 and 10−5 depending on the SNR.
The applied double-pinching configuration for the GFDM preamble shows that single-shot burst synchronization
can be achieved without performance degradation compared to the results in [AKE08]. Furthermore, with thepresented approach, OOB radiation is kept low by the application of the windowing above the GFDM block
and preamble, which is an important aspect in particular in unpredictable and asynchronous operation of several
users.
2.1.3

Channel Estimation for GFDM

The previous section has shown a technique to synchronize user and access point with an extra GFDM preamble.
Within this section, time and frequency misalignment is considered as a part of a time-variant channel and cantherefore be equalized by estimating the channel with a piloting structure [MMF15]. The presented approach isable to compensate the residual timing and frequency misalignment that results from the synchronization approachpresented earlier.
We assume a system with the parameters from Tab. 2.3 where only half of the subcarriers are allocated with auser and the access point has a constant time misalignment ∆τ and a frequency misalignment ∆φ . In order to beable to compensate negative as well as positive timing offsets, the receiver window is located in the middle of theestimated CP of the received signal and the contained signal is rotated back by half of the CP length to obtain theoriginal signal, which is denoted by the operation T NNCP . Hence, assuming that |∆τ| < N2CP , the signal y[n] sent to2

the GFDM demodulator is given by

where

y[n] = T∆τN F∆φ x[n]= δ [n − ∆τ] ~ exp( j2π( ∆φK n + φ0 ))x[n] ,Tτ x[n] = δ [n − τ] ∗ x[n],(2.16)(2.17)TτN x[n] = δ [n − τ] ~ x[n](2.18)

(2.19)

andFφ x[n] = exp( j2πφn
N + φ0 )· x[n]

cause a shift in time and frequency where φ0 is an arbitrary constant phase offset. TτN causes a circular time shiftof τ samples with respect to the block length N. Accordingly, the kth subcarrier and mth subsymbol is detectedbydˆk,m = gRx
k,m , y[n]N
= dk,m gRx
k,m [n], T∆τ F∆φ gk,m [n](2.20)+ Ik,m ,(2.21)

where

Ik,m =∑0 0N
dk0 ,m0 gRxk,m , T∆τ F∆φ gk0 m0 + n(2.22)k ,m(k0 ,m0 )6=(k,m)

denotes interference coming from adjacent subcarriers and subsymbols. gRx
k,m [n] is the receiver filter shifted to thespecified location in time and frequency according to (2.2).

Page 15 (49)RESCUE

D1.4, Version 1.0mg0m [n]1arg{F∆φ [n]}2πF{gk0 }[η]

p0

0.5
0
0
6
4
2
0

0

100

100

200

200

1

300

300

400

400

500
n

600

500
n

600

F{g16,0 }[η]

0.5

700

800

p1
p2

900

3
2
1
0

100

200

300

400

p3
p4

700

800

p9

600

p8

500
η

600

700

800

p10
p13

900

100

200

300

400

p12
p15

p16
0

p7

p11

F{g32,0 }[η]

500
η

p5
p6

900

0
0

N
arg{F{T∆φ}[η]}
2πg0,13 [n]g0,1 [n]

700

800

p14
p17

900

k

(a) Phase shift illustration for time and frequency misalignment. M = 15, K = 64, ∆τ = 1, ∆φ = 0.1(b) Proposed piloting scheme for GFDM resouce grid.

Figure 2.6: Illustration of phase shift and piloting scheme to estimate the phase shift [MMF15].
First, let only a frequency offset appear between a user and the access point, i.e. ∆τ = 0. Looking at Fig. 2.6a wefind that both gRx
k,m [n] and gk,m [n] are well localized in time, where the concentration is around n = mK. Eq. (2.19)
shows, that a frequency shift actually is a multiplication of the transmit signal with a slowly rotating complex
exponential. We assume the phase rotation due to this multiplication to be constant in the contributing region of
gk,m [n] and an approximation of the phase error φm of the mth subsymbol is given byarg dˆk,m = arg dk,m + 2π( ∆φKmK + φ0 ).

(2.23)

Obviously, only the subsymbol index determines the phase error due to a frequency offset. In the frequency domain
a similar chain of argumentation can be provided when no frequency offset appears but a timing offset ∆τ 6= 0.
Expressing the receiver operation in the frequency domain the received data symbol is given by∆τηRxdˆk,m = F−1
N {Gk,0 [η]Gk,0 [η] exp( j2π N }|n=mK ,(2.24)

where FN {·} denotes N-point discrete Fourier transform (DFT) and Gk,m [η] = FN {gk,m [n]}. Due to the goodfrequency localization of the transmitter filter Gk0 [η], it is considered to be concentrated arount η = kM. By asimilar argument as for the frequency offset, the phase error of the received symbols can be approximated byarg dˆk,m = arg dk,m + 2π ∆τkM
N

(2.25)

for a time offset. Dual to the case of frequency-offset, the subcarrier index determines phase error when a timeoffset appears.

Page 16 (49)RESCUE
2.1.3.1

D1.4, Version 1.0
Estimation of Time and Frequency Misalignment

Naturally, when misalignments in both time and frequency occur, the phase rotation depends on both the subcarrierand subsymbol index.
Let
φ00 = 2πφ0 .

φt = 2π ∆φNK ,φ f = 2π ∆τM
N ,(2.26)

Then, the system model used for estimation of ∆φ and ∆τ can be given byarg dˆk,m = arg dk,m + mφt + kφ f + φ00 .

(2.27)

To estimate the misalignment, we introduce a piloting scheme according to Fig. 2.6b. The position of the pilots intime (µ(i)) and frequency (κ(i)) is given by
κ(i) = b 2i cµ(i) = 1 +i + (i mod 2) · M−12
mod (M − 1) .

(2.28)(2.29)

Using the system model (2.27) the following equation system can be stated that describes the expected phases ofpilots at the receiver:arg p̂i = arg pi + µ(i)φt + κ(i)φ f + φ00 .

(2.30)

Depending on the misalignment and subcarrier and subsymbol index, arg pi can grow larger than 2π which makes
the phase rotation ambiguous. This effect is combated by not considering the absolute offset but the phase difference between adjacent pilots given byφ̂i j = arg p̂i − arg p̂ j
 

 φt
= µ(i) − µ( j) κ(i) − κ( j)
.
|
{z
} φf

(2.31)(2.32)vi j

For the present investigation we assume arg pi − arg p j = 0, i.e. all pilots carry the same phase information. If
the estimation performance can be increased by considering different phase distributions needs to investigated insubsequent works. Now, let V be a matrix that contains all vi j where the absolute value of the elements of vi j isless or equal to M−1
2 . This way, only phase differences between adjacent pilots are considered. Further, let P be avector containing φ̂i j of the selected elements in V. Then,

P=V

 
φt
.
φf

(2.33)

Calculation of a least-squares solution φ̂t= (VH V)−1 VH P.
φ̂ f(2.34)

provides an estimation of the phase rotations φt and φ f per subsymbol and subcarrier, respectively.

Page 17 (49)RESCUE
2.1.3.2

D1.4, Version 1.0
Compensation of Time and Frequency Misalignment

Two possibilities arise to compensate the time and frequency misalignment: compensation in data domain andsignal domain. The compensation in the data domain is simply achieved by rotating back the demodulated data
symbols dˆk,m according to the estimated phase rotation per subcarrier and subsymbols, yielding the compensated0
symbols dˆk,mgiven by0dˆk,m= dˆk,m · exp(− j(mφ̂t + kφ̂ f )).

(2.35)

Signal domain compensation is achieved by applying the inverse of the estimated time and frequency misalignment
to the received GFDM block y[n], yielding the signal y0 [n] given byN
y0 [n] = F−∆φ
ˆ T−∆τ
ˆ y[n],(2.36)whereˆ = N φ̂ f .
∆τ
2πM

ˆ = N φ̂t
∆φ
2πK(2.37)

Then, GFDM demodulation is again carried out, but using the compensated signal y0 [n]. Signal domain compensation outperforms data domain compensation, because time and frequency misalignment additionally leads tointer-carrier interference (ICI) and inter-symbol interference (ISI) which cannot be compensated by the data domain compensation. However, because of the repeated GFDM demodulation, signal domain compensation requires
a higher computational complexity. With the signal domain compensation, repeated iterations of compensation andestimation can be carried out to further increase the compensation performance.

2.1.3.3

Estimation and Compensation of Constant Phase Error

Once the time and frequency misalignment is compensated, only a constant phase error is left, which is compen0
sated in a subsequent step. When p̂0i and dˆk,mdenote the pilots and data symbols after the offset compensation,
the common phase error is can be estimated from the mean of the measured phase difference between known andmeasured pilots:

φ̂0 = arg p̂i − arg pi .

(2.38)

0 , which leads to the final estimation
Then, constant phase error compensation is applied on the data symbols dk,m00
dˆk,m , given by
00
0
dˆk,m
= dˆk,m· exp(− jφ̂0 ).

2.1.4(2.39)

Transmit Diversity Techniques with GFDM

Especially in unpredictable environments as they appear in RESCUE scenarios, it is very important to keep available links as robust as possible. The application of multi-antenna techniques which exploit spatial diversity tocombat severe fading of the distinct channels between the seperate antennas can significantly help at most basic
level of the PHY layer. This section describes a possibility on how space-time coding (STC) [Ala98] can be applied
to GFDM to increase the robustness in a fading multipath environment by exploiting transmit diversity.
The key challenge for the application of STC to GFDM is its inherent non-orthogonality between subsymbols andsubcarriers which impedes the simple approach that can be used for OFDM [LW00]. When using a linear equalizer,
severe ISI is included within the estimated symbols, as shown in [MMF14b]. However, the STC can be applied toPage 18 (49)RESCUE

D1.4, Version 1.0

AB~
x11 , ~x12CP
d~1 , d~2 GFDM ~x1 , ~x2 space-time
encoder ~x21 , ~x22
mod.
CP

..
.

removeprefix.. space-time. combiner/

synch.

/

2I

~ˆ ~ˆ
GFDM d1 , d2
demod.

2I

channelestimator

Figure 2.7: Block diagram for the proposed STC-GFDM transceiver [MMG+14b].
GFDM at a block-level instead of a symbol-level by the application of time-reversal space-time code (TR-STC).
TR-STC was initially developed to provide transmit-diversity for single-carrier transmission in multipath fading
environments [AlD01]. The application of TR-STC to GFDM is presented in the block diagram in Fig. 2.7. For
TR-STC GFDM, two subsequent GFDM blocks d~1 and d~2 are independently modulated leading to~x1 = Ad~1 and ~x2 = Ad~2 .

(2.40)

Then, the two subsequent blocks are processed by the space-time encoder which outputs the signals to transmit to
the antenna which are given by the transformation in Tab. 2.1.
Table 2.1: Space-Time encoded GFDM signals.
Time frame 1
Time frame 2

Antenna 1

Antenna 2

x11 [n] = x1 [n]x12 [n] = x2 [n]x21 [n] = −x2∗ [−n mod N]
x22 [n] = x1∗ [−n mod N]

Hence, the space-time encoder produces a time-reversed and conjugated version for the second antenna. Then, a
CP is prepended to each block before signal transmission. Then, the signal of block 1 and 2 received at the ith
receiving antenna after being sent through two independent wireless channels is given by~yi1 = H1i~x11 + H2i~x21 + ~wi1.
~yi2 = H1i~x12 + H2i~x22 + ~wi2 ,(2.41)

There, H ji denotes the circulant channel matrix that represents the circular convolution of the signal with thechannel between the jth transmitting and ith receiving antenna. ~wi1 and ~wi2 denote the noise vectors on the
ith receiving antenna collected in the first and second frame slot, respectively. At the receiver, the space-time
combining including a maximum ratio combiner (MRC) is carried out in the frequency domain given by
I
I
∗~
∗~
~∗
~∗
~Xˆ1 = ∑i=1 φ1iYi1 + φ2iYi2 = ~X1 + ∑i=1 φ1iWi1 + φ2iWi2 = ~X1 + W~ eq
1∑Ii=1 φ1i∗ φ1i + φ2i∗ φ2i∑Ii=1 φ1i∗ φ1i + φ2i∗ φ2i
I
I
∗~
∗~
~∗
~∗
~Xˆ2 = ∑i=1 φ1iYi2 − φ2iYi1 = ~X2 + ∑i=1 φ1iWi2 − φ2iWi1 = ~X2 + W
~ eq
2
I
I
∗
∗
∗
∗
∑i=1 φ1i φ1i + φ2i φ2i∑i=1 φ1i φ1i + φ2i φ2i(2.42)(2.43)

Here, I is the number of receiving antennas, φ ji = FH ji F−1 and ~Yi1 and ~Yi2 are the DFT of ~yi1 and ~yi2 , respectively.
Finally, the GFDM demodulation is carried out on the time-domain signal given byˆd~ j = BF−1~Xˆ j .
2.1.5(2.44)

Multiple Access with GFDM

This section deals with the application of MA schemes to GFDM, leading to GFDMA. In RESCUE, perfectsynchronization cannot be achieved and hence the MA scheme must be robust against timing and frequency misalignment between users. In particular, in this section we focus on the application of frequency-division multiple

Page 19 (49)RESCUE

D1.4, Version 1.0

↔φ

CP
CP
CP

↔τ

t=0
CP
CP
CP

CP

CP
CP
CP

CP
f

CP

t User 1 ( Nτ = 0)CPτ
User 2 ( NCP= 0.3)
τ
User 3 ( NCP= −0.3)
τ
CP User 4 ( NCP = 0.8)

Receiver window
y 1 [n]Figure 2.8: Position of the receiver window for user 1. Other users have different timing offsets [MMF15].
access (FDMA) and CSMA/CA to GFDM [MMF15], and describe the effects when timing misalignment between
users occur. Especially, inter-user-interference (IUI) appears in non-synchronized MA that needs to be handled by
the system. Note that in Sec. 2.1.3 methods have been described to cope with time and frequency misalignment
between transmitter and receiver.

2.1.5.1

Characterization of inter-user-interference(u)

We denote the GFDM block of the uth user by xsync [n] given by(u)

xsync [n] = ∑(u)∑ dk,m gk,m [n] n = 0 . . . , N − 1.

(2.45)m∈M k∈K (u)(u)

The set of allocated subcarriers for user u is denoted by K (u) . Furthermore, adding a CP to xsync [n] yields thesignal x̃(u) [n]. Assuming each user has a time offset of τ (u) time samples and a frequency offset φ (u) frequency
samples, the transmit signal x(u) [n] is given by(u)x(u) [n] = Tτ (u) Fφ (u) x̃sync [n].

(2.46)To analyze IUI, the signal is transmitted over a MA AWGN channel. At the receiver, the signals of all users add
up to the signal y[n] given by
y[n] = ∑ x(u) [n] + w[n],(2.47)

u∈U

where, w[n] is AWGN at the receiver and U denotes the set of transmitting users. Assuming ZF receiver andperfect synchronization between transmitter and receiver, the demodulated data symbols of the uth user are given
by
D
E
(u)
(u0 )
(2.48)
dk,m = dk,m + ∑ ∑ ∑ dk0 m0 gRxk,m , Tτ (u0 ) Fφ (u0 ) gk0 m0 ,u0 ∈U \u m0 k0 ∈K

|

{z

Ik,m

}

where Ik,m denotes the interference from other users onto the (k, m)th data symbol of the user of interest.

2.1.5.2

Considered Scenario

For the investigation of IUI we consider a multi-user uplink scenario that is derived from the OFDM PHY ofIEEE 802.11a. In this scenario we compare the IUI when using OFDM and GFDM. In the scenario, the available
bandwidth is divided into 64 subcarriers, but only 52 are allocated. The allocated subcarries are separated into
13 subchannels, where each subchannel consists of 3 subcarriers. Between subchannels, one guard carrier isinserted. This scenario is illustrated in Fig. 2.9, where also the OOB radiation of GFDM and OFDM is compared.
Obviously, with GFDM a signifantly lower OOB radiation is obtained. In RESCUE, we assume that several devices
are concurrently accessing the channel. Each device can choose between the available subchannels to transmit therequired packet. Within each subchannel, the nodes can obey a CSMA/CA behaviour, which shows that GFDM

Page 20 (49)RESCUE

D1.4, Version 1.0
GFDM

OFDM
0
P (f )

P (f )

0
−20
−40
−60

−30

−20

−10

0

10

20

−20
−40
−60

30

Subcarrier index

−30

−20

−10

0

10

20

30

Subcarrier index

Figure 2.9: Subchannel division for the presented scenario. Subchannels are described by varying colors[MMF15].

Table 2.2: Parameters for GFDM and OFDM for performance evaluation.
Parameter

Symbol

GFDM

OFDM

M
M
K
NCP
g[n]

9
{1, . . . , 13}64
16RC, α = 0.1
16QAM

1
{0}
64
16
rect
16QAM

# subsymbols per block
Allocated subsymbols# available subcarriers
CP duration
FilterModulation

can work with acCSMA/CA as OFDM can. With this MA approach, several users can simultaneously accessthe channel, however, they do not overlap and can hence be easily decoupled by the destination. How to copewith collisions in CSMA/CA when using GFDM is out of scope of this Deliverable and needs to be assessed inlater studies. Due to the CSMA/CA behaviour, transmissions in several subchannels is completely asynchronous.
Within this scenario, we analyze the suitability of GFDM and OFDM for the PHY of the RESCUE system.

2.1.5.3

Performance Evaluation of inter-user-interference

We consider the GFDM and OFDM parameters shown in Tab. 2.2 for the subsequent evaluation. User 1 is continously accessing subchannel 1 and user 2 is transmitting in the 2nd subchannel with varying time and frequency
misalignment. This scenario is illustrated in Fig. 2.10. As a performance measure we consider the MSE of theconstellation points of user 1, when user 2 is transmitting with different misalignments. The MSE is given by

MSE(1) = E"

1|K (1) ||M |

∑

#
2
ˆ
∑ |dk [m] − dk [m]| .

(2.49)k∈K (1) m∈M

The resulting system performance describing the MSE of user 1 is shown in Fig. 2.11, where Fig. 2.11a and Fig.
2.11b show the interference for varying time and frequency offsets, respectively. The interference on user 1 is a
result of spectral leakage from user 2 into the subchannel of user 1. Therefore, GFDM can significantly outperform
OFDM in this scenario.
OFDM is orthogonal when perfect frequency synchronization appears (φ (2) = 0), and when the timing offset iswithin the guard interval defined by the CP. Then, the MSE remains at the noise level of -40dB. This behaviour isillustrated by user 2 and user 3 in Fig. 2.8. As soon as τ (2) grows beyond the CP length, the receiver window covers
two subsequent data blocks if the interfering user. Then, orthogonality is destroyed and the constellation MSE
significantly increases. This timing situation is described by User 4 in Fig. 2.8. Already with a small frequency
offset (φ (2) 6= 0) in OFDM, the high sidelobes of the rectangular OFDM filter strongly interfere with user 1 andthere is strong interference, even when the timing offset is within the timing offset. The interference appears forboth positive and negative frequency offset, because the sidelobes of OFDM decay very slowly. Naturally, with afrequency offset of exactly one subcarrier, OFDM becomes orthogonal again.

Page 21 (49)RESCUE

D1.4, Version 1.0
OFDM

P (f ) [dB]

GFDM
0
−20
−40

User 1

User 2

φ(2) < 0

0
−20
−40

User 1

User 2

-13

−24

−23

Subcarrier index

−22

−24

−23

Subcarrier index−22

Figure 2.10: Overlapping OFDM and GFDM spectrum. OFDM does not have zeros at the subcarrier positions due to the insertion of the CP [MMF15].
00φB = 0

OFDM

B

φ

= 0.1

φ

φB = −0.1

τB == −0.3−10MSE(A) [dB]

MSE(A) [dB]−10B

τB = 0OFDMφB = 0.3

-16

−20
GFDM
−30
-38

NCP
2

−1

τ B = 2NCP

−20

GFDM

−30
−40

−40
−30

−20

−10

0

10

20

−150

30

τ B [samples](a) Constant frequency offsets of user 2.

−100

−50

0

50

φB [% of subcarrier](b) Constant timing offsets of user 2.

Figure 2.11: Constellation MSE for different offsets of user 2 with a noise level of -40dB and -10dB[MMF15].
The behaviour with GFDM is different. When the timing misalignment stays within the guard period and φ (2) = 0
the ZF receiver obtains bi-orthogonality. Accordingly, the constellation MSE remains at the noise level. As soon as
the time offset increases beyond the guard interval, the same situation as in OFDM appears: (bi-)orthogonality islost and the spectral leakage of user 2 interferes with user 1. However, because GFDM provides significanly lower
sidelobes than OFDM, the MSE does only marginally increase. Even with a very small frequency misalignmentφ (2) 6= 0, (bi-)orthogonality is also lost in GFDM. However, the MSE again only slightly increases because of the
low sidelobes. Furthermore, in contrast to OFDM interferences only increases, when φ (2) < 0, i.e. user 2 getscloser to user 1 because the sidelobes in GFDM decay much faster than these of OFDM.
The evaluations in this section have been carried out with perfect channel state information (CSI) and known timeand frequency misalignment. In real applications, the algorithms for coarse and fine synchronization describedin Sec. 2.1.2 and Sec. 2.1.3 can be applied per user. Channel estimation for multi-user scenarios needs to beinvestigated in ongoing studies.
2.1.6

Performance analysis

This section is dedicated to summarize simulation results carried out for GFDM. First, we present the uncoded
symbol error rate (SER) of the ZF GFDM receiver in various channels. Furthermore, the performance of thechannel estimation algorithm presented in Sec. 2.1.3 is shown and the OOB radiation of GFDM is measured.
Throughout this section, the GFDM system is configured with the paremeters listed in Tab. 2.3. The channel
model we assume is derived from the WINNER model B1 “Urban micro-cell” with no LOS component [Kyö07].
The according power delay profile is given in Tab. 2.4.
In AWGN channel, compared to an orthogonal system, the noise enhancement factor (NEF) ξ determines the SNR
reduction when using the ZF receiver. It is defined as
MK−1

ξ=

2

∑ [BZF ]k,n ,(2.50)

n=0

Page 22 (49)RESCUE

D1.4, Version 1.0

Table 2.3: GFDM parameters used for simulation.
Parameter

Value

Mapping
Transmit Filter
Roll-off (α)
Number of subcarriers (K)
Number of subsymbols (M)CP length (NCP )CS length (NCS )Sampling Frequency

16-QAMRC
0.1 or 0.964916 samples
0 samples
20Mhz

Table 2.4: Considered power delay profile of the multipath channel. Adapted from [Kyö07].
Delay (ns)Tap Power (dB)

0
-1

100
2.9

250
-5.7

300
-12.9

400
-19.6

450
-19.8

500
-21.3

600
-26.4

which is equal for every k and depends on the employed receiver filter. For frequency-selective channels, the NEF
varies for each subcarrier, but in average it is equal to ξ . A more exact equation for the NEF and the ZF SER
performance in various channels is provided in [MMG+14b]. Closed-form solutions for the SER performance in
AWGN-channels with a MF receiver are available in [MMG+14a].
1) AWGN Channel: The NEF of the GFDM system shifts the equivalent SNR. Hence, the GFDM and OFDM
performance only differ by the SNR shift caused by the NEF. With the GFDM configuration from Tab. 2.3, theNEF equals ξ = 0.05 dB and hence GFDM and OFDM performance differ only marginally. Significantly more
influence has the effect of the energy spent for insertion of the CP, which happens to be disvantageous for OFDM.
The benefit in terms of spectral efficiency when comparing GFDM and OFDM is given byM(K + NCP + NCS )η = 10 log
dB.
(2.51)MK + NCP + NCS
Fig. 2.12(a) shown simulated and analytic results for the SER of GFDM and OFDM in an AWGN channel. As
expected, GFDM can reach a lower bit error rate (BER) than OFDM because of the more efficient use of the CP.
2) Frequency-selective channel: In frequency-selective channels, the same behaviour as in the AWGN case isvisible. OFDM and GFDM both achieve roughly the same performance, however GFDM again benifts from CP
usage. For the simulation in Fig. 2.12, the channel delay profile from Tab. 2.4 has been used, where a static power
delay profile was assumed.
3) Time-variant channel: To produce a time-variant channel the power delay profile from Tab. 2.4 was used again,
however each tap is multiplied by a random variable with complex normal distribution with unit variance, whichleads to a Rayleigh fading channel. It has been assumed that the channel remains constant for the transmission of
one GFDM or OFDM symbol. Hence, the instantaneous SNR and SER are both random variables and therefore theaverage SER is plotted vs the average SNR. Fig. 2.12(c) shows the simulated results comparing OFDM and GFDM.
As before in the static FSC case, GFDM outperforms OFDM due to its more efficient use of the CP. However, thedifference becomes marginal due to the wide SNR range that is considered for simulations in Rayleigh fading.
4) Space-Time Coding: Fig. 2.13a compares the SER performance of classical STC-OFDM [LW00] with thedescribed STC-GFDM system considering the system parameters from Tab. 2.3. As the channel the same fading
model as in 3) is assumed. Note that the transmission power of the system is constant, i.e. each transmit antennatransmits with half power compared to the single-input single-output (SISO) case. The figure reveals that STCGFDM and STC-OFDM present the same diversity gain, however GFDM outperforms OFDM because of theusage of the CP. There is a significant performance gain visible when applying space-time coding compared to theconventional SISO case.
Fig. 2.13b shows the performance of the proposed misalignment estimation and compensation algorithms fromPage 23 (49)RESCUE

D1.4, Version 1.0

Theo. OFDM
Theo. GFDM - RC α = 0.1

Sim. CP-OFDM
Sim. CP-GFDM- RC α = 0.1

Theo CP-OFDM
Theo. CP-GFDM - RC α = 0.1

symbol error rate

100
10−1
10−2
10−3
10−4

0

5

10
15
Es /N0 [dB]

20 0(a) AWGN51015
Es /N0 [dB]

20 0

10

(b) frequency-selective

20
30
Es /N0 [dB]40(c) time-variant

Figure 2.12: GFDM and OFDM SER performance in different channels.

0

10−1
10

MSE(d00km ) [dB]symbol error rate

100

−2

10−3
10−4

4

8

12 16 20
Es /N0 [dB]2428(a) SER performance of the 2x1 space-time coded systems
under fading FSC.

∆τ = 1∆τ = 3

Data domain
SNR=10dB

Signal domain;
3 iterations
SNR=10dB−10
Data domain
SNR=40dB−20

Signal domain;
3 iterations
SNR=40dB

Signal domain;
1 iteration
SNR=40dB−30

STC-OFDM
STC-GFDM - RC α = 0.10∆τ = 0∆τ = 2∆τ = 4−40−0.1(b) Performance[MMF15].

−0.05

of

0

0.05

0.1

∆φ

misalignment

compensation

Figure 2.13: Performance simulation for STC-GFDM and Misalignment Compensation.
Sec. 2.1.3. As a performance measure the constellation MSE of the demodulated data symbols is considered. The
figure shows the compensation performance when transmitter and receiver are offset in time by ∆τ samples and in
frequency by ∆Φ samples. The noise floor was set to -40dB and -10dB and simulations were carried out.
Obviously, the compensation in the signal domain outperforms the compensation in the data domain with one
iteration. This is due to additional ISI and ICI that is generated by misalignments and which is not consideredby compensation in the data domain. With one signal domain iteration, the timing misalignment can already becorrectly compensated. The frequency misalignment needs more iterations and hence the MSE can be lowered byseveral compensation iterations. With 3 iterations, the MSE can be lowered to the noise floor for both low and high
SNR. As soon as the frequency misalignment grows beyond 12% of the subcarrier bandwidth, there is an abrupt
performance degradation of the compensation algorithm. In this case, the phase difference between two adjacentpilots on the same subcarrier approaches π and hence the rotation becomes ambiguous and estimation performancedegrades. The performance be be increased by using different piloting structures or pilot angles within the pilot
grid.

Page 24 (49)RESCUE𝑏1,𝑖

D1.4, Version 1.0

Encoder∏1

Mapping

Encoder

∏K

𝑥1,𝑚

⋮

⋮

Mapping

𝑏 𝐾,𝑖

⋮
𝑏𝐾,𝑖

𝑏 1,𝑖

𝑥𝐾,𝑚

(a) The transmitter part.

Decoder

Decoder

∏1-1
𝑟𝑚

∏1

∏K-1

⋮

ESE

∏K

(b) The receiver part.

Figure 2.14: The system model of conventional IDMA.

2.2

Interleaved Division Multiple AccessIn disaster scenarios assumed in RESCUE project where transmission links may become lossy, the forwarding
nodes in the wireless sensor network may not be able to fully decode the original data sequences. However,with our proposed technique, the recovered data sequences, which may contain some errors, are allowed to be
forwarded to the next node. Therefore, at the final destination node, the receiver may receive signals via manydifferent pathes in order to enhance the detection of the original information sequence. Signals with regard to one
information sequence will probably arrive at the same time, which can be formulated as a multiple access channel.
In this section, IDMA is selected as our multiple access scheme, and its principles and performances are presented
in the following discussion.
2.2.1

Introduction to IDMA

Nowadays, non-orthogonal multiple access (NOMA) has attracted more attention with the aim of contributing
to the next generation broadband wireless communication systems, since it outperforms the orthogonal signaling
technique in term of spectral efficiency[WXL06]. As one of the NOMA-based multiple access techniques, the ideaof IDMA is originally inspired by Viterbi in [Vit79], and proposed in [FOO00]. After that, the IDMA concept
was reformulated and introduced in [SH04; HS06] and [LLW+06]. Instead of adopting user-specific spreading
sequence for user separation in code-division multiple access (CDMA) systems, IDMA employs interleavers toseparate different users. Moreover, it combines iterative joint detection and channel decoding to distinguish different users according to the turbo principle. Since IDMA has a variety of design flexibility and exhibits highperformance superiority, it has been proposed for many applications and considered as one the most promising
candidates for future uplink wireless communications.
The system model of the conventional IDMA is shown in Fig. 2.14, where the transmitter and the receiver parts areillustrated separately. Fig. 2.14a shows the transmitter structure of the multiple access scheme under considerationof K simultaneous users. The input data bk,i (i denotes the time index of the users’ original bit sequence) of thek-th user is encoded by forward error correction (FEC) encoder, bit-interleaved by random interleaver, and then thecoded bit sequence is modulated by a specific mapping rule so that each segment of the sequence is mapped to thesymbol xk,m (m denotes the time index of the symbol sequence). After that, the modulated signals are transmitted
through mutiple access channel.
At the receiver side, as shown in Fig. 2.14b, the received signal rm is a mixture of all the users’ transmitted signals
plus noise components, which can be expressed asK

rm = ∑ xk,m + nm ,(2.52)

k=1

where nm denotes the AWGN component with variance σn2 . However, if only information sequences of the k-th user
is wanted, the interferences composed of the other users’ signals can also be seen as a part of noises. Therefore,
the received signal (2.52) can be rewritten as
rm = xk,m + ζk,m

(2.53)

Page 25 (49)RESCUE

D1.4, Version 1.0
14−QAM lmap=4 p=10 D=0.1 Optimized by EBSA
0.9

dc= 5 dv=[2 5 20 21 100] a=[0.0721 0.2441 0.4767 0.1610 0.0461]
Optimized by EBSA

0.8

IE(dem)/IA(dec)

0.7
0.6
0.5

1010
0000
0011
1001

0101
1111
0110
1100

1101
0111
1110
0100

1011
1000
0001
0010

0.4
0.3
0.2
0.1
0

0

0.1

0.2

0.3

0.4
0.5
0.6
IA(dem)/IE(dec)

0.7

0.8

0.9

1

Figure 2.15: EXIT chart obtained as the result of EBSA at SINR = −8.69 dB (Code rate Rc = 0.0424, Spectrum efficiency ηSPC−IrR = 0.1611 bits/4QAM-symbol).

withK

ζk,m =∑ xg,m + nm ,(2.54)

g=1,g6=kwhere ζk,m indicates the multiple multiple access interference (MAI) from the other users plus AWGN It is assumed
that ζk,m in (2.53) can be approximated by a Gaussian random variable. The information exchange between
elementary signal estimator (ESE) and the each decoder is performed iteratively following the turbo concept.
In IDMA, the key principle is that the interleavers πk should be different for each user. The interleavers aredesigned randomly and independently so that the interleaved simultaneous transmitted sequences are statisticallyindependent. The bandwidth expansion is fully exploited for FEC coding that typically uses very low rate code,
unlike in the CDMA systems. Based on the bit-interleaved coded modulation with iterative detection (BICM-ID)
system design, an idea of applying BICM-ID structure into IDMA system is arisen in [WAM14], where it is foundthat the BICM-ID technique [FOT+12] is very suitable for designing very low rate code achieving near-capacity
performance. In the technique proposed in [FOT+12], the single parity check and irregular repetition (SPC-IrR)
code parameters, the doped accumulator (DACC), the modulation mixing (MM) ratio, and the labeling patterns
for extended mapping (EM) are optimized in a systematic way by using the EXIT-constrained binary switchingalgorithm (EBSA) technique in the AWGN channel. EBSA optimizes SPC-IrR BICM-ID transmission chainthat achieves close matching between the decoder and demapper’s extrinisic information transfer chart (EXIT)
curves even in a very low SNR range. The use of the optimized SPC-IrR BICM-ID in IDMA systems has alreadybeen investigated in [WAM14], where the excellent performance can be demonstrated via the convergence andrate region analyses. In the following sections of this chapter, the system design of the proposed BICM-IDbased IDMA, including the single-user detection (SUD) and multi-user detection (MUD) techniques, is presented,
together with the performance evaluation as well as the impact analysis of power allocation on convergence andrate region characteristics.
The motivation of using very low rate SPC-IrR codes for BICM-ID based IDMA with EM has been presented
in D2.1.1 in detail, from the viewpoint of the shape matching of their EXIT curves. Fig. 2.15 shows the result oflabeling pattern optimization for 2-bit extended 4-QAM with its demapper’s EXIT curve and the SPC-IrR decoder’s
EXIT curve; the labeling pattern and the code parameters were jointly optimized for spectrum efficiency ηSPC−IrR
of 0.1688 bits per 4-QAM symbol (code rate Rc = 0.0424) by using EBSA. The parameters related to SPC-IrR,
DACC, and MM are presented in the box in the figure.
With EM, lmap bits are mapped on to a constellation point in the modulation part. If, 4-QAM, lmap > 2, more than

Page 26 (49)RESCUE

D1.4, Version 1.0
𝑥1,𝑚

𝑗 𝑚𝑜𝑑 𝑝 ≠ 0
SPC
IrR

∏1
M

𝑢1,𝑗

EM

ℎ1

𝑟𝑚

DACC



ℎ𝐾
𝑥𝐾,𝑚

𝑗 𝑚𝑜𝑑 𝑝 ≠ 0

𝑏𝐾,𝑖

SPC
IrR

∏K

-

∏1
𝐿1,𝑝,𝑑𝑎𝑐𝑐,𝑗

DACC-1

EM
M

DACC

∏1-1

∏K-1
∏K

𝑢𝐾,𝑗
𝐿𝐾,𝑝,𝑑𝑎𝑐𝑐,𝑗

𝐿1,𝑎,𝑑𝑒𝑐,𝑗

SPC-1
IrR-1

𝑏 1,𝑖

𝐿1,𝑒,𝑑𝑒𝑐,𝑗 -+𝑗 𝑚𝑜𝑑 𝑝 ≠ 0

EM-1

𝑏1,𝑖

DACC-1

𝐿𝐾,𝑎,𝑑𝑒𝑐,𝑗
𝐿𝐾,𝑒,𝑑𝑒𝑐,𝑗

SPC-1
IrR-1

𝐿 1,𝑝

𝑏 𝐾,𝑖

+

𝑗 𝑚𝑜𝑑 𝑝 ≠ 0

Outer iteration

𝐿 𝐾,𝑝Soft-Symbol
Generator
Only for MUD

Figure 2.16: System model of the proposed BICM-ID-based IDMA system [WAM14].

one labels having different bit patterns are mapped on to each constellation point. D is the parameter that specifies
the switching ratio for DACC, where every pth of the systematic bits is replaced by the accumulated coded bit[AM11]. single-parity check (SPC) code has its parameter dc , indicating that a single parity bit is added to everydc − 1 information bits. In IrR code, the parameter dv determines the repetition times for the SPC coded bits, andthe ratio is specified by parameter a.
It is found that their EXIT curves are very closely matched, however, the convergence tunnel is open until a point
very close to the (1.0, 1.0) mutual information (MI) point. This result has motivated us to design BICM-ID based
IDMA using very low-rate SPC-IrR.
2.2.2

Detection Schemes

The BICM-ID-based IDMA system design best utilizes the properties of BICM-ID scheme, a serial concatenationof channel coding and mapping, which requires low rate and near-capacity achieving codes. Since the performanceof BICM-ID strongly depends on the matching degree between the constellation labeling pattern and the code
structure, the code and mapping rule optimization technique described in the previous section provides an optimal
solution to the system design. With the superiorities of the EBSA technique, the joint optimizations on codes andmapping of BICM-ID are feasible so that the designed BICM-ID scheme is well suited for applying to the IDMA
system, which allocates the full bandwidth for channel coding.
The system model of the BICM-ID-based IDMA is depicted in Fig. 2.16. At the receiver side, the iterative processing between the demapper and decoder is invoked user-by-user. It has to be noted that there are two types of
iterations between demappers and decoders: inner iteration and outer iteration. The inner iteration is conductedindependently for each user, while the outer iteration is performed between different users. When the outer iteration is not involved in other users’ signal detection, the system performs SUD; on the contrary, in MUD, the outer
iteration is activated aiming to cancel the interference from the other users. Each user uses the same BICM-ID
transmission structure, where the binary bit information bk,i of user-k, k ∈ {1, · · · , K}, at the timing index i is SPCIrR-encoded at the transmitter. The encoded bit sequence is bit-interleaved by a random interleaver Πk , and thenaccumulated by DACC with the switching ratio p to generate a new bits sequence uk, j , where j is the timing index
at the output of DACC. The DACC output binary sequence uk, j are serial-to-parallel converted, and mapped on
to a 4-QAM signal point, in part, according to the labeling pattern of EM, and in part, according to the non-Graylabeling pattern to produce transmission symbols xk,m at the timing index m, with modulation mixing ratio D. After
that, the transmission symbols xk,m are transmitted to the channel. With SUD, the receiver part detects each user
independently, without the any help from the other users; however, with MUD, all the simultaneous users are to be
detected jointly with the help of the other users in the form of soft cancelation.

Page 27 (49)RESCUE

D1.4, Version 1.0
−1

10

−2

10

−3

BER

10

0.95 dB

−4

10

−5

10

−6

10
−12

BER ( 6 users with SUD)
Shannon Limit
−11

−10

−9
SINR (dB)

−8

−7

−6

Figure 2.17: The BER peformance of IDMA-SUD with 6 users.

2.2.2.1

Single-user Detection

As described above, in IDMA systems, user separation is performed by the interleaver utilized in the iteratively
decodable demapping and channel decoding system, for which its patterns have to be different user-by-user. The
demapper of BICM-ID assumes that the other users are regarded as being equivalent to noise, and hence it ispurely a SUD detector. Moreover, the SUD detector works together with decoder so that each user is detectedindependently. The detail of SUD schemes for IDMA can be found in deliverable D2.1.1.

2.2.2.1.1

EXIT Analysis of IDMA-SUD

This subsection presents results of EXIT analysis for the proposed BICM-ID-based IDMA with SUD. We exactly
follow the established methods [Bri01; Hag04] when calculating the EXIT curves. SINR and SNR for each user
are defined as follows
SINRk

=

Pk
,
K
∑g=1,g6=k Pg + σn2(2.55)

SNRk

=

Pk
,
σn2

(2.56)

where Pk , and σn2 denote the power allocated to k-th user and AWGN noise variance, respectively.
Assume that the power allocated to each user is identical, i.e, Pk = 1.0. Fig. 2.15 shows the result of labeling pattern
optimization for 2-bit extended 4-QAM (lmap = 4) with its demapper’s and SPC-IrR decoder’s EXIT curves; thelabeling pattern and the code parameters were jointly optimized for spectrum efficiency ηSPC−IrR of 0.1688 bits
per 4-QAM symbol (code rate Rc = 0.0424) by using EBSA. The parameters related to SPC-IrR, DACC, and MM
are presented in the box in the figure. With this very low code rate, because of the area property, the area under
the decoder curve is very small, which means that the decoder EXIT curve exhibits a “reverse-L” shape, and sois the demapper’s EXIT curve. Because they are closely matched, near-capacity performance, very sharp BER
threshold and error-floor removal (or reduction to a value range below 10−6 − 10−5 of BER) can be expected withthe designed very low-rate code. Note that IDMA-SUD uses the code parameters and labeling pattern shown in
Fig. 2.15.

Page 28 (49)RESCUE

D1.4, Version 1.0
14−QAM l=4 p=20 D=0.01 Optimized by EBSA

map

0.9

dc=5 dv=[2 6 7 23 24] a=[0.2542 0.2479 0.4360 0.0109 0.0511]
Optimized by EBSA

0.8

IE(dem)/IA(dec)

0.7

Q

0.6
0.5

0011
1010
0110
1111

0000
0101
1001
1100

1000
0001
0100
1101

1011
1000
0001
0010

0.4
0.3
0.2

I

0.1
0

0

0.1

0.2

0.3

0.4
0.5
0.6
IA(dem)/IE(dec)

0.7

0.8

0.9

1

Figure 2.18: EXIT chart of IDMA-MUD technique at SNRk = −3.8 dB, k = 1, 2 (Code rate Rc = 0.1226,
Spectrum efficiency ηSPC−IrR = 0.4879 bits/4QAM-symbol) [WAM14].

2.2.2.1.2

Performance Evaluation of IDMA-SUD

BICM-ID-based IDMA-SUD does not require soft interference cancellation, and it only performs demappingand decoding, user-by-user, independently, without providing any a priori information to help the other users.
Therefore, with IDMA-SUD, the BER performance versus SINR, defined by (2.55), is not affected by the numberof the users, if the total user number K and the noise variance σn2 are fixed. Fig. 2.17 plots BER performance of the
proposed BICM-ID-based IDMA-SUD for K = 6, shown by “◦”. The BER threshold is very sharp, and no error
floor can be observed (or invisible within the BER value range shown in the figure). It is found that the threshold
SINR is around −8.69 dB, which is exactly consistent with the EXIT chart shown in Fig. 2.15, and this observation
is independent of the number of the simultaneous users. The consistency between Figs. 2.15 and 2.17 indicates that
it is reasonable to approximate the composite signal composed of the simultaneous users’ signals plus Gaussian
noise by equivalent Gaussian noise having the same power, at least, in a low enough SINR range.

2.2.2.2

Multi-user Detection

In MUD, soft replica of each user’s signal is subtracted from the received composite signal, of which process isreferred to as soft cancellation. The outer iteration shown in Fig. 2.16 is now activated which indicates the outer
iteration is included in MUD. The detection scheme for MUD is detailed in deliverable 2.1.1.

2.2.2.2.1

EXIT Analysis of IDMA-MUD

As stated before, we assume the simplest two-user (K=2) IDMA MAC scenario to identify the convergence property and to focus on the impact of unequal power allocation. We first draw the demapper’s EXIT curve by assumingthat all the other users’ signals are cancelled and we only consider one user. This assumption is reasonable because
as described above, labeling patterns and coding parameters are determined by the EBSA algorithm so that two
EXIT curves are very closely matched, and hence very sharp BER threshold can be expected. This means that if thelabeling patterns and coding parameters are designed at a specified SNR value, soft successive interference cancellation (SSIC) can gradually but finally completely eliminate the other users’ signals, without having to involve any
statistical signal processing-based interference cancellation, as opposed to [KBU09]. The EXIT curve obtained byusing EBSA is presented in Fig. 2.18 for SNRk = −3.8 dB. Since the simultaneous users provide the LLR feedback
to each other, three-dimensional (3D) EXIT curves are used to visualize the convergence property.

Page 29 (49)RESCUE

D1.4, Version 1.0(a) Equal power allocation, (P1 + P2 )/σn2 = 2.21 dB,
SNR1 = SNR2 = −0.8 dB.

(b) Unequal power allocation, (P1 + P2 )/σn2 = 0.63 dB,
SNR1 = −3.29 dB, SNR2 = −1.62 dB.

Figure 2.19: 3D EXIT chart of IDMA-MUD technique with equal and unequal power allocation [WAM14].
0

10

−1

10

−2

BER

10

−3

10

−4

10

0.52 dB−510

BER (2 users with MUD)
Shannon Limit

−6

10

−6

−5.5

−5

−4.5

−4
−3.5
SINR (dB)

−3

−2.5

(a) The BER performance of IDMA-MUD.

−2(b) The trajectory of IDMA-MUD with equal power allocation, SNR1 = SNR2 = −0.8 dB [WAM14].

Figure 2.20: Performance of IDMA-MUD.

2.2.2.2.2

Unequal Power Allocation

Assume that the powers allocated to different users are unequal, or, equivalently, the transmit signal per user isfaded independently and hence the received power per user at the base station (BS) is different. To keep comparison
fair, the total receive power Ptotal is kept constant. In this chapter, Ptotal = 2.0, for all the scenarios tested with twouser IDMA-MUD, and the noise variance is changed accordingly in this case. Fig. 2.19b presents the 3D EXIT
chart in an unequal power allocation scenario where the ratio P1 /P2 = 0.68, SNR1 = −3.29 dB and SNR2 = −1.62
dB. It is found that the two planes intersect at the most of the middle part where the decoder’s EXIT plane is
obviously above the demapper’s. However, there still remains a gap, through which the trajectory is expected togo through and reach a point very close to the (1.0, 1.0, 1.0) mutual information point. It should be noticed thatfor the both users, the SNR value is less than -0.8 dB, which is the case of equal power allocation, tested in theequal power allocation’s case described above. This makes significant impact on the MAC region to be analysedin section 2.2.3.

Page 30 (49)RESCUE

D1.4, Version 1.0

0

10

−1

10

−2

BER of User1

10

−3

10

−4

10

2

(P1+P2)/σn=0.46 dB(P1+P2)/σ2n=0.63 dB(P1+P2)/σ2n=1.08 dB

−5

10

0

0.5

0.68

1

1.5

2

2.5

Ratio (P1/P2)

Figure 2.21: BER performance of IDMA-MUD for K = 2 users with unequal power allocation [WAM14].

2.2.2.2.3

Performance Evaluations of IDMA-MUD

The significant contribution of the research on BICM-ID-based IDMA over AWGN channels is the impact analysis
of equal and unequal power allocation on MUD convergence property, also by using the multi-dimensional EXIT
chart. Note again that since we assume only the simplest two-user IDMA in single-path AWGN channels, power
allocation is intuitively equivalent in meaning to each user’s transmit signal power. It is shown that with unequal
power allocation, smaller total received SNR is required to achieve the BER threshold than with the equal power
allocation. This result is consistent to [Ver98].
With the proposed BICM-ID-based IDMA technique, excellent BER performance versus SINR can be achieved
with SUD. However, when the number of users increases, BER versus each user’s SINR, defined by (2.55),
degrades due to the multiple access interference from the other simultaneous users. Therefore, to achieve better
performance, a technique to reduce or to ultimately eliminate the interference, such as SSIC, is needed. Again, thesimplest two-user scenario is assumed, and this sub-section investigates the BER performance of user 1 with the
SSIC IDMA-MUD without loss of generality. The trajectory indicating the mutual information exchange obtained
through chain simulation is also presented.

2.2.2.2.4

Performance of Equal Power Allocation

Fig. 2.20b presents 3D EXIT chart and the trajectory of the proposed BICM-ID-based IDMA-MUD for K = 2
users, respectively, where the SINR1 = SINR2 = −3.43 dB (SNR1 = SNR2 = −0.8 dB and (P1 + P2 )/σ 2 = 2.21dB). The BER performance in this scenario is shown by “” in Fig. 2.20a. With our proposed technique, the BER
threshold is very sharp and no error-floor is visible in the BER range. Due to the exactly matched EXIT curves
combined with the soft cancellation technique, very near-capacity performance, only 0.52 dB away from the limit,
can be achieved. It is found that the trajectory directly goes through the middle part between the two planes, andreaches a point very close to the (1.0, 1.0, 1.0) mutual information point, as expected in subsection 2.2.2.2.1.

Page 31 (49)RESCUE

D1.4, Version 1.0(a) P1 /P2 = 1, (P1 + P2 )/σn2 = 0.63 dB.

(b) P1 /P2 = 0.6, (P1 + P2 )/σn2 = 0.63 dB.

(c) P1 /P2 = 0.68, (P1 + P2 )/σn2 = 0.63 dB.

Figure 2.22: The trajectories of IDMA-MUD with unequal power allocation [WAM14].

Page 32 (49)RESCUE2.2.2.2.5

D1.4, Version 1.0
Performance of Unequal Power Allocation

Assume that the total power is fixed as (P1 + P2 ) = 2.0 such that (P1 + P2 )/σn2 = 1.08 dB, while changing theratio of P1 /P2 . The results of the simulations conducted to evaluate the BER performance with unequal power
allocation, are plotted in Fig. 2.21. It is found from the figure that the BER curve shown by “+” for user 1 firstdecreases very sharply as the ratio P1 /P2 increases, and it becomes lower than 10−5 between P1 /P2 = 0.58 andP1 /P2 = 0.75, and then it suddenly increases to a value larger than 10−1 , when P1 /P2 ∼= 1.0. Then, after that, it
sharply decreases again when P1 /P2 ≥ 1.3. When the (P1 + P2 )/σn2 is decreased to 0.63 dB, the BER curve shownin “*” has almost the same tendency, still the rapid decrease in BER can be found around P1 /P2 = 0.68, when theratio P1 /P2 < 1.0. However, the BER curve shown in “×” is for (P1 + P2 )/σn2 = 0.46 dB where the same labeling
pattern and code parameters, including the switching and mixing ratios, p and D, respectively, are used as in thecase of (P1 + P2 )/σn2 = 1.08 dB. A similar tendency can be observed but the rapid decrease in BER can not be
observed, when the ratio P1 /P2 < 1.0. Hence, (P1 + P2 )/σn2 = 0.63 dB is identified as the limit case with this code
parameters set and labeling pattern. In the following part, we focus on the limit case so as to make comparison
among several unequal power allocation cases as well as with a counterpart technique, shown in section 2.2.3.
The trajectories representing the mutual information exchange are investigated at the ratios of P1 /P2 ={1.0, 0.6, 0.68} with (P1 + P2 )/σn2 = 0.63 dB. Fig. 2.22a shows the trajectory for P1 /P2 = 1.0 in the 3D EXIT
chart. It is found that although there is a gap between the planes of demapper and decoder, the trajectory is stuckat a relatively low mutual information point. This is because with P1 /P2 = 1.0, SNR1 = SNR2 = −2.38 dB and(P1 + P2 )/σn2 = 0.63 dB, the two planes are so close to each other around the (0.0, 0.0, 0.0) mutual information
point and hence the trajectory can not go through the tunnel. Fig. 2.22b shows the trajectory for P1 /P2 = 0.6(SNR1 = −3.62 dB, SNR2 = −1.4 dB and (P1 + P2 )/σn2 = 0.63 dB). It is found that the two planes intersect in themost of the middle part of the 3D EXIT chart, however, there still exists a small gap on the left side, and the tunnel
is open until the extrinsic mutual information of the demapper for user 1 is around 0.35 and hence the trajectory
can reach the point (demapper’s extrinsic mutual information = 0.35 for the user1). Furthermore, it is found fromthe trajectory that for user 2, decoder’s extrinsic mutual information reaches very close to 1.0. This means thatuser 2 can be nearly fully detected without errors while user 1 can not be fully detected. Fig. 2.22c presents the
trajectory for P1 /P2 = 0.68 (SNR1 = −3.29 dB, SNR2 = −1.62 dB and (P1 + P2 )/σn2 = 0.63 dB). It is found thatthe two planes intersect in the most of the middle part of the 3D EXIT chart, but a gap still exists near the left edgeof the two planes, and the tunnel opens until a point very close to the (1.0, 1.0, 1.0) mutual information point. The
trajectory can sneak through the gap between the two planes, and reach the point very close to (1.0, 1.0, 1.0) mutual information point, which also means both the two users can be fully detected, even with smaller (P1 + P2 )/σn2
value (0.63 dB), compared with equal power allocation case, whose (P1 + P2 )/σn2 value is 2.21 dB.
2.2.3

MAC Rate Region Analysis

It is of our great interest to make the MAC rate region comparison between equal and unequal power allocation
cases, as well as between our proposed and [KBU09]’s proposed technique. The scenario with K = 2 is considered
for the both equal and unequal power allocation cases. To calculate the MAC rate region, we assume that all theusers use Gaussian codebook. The points A1 and A2 , as defined in Fig. 2.23, are given by 
P1,(2.57)
A1 = C
σn2
P1
A2 = C,(2.58)
P2 + σn2with C(x) = log2 (1 + x).
B1 and B2 are defined in the same way as the points A1 and A2 by replacing the user index 1 by 2. Moreover, since
we are assuming P1 = P2 = 1.0 for the case of equal power allocation, and P1 + P2 = 2.0, while changing σn2 andP1 /P2 values for the unequal power allocation case.
From Fig. 2.20a we first determined the SINRk value required to achieve 10−6 BER for P1 /P2 = 1.0 and (P1 +P2 )/σn2 = 2.21 dB, then, all the values of the argument of C(x), which is needed to calculate (2.57)–(2.58), canbe determined for equal power allocation case. For the unequal power allocation case, those values were directly
obtained from P1 /P2 = 0.68, (P1 + P2 )/σn2 = 0.63 dB and P1 + P2 = 2.0 (recall that the rapid decrease of BER

Page 33 (49)RESCUE

D1.4, Version 1.0

2.5
2 users, equal power allocation
FDMA/TDMA
2 users, unequal power allocation
FDMA/TDMA
2 users with counterpart IDMA
FDMA/TDMA

B1
2

Achieved by counterpart
IDMA techniqueRate R2

1.5

Achieved by the proposed
IDMA technique

B1 1
B2
B1
B2
B2 0.500

A2 0.5A2
A1

A2 A1 1

1.5

2

A1

2.5

Rate R

1

Figure 2.23: Gaussian multiple access channels for K = 2 users (the comparison between our proposed
IDMA technique (with equal and unequal power allocation cases) and IDMA counterpart technique proposed by [KBU09]), equal power allocation: SNR1 = SNR2 = −0.8 dB, unequal power
allocation: SNR1 = −3.29 dB, SNR2 = −1.62 dB [WAM14].

happens at P1 /P2 = 0.68, as shown in Fig. 2.21). Fig. 2.23 shows the MAC rate regions (or MAC-pentagon) withthe equal and unequal power allocation cases. It is found that the MAC rate region with unequal power allocationis smaller than that with equal power allocation. However, the achieved spectrum efficiency of two cases are thesame, i.e, R1 = R2 = ηSPC−IrR = 0.4879 bits per 4-QAM symbol, which is shown in Fig. 2.23 by “◦”. This confirmsthat unequal power allocation can achieve the same spectrum efficiency with smaller SNR values.
The dotted line connecting Ai and Bi , i = {1, 2}, corresponds to two-user orthogonal signalling such as frequency ortime division multiple access (FDMA or TDMA). It is found that with both the equal and unequal power allocation
cases, the rate-pair plot is above the FDMA/TDMA line, and with the unequal power allocation, the plot is closer
to the theoretical MAC rate region.
The upper bound of the sum-rate R1 + R2 is given by
R1 + R2≤ C

P1 + P2σn2(2.59)

It is found from Fig. 2.23 that the MAC rate region with unequal power allocation is smaller than that with equal
power allocation, and obviously, with the unequal power allocation, the MAC region is not symmetric. With
the equal power allocation (SNR1 = SNR2 = −0.8 dB), the sum-rate bound Bequal is 1.41, while with unequal
power allocation (SNR1 = −3.29 dB and SNR2 = −1.62 dB), the sum-rate bound Bunequal is 1.1. Therefore, we
can conclude that the achieved sum-rate R1 + R2 = 2 × 0.4879, and R1 + R2 < Bunequal (SNR1 = −3.29 dB and
SNR2 = −1.62 dB)<Bequal (SNR1 = SNR2 = −0.8 dB). It can be concluded that to achieve the same spectrum
efficiency (ηSPC−IrR = 0.4879), unequal power allocation requires smaller SNRs values for each user.
It is interesting to make comparison of the achieved rate pair and the MAC rate region between our proposed and[KBU09]’s proposed techniques. Since [KBU09] assumes an 8 user IDMA scenario, we converted the rate pair andthe MAC region to the two-user case by the following method: first of all, we identified the SNR value, required
to achieve 10−6 BER from Fig. 3 in [KBU09]. Since, in [KBU09], all users use the same code, which achieves
0.2550 bits per 4-QAM symbol, and the same power is allocated to them, A1 point in Fig. 2.23 can be calculated
by assuming that 6 out of 8 users are totally cancelled, and A2 by assuming that 6 out of 8 users are equivalent tonoise. The point B1 and B2 can also be calculated in the same way.

Page 34 (49)RESCUE

D1.4, Version 1.0

1

User1:

3
2

User2:

2N-3

…
4

2N-1

2N-2

…

2N

Interval ߬Figure 2.24: Asynchronous frame timing.

0

10

−1

10

−2

BER

10

−3

10

−4

10

−6

Shannon Limit
Synchronous IDMA
Asynchronous IDMA with CHADE
Asynchronous IDMA without CHADE
−5.5

−5

−4.5

0.52 dB−4
SINR (dB)

−3.5

0.32
dB

0.75 dB
−3

−2.5

−2

Figure 2.25: The comparison of the BER performance between synchronous and asynchronous IDMA.

Since spectrum efficiency of the system proposed in [KBU09] is 0.2550 bits per 4-QAM symbol for all the 8 users,
it is equivalent to each user’s spectrum efficiency of 1.02 bits per 4-QAM symbol in two-user case, assuming a
Gaussian codebook. The calculated MAC region and the rate pair (indicated by “”) obtained by converting from
8 user IDMA to two-user IDMA are also shown in Fig. 2.23. It is found that surprisingly the achieved rate pairwith [KBU09]’s technique is lower than the FDMA/TDMA line with a Gaussian codebook. Obviously, this isbecause the code used in [KBU09] (a convolutional code combined with a low rate repetition code) can achieve
neither near-capacity performance nor very sharp BER threshold.
As one of the future works, the RESCUE project will include correlated source transmission in MAC channels,
by analysing the combination of Slepian-Wolf and MAC rate region. In the previous contribution, the arrival
time of signals containing correlated information is assumed to be different, such as in the Slepian-Wolf relay
system. However, it is quite likely that the signals containing correlated information will mixed at the receiver sothat multiuser detection technique has to be invoked. Consequently, new theoretical rate analysis and decoding
algorithm design has to be conducted in order to provide a spectrum efficient solution. Based on the discussion
above, IDMA is believed to be one of the promising techniques which could be competent at this matter.
2.2.4

Asynchronous IDMA

The scenario assumption made for the BICM-ID-based IDMA algorithm design and performance evaluation, described above, has assumed the perfect synchronism among the different users. Since IDMA uses interleavers to
distinguish different users. Interleaver also plays an important role in the iterative detection. This observation
indicates that IDMA detects the signals of each user by considering the other users’ signals as being equivalent tonoise, regardless of the relationship among the different users as in the soft cancelation process. For example, in
SUD, when concentrating on one user, the other users’ signals are equivalent to noise. So does in the inner iterationof MUD. Hence, it is supposed that proposed IDMA is robust against the frame-asynchronism.
In order to study this issue, we construct an asynchronous BICM-ID-based IDMA system model by adding certaintransmission delay for each user. As shown in Fig. 2.24, for simplicity, we consider the case where there are 2
users in the transmitter part. However, the later on proposed algorithm can be extended in a straight-forward way

Page 35 (49)RESCUE

D1.4, Version 1.0

Figure 2.26: Overview of measurement route (Tx) and the position of fixed Rx [AM13].

to more users. The transmission delay, between 2 different users’s transmitted siganls, is defined as the interval
τ. Thus, in the receiver part, the received signal is the mixture of two asynchronous users’ signals with a delay
interval τ plus noise. Here, we assume that the start and end point of each frame is known.
In Fig. 2.24, since each frame of user 1 is not synchronized with each frame of user 2, for example the first frameof user 2 is overlaping with the first and second frames of user 1. Performing the MUD technique described above
neither between the first frame of user 2 and the first frame of user 1 nor between the first frame of user 2 andthe second frame of user 1 can fully eliminate the interference from the other user. To detect the asynchronousreceived frames as shown in Fig. 2.24, we propose a chained detection (CHADE) technique, with which multiple
frames are jointly detected by performing the iterative MUD on the frame by frame basis with the order indicatedin each frame, 1, 2, ..., 2N − 1, 2N, as shown in Fig. 2.24: start from the first frame of user 1, then the first frameof user 2, ..., until the last frame of user 1 and the last frame of user 2, as illustrated with the solid arrows. Then,soft cancelation applies to the each signal’s overlapping parts. After the soft cancellation, the frame-wise detection
is again performed but in backward direction, i.e. 2N, 2N − 1, ..., 2, 1, as illustrated with the dashed arrows. This
forward and backward recursion is repeated until all the frames are detected.
The computer simulations are conducted to evaluate the BER performance of the constructed asynchronous IDMA
system with delay interval, τ symbol-durations (τ is a random integer that uniformly distributes in the range 1 to100). The code parameters and labeling pattern are the same as shown in Fig. 2.18 with the transmitted symbol
length 20000. The simulation results are presented in Fig. 2.25, where the curve plotted with “◦” is the BER
performance of the asynchronous IDMA with performing the proposed CHADE technique and the curve shownwith “×00 is the BER performance of the asynchronous IDMA without performing the CHADE technique. It
can be observed that the BER performance with CHADE technique outperforms that without CHADE technique,which indicates that with the CHADE technique, the BER performance has been improved 0.75 dB. Then, thecomparison is made between the BER performance of the asynchronous IDMA with performing the proposed
CHADE technique and the BER performance of the synchronous IDMA described in Fig. 2.20a. It is found thatthe BER performance of the asynchronous IDMA proposed with performing the proposed CHADE technique isvery close to the synchronous case, only 0.32 dB gap between the two cases. This comparison shows that the
proposed IDMA system is robust against the frame-asynchronism.
2.2.5

Initial Measurement Data-based Evaluation

Our proposed system is verified by using measurement data in a more practical environment. The measurement
data used in this section, named “Apothekerbrunnen”, was measured and recorded in the city center of Ilmenau,
Germany. The geographical environment of that measurement campaign, from the top view, is shown in Fig. 2.26,

Page 36 (49)RESCUE

D1.4, Version 1.0

Item
Scenario nameScenario type and place
Channel sounderTransmit power
Carrier frequency
Bandwidth
Channel impulse response length
Number of measured channels
within one snapshot
Approx. snapshot distance in time
Tx arrayRx arrayTx vehicle
Rx vehicle

Setting“Apothekerbrunnen”
outdoor, city center of Ilmenau, Germany
RUSK ATMca. 27 dB m at the TX switch input
5.2 GHz
120 MHz1.6 µc s (number of taps 193)
272 (16×16 including guard interval)
20 ms (indirect corresponding to measurement rate)
16 elements UCA, vertical polarization only, mounted on ameasurement trolley (height approx. 1.30m)
8 elements PULA, vertical and horizontal
polarization, 5.50 m above groundhand driven trolley carrying the transmitter
fixed base station position (Receiver)

Table 2.5: Measurement campaign setup.
0

10

0

10

45

8 users, model−based channels, 6 paths
8 users, measurement data−based channels, 6 paths

40
−1

10

−1

10

25
−3

10

20
15

−4

10

BER of User1 (each snapshot)
Received signal power of User1 10
PN (Noise power of User1)
0

500

1000

1500

2000

5
2500

Average FER

30

−2

10

Pr1 (dBm)

BER of User1

35

−2

10

−3

10

−4

10

−5

Snapshot(a) BER performance with measurement data-based channels.

−4.5

−4

−3.5
−3
−2.5
Average SNR (dB)

−2

−1.5

−1

(b) Average FER performance with measurement databased channels.

Figure 2.27: Performance of real-world measurements.
where the measurement has a length about 60 m and was sampled with 2481 snapshots, corresponding to a distanceabout 0.024 m between each pair of the neighboring snapshots. At the transmitter side, an 16-element uniform
circular array (UCA) was used, and the transmitter was indicated by Tx. The Tx was moved along the routemarked by the dashed line shown in Fig. 2.26 at walking speed. The receiver side used an 8-element uniform
linear array (ULA) which is fixed in a certain position about 5.5 m above the ground, and the receiver in Fig. 2.26
is indicated by Rx.
The measurment route can roughly be divided into two regions as shown in Fig. 2.26: (1) the first part in frontof the large open place, route A → B, is mainly dominated by line-of-sight line-of-sight (LOS) propagation; (2)the second part, route B → C from the open place into the pedestrian street, is non-line-of-sight non line-ofsight (NLOS) region, where the snapshot positions are surrounded by buildings with a height about 10 to 15 m.
The major specifications of the measurement campaign and the antenna setup are summarized in Table. 2.5
2.2.6

Measurement Data-based Simulations and Evaluations

A series of computer simulations were conducted to verify the performance of the proposed system in realistic
scenarios by using channel sounding measurement data. The system scheme we use here is exactly the same as ourPage 37 (49)RESCUE

D1.4, Version 1.0

design in previous sections in this chapter, where the joint turbo equalization and BICM-ID-based IDMA model
was used. The code parameters and labeling pattern are optimized by EBSA with each user’s SNRk = 0 dB. 8
simultaneous users (K = 8) and 6 paths (L = 6) were considered in the simulations, and the sufficient outer andinner iterations were performed (10O, 20I).
• Simulation results on BER performance for each snapshot in the view point of user 1 are shown in Fig. 2.27a,where the BER performance versus the relationship between received signal power Pr1 and noise power PN
are presented. In Fig. 2.27a, it is found that the BER performance (indicated with dots) of user 1 (eachsnapshot) are classified into either 0 or almost 0.5. The BERs are 0 when the received power Pr1 (indicatedwith dashed curve) > noise power PN (indicated with solid line), which indicates the SNR is larger than 0
dB. On the contrary, when the received signal power Pr1 < noise power PN , which indicates SNR is smaller
than 0 dB, BERs are almost 0.5. This correponds to the EXIT chart optimized by EBSA, which further
indicates the turbo cliff in BER performance happens around SNR=0 dB.
• Simulation results on average FER performance are shown in Fig. 2.27b, where the average FER performances with measurement data-based channels (indicated with “*”) and with model-based channels (indicated with “+”) are presented. It is observed that the consistency between measurement data-based andmodel-based FER performances has been proven, especially in terms of diversity order.

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D1.4, Version 1.0

Figure 2.28: Block diagram of proposed combination of GFDM and IDMA.

2.3

Combination of GFDM and IDMA

In the previous section it was shown that both GFDM and IDMA provide advantages for RESCUE scenarios.
Therefore it is a natural consideration, to combine GFDM with IDMA to yield a superior system that takes advantages of both components. In GFDM we emphasize the flexibility to adapt to many different channel conditions
and the possibility to coexist with other wireless systems because of its very low OOB radiation. From IDMA
we appreciate the possibility of simultaneous transmission of every all users at the same frequencys, which cansignificantly reduce scheduling overhead. Furthermore, the combination of GFDM with IDMA would result in acompletely non-orthogonal system which can exploit all the benfits of non-orthogonality in aspects of both waveform properties and multiple access.
Fig. 2.28 shows the proposed block diagram of the combination of GFDM and IDMA. At the transmitter side,
each user distributes its encoded bits and extended mapped symbols onto the full allocated time-frequency grid of
the GFDM block. Then a CP is added and each user transmits its signal over the wireless channel to the receiver.
Then, at the receiver, either SUD or MUD can be carried out. SUD is easily achieved by synchronizing to theuser of interest, and performing the IDMA-SUD with an extra step of considering the GFDM modulation in theextended demapper. For MUD the receiver is required to perform the soft-cancellation between the users either inthe data domain after GFDM demodulation or directly onto the received antenna signal.
There exist two ideas on how GFDM can be configured as the underlying waveform to best serve IDMA. One is
to allocate the entire bandwidth and subsymbols to each user so that the data of each user is distributed over theentire bandwidth that is available. Another approach is to group the available bandwidth into smaller channels,
each containing one wide subcarrier. Then, on each subcarrier GFDM is configured to act as a single-carriersystem where the interleaved bits are transmitted as in the standard configuration. This way initial decouplingof users is achieved by separating in frequency, but spectral efficiency is kept high due to IDMA on each single
carrier. For example, different subcarriers can be used for different purposes, such as source, relay and feedback
transmissions.
In general, research on the combination of IDMA and GFDM is ongoing and no detailed results are available by
the time of this writing. More results are to be researched for inclusion into the final report on multiple access in
D1.3.

Page 39 (49)RESCUE3.

D1.4, Version 1.0

Reduced-Complexity Multi-Relay Coordinated MIMO
Beamforming Technique

Frequency reuse has a potential of enhancing the network coverage and accommodating more users in a specificcommunication area. However, such reuse may produce severe co-channel interference especially when the network is dense. In order to handle such problem, multi-relay coordinated multiple-input multiple-output (MIMO)
beamforming techniques have potential of enhancing the spectrum efficiency and guaranteeing the reliability of
unpredictable wireless communication environments, which fits to the perspective of RESCUE project.

3.1

Motivation and Objective

In this chapter, a reduced-complexity multi-relay coordinated MIMO beamforming technique is considered inorder to efficiently handle the co-channel interference and increase the system performances. Comparing withmulti-relay cooperative MIMO beamforming technique that requires the data-level sharing among relay stations(RSs) [LJ08], multi-relay coordinated MIMO beamforming allows each user to be served by one specific relay
station (RS) whereas the set of precoders to be optimized jointly among RSs. In this case, only the transmitter-side
CSI is shared by RSs, which significantly reduces the backhaul burden among RSs.
In the literature, there are many ways to handle the coordinated MIMO beamforming problems. For example,
in [DY10], Lagrangian duality theory was adopted to solve the weighted sum-power minimization problem with
SINR constraints. In [HZB+12], the relation between the SINR optimization and the power optimization has been
applied to achieve a distributed solution of maximization of the minimum SINR (max-min SINR) problem under sum-power constraint, where solving the max-min SINR problem provides a better rate-fairness particularly
important for the cell-edge users. Meanwhile, a robust max-min SINR problem under per-RS power constraints
was proposed by Tajer et al. in [TPW11; HZB+12]. With the transmit CSI to be shared by RSs, it is shown that
Tajer et al.’s algorithm leads to the optimal solution of the max-min SINR problem. However, the optimal solution is carried out by alternatively solving a standard semidefinite programs (SDP) problem in conjunction with alinear bi-section search, where the SDP is performed over matrices with more degrees of freedom than beamforming vectors, so that the solution comes with relatively high computational complexity [SB04]. Motivated by thisobservation, the objective of this work is to propose a reduced-complexity multi-relay coordinated MIMO beamforming for solving the max-min SINR problem under per-RS power constraints. Comparing with the optimum
solution, the proposed algorithm can exhibit close performance with faster convergence and lower computational
complexity.

3.2

System Model and Problem Formulation

Consider that there is a central BS broadcasting different set of data streams to L RSs, where each of RSs accommodated with M transmit antennas serves K single-antenna based users. In this case, all the nodes are operated in
the same frequency band with flat fading channels. Assume that only the transmit CSIs are shared between RSs
but no data sharing, hence, the discrete-time equivalent form of received signal at the kth user served by the jth RS
is given byLy( j,k) = ∑ hT( j,k),l xl + v( j,k) , j=1,...,L,k=1,...,K,

(3.1)

l=1

where xl denotes an M × 1 signal vector transmitted by the lth RS; h( j,k),l denotes an M × 1 channel transition
vector between the lth RS and the ( j, k)th user; v denotes the additive white Gaussian noise with zero mean andvariance σv2 ; and (·)T denotes the matrix transpose. The lth RS employs an M × K (K ≤ M) precoding matrix Tl
to relate xl to a K × 1 symbol vector sl
xl = Tl sl ,(3.2)
where the kth element of sl , denoted by s(l,k) , is the symbol transmitted to the kth user served by the lth RS.
The ( j, k)th user decodes the corresponding message s( j,k) with the interferences to be treated as noise. The SINR

Page 40 (49)RESCUE

D1.4, Version 1.0of the jth RS to the ( j, k)th user link is
SINR( j,k) =pk, j |hT( j,k), j tk, j |2

I( j,k), j + I( j,k),l + σv2

,

(3.3)

√
where pk, j times tk, j denotes the kth column of the precoding matrix T j , tk, j is the beamforming vector withunity norm, and pk, j represents the transmit-power spent on the kth symbol s( j,k) ; I( j,k), j and I( j,k),l denote theintra-cell interference and inter-cell interference, respectively, with their mathematical form given byI( j,k), j = ∑ pn, j |hT( j,k), j tn, j |2 ,(3.4)

I( j,k),l = ∑ khT( j,k),l Tl k2

(3.5)

n6=k

l6= j

with k · k denotes the Frobenius norm.
Based on the above system description, the max-min SINR problem can be mathematically described by
SINR( j,k) /γ ( j,k)

max minTjj,ks.t. kT j k2 ≤ P j , ∀ j,

(3.6)

where γ ( j,k) denotes the target SINR for the ( j, k)th user, P j is the power constraint for the jth RS. With thetransmit CSI to be shared by RSs, each RS can run the optimization algorithms (such as the bi-section searchalgorithm in [TPW11]) independently and form the same beamforming solutions for all RSs. It is worthwhile tonote that although the computational complexity is not a considerable issue for large RSs, in practice, low-complex
algorithms are needed for low-cost access points such as MIMO relays for dense network.

3.3

Reduced-complexity Bi-Layer Iterative Algorithm

The proposed reduced-complexity bi-layer iterative algorithm can be divided into inner layer search and outerlayer search: In the inner layer search, the uplink-downlink duality theory is used to address the optimization of
the max-min SINR problem under a predetermined sum-power constraint; While in the outer layer search, theoptimal downlink transmit-power vector, which was obtained from the inner layer, is proportionally scaled tosatisfy per-RS power constraints.
3.3.1

Bi-layer Iterative Algorithm Design

In detail, the optimization problem solved by the inner layer process can be expressed as
SINR( j,k) /γ ( j,k)

max minTjj,ks.t. ∑Lj=1 kT j k2 ≤ Psum ,(3.7)

where Psum is defined as the predetermined sum-power constraint. Such problem can be solved based on thestandard uplink-downlink duality theory. Specifically, start from that the L RSs compose a virtual RS serving LK
users, and the precoding matrix of the virtual RS formulates a block-diagonal structure, where the jth elementon the block-diagonal is T j . Such block-diagonal structure limits the job within the coordinated beamforming.
T
Denote that q j = q1, j , . . . , qK, j is the virtual uplink transmit-power vector for the users served by the jth RS,
the virtual uplink SINR for the kth user served by the jth RS is given by
SINRUL( j,k) =qk, j hT( j,k), j tk, j

2

I(UL+ I(UL+ σv2j,k), jj,k),l

where

,

(3.8)

2

T
I(UL
j,k), j = ∑ qn, j h( j,k), j tk, j ,

(3.9)

n6=k

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D1.4, Version 1.0

2

T
I(UL
j,k),l = ∑ qn,l h(l,n), j tk, j .

(3.10)

l6= j,nUnder the sum-power constraint, as shown in in [SB04], the uplink and downlink problems share the same SINR
achievable region, which means that, despite the block-diagonal structure of the precoding matrix, the downlink
max-min SINR problem under sum-power constraint can be solved via the virtual uplink max-min SINR problem
under the same sum-power constraint. In this case, the joint beamforming and power allocation algorithm in[SB04] can be implemented in order to get the optimal solution. Meanwhile, the problem can also be solved witha distributed manner as shown in [HZB+12].
Following the optimal beamforming vectors tk, j , ∀k, j, formulation, the optimal downlink transmit-power vector(denoted by p̆) can be formulated by finding the first LK elements of the dominant eigenvector of the matrix


Cψ
Cu
D, 1 T,(3.11)T
1
1 Cψ P 1 Cu
P
sum

sum

which can be scaled so that its last element equals one [SB04]. Regarding to the matrix D,






γ (1,1)γ (L,K),.
.
.
,;C = diag
2
2


 hT
t1,1
hT
tK,L 
(1,1),1(3.12)(L,K),LTu = σv2 , . . . , σv2 is LK × 1 noise vector; 1 is all-one vector with the size of LK × 1; ψ consists of LK × LK
sub-matrices ψ j,l , ∀ j, l, with the size of K × K each, and the (m, n)-th element of each sub-matrix is defined as:(m,n)ψ j,l =(

l = j and n = m,

0,hT( j,m),l tn,l2,otherwise.

(3.13)

Hence, the downlink transmit-power vector p̆ is formed byTp̆ = pT1 , . . . , pTL ,(3.14)

T
where p j = p1, j , . . . , pK, j is the transmit-power vector for the jth RS, ∑Lj=1 p j 1 = Psum , and k·k1 stands for
the Manhattan norm of a vector.
After we found the initial beamforming vectors through the inner layer process, the outer layer process is used
to satisfy the current stage per-RS power constraints. Let the superscript (n) denote the nth iteration of bi-layer
iterative algorithm, and let p̆(n) denote the tentative downlink transmit-power vector obtained from the inner layer
process. By implementing the proposed Proposition 1, p̆(n) can be proportionally scaled for satisfying the per-RS(n+1)
power constraints, and the updated Psum will be used as the sum-power constraint for the next time inner layer
process.
Proposition 1: After giving the optimal downlink transmit-power vector p̆ for the max-min SINR problem undersum-power Psum constraint, each element in p̆ is continuous and strictly monotonically increasing with respect toPsum .
Te , p̆T 1 denote an extended power vector. As we
Proof: Fixed the set of beamforming vectors tk, j , ∀ j, k, let pmentioned above, the way of finding the optimal downlink transmit-power vector p̆ can be formulated as


Cψ
Cu
e = λmax pe,p(3.15)
1
1T Cψ P 1 1T Cu
P
sum

sum

where λmax is the dominant eigenvalue, and then we have



Cψ
Cu
e = 0,
−
λ
I
p
1
max
1T Cψ P 1 1T Cu
Psum(3.16)

sum

Page 42 (49)RESCUE

D1.4, Version 1.0
Algorithm 1: Bi-Layer Iterative Algorithm

1)
2)
3)

(1)

(1)

Initialize Psum = ∑Lj=1 P j , an arbitrary set of tk, j , ∀k, j, and n = 0;(n)

Let n = n + 1, solve the max-min SINR problem under the sum-power Psum constraint from[SB04] or [HZB+12] and obtain p̆(n) ;(n)
If max j kp j k1 /P j , j ∈ [1, L], is greater than one, scale p̆(n) with a scalar α1 , where
(n+1)

(n)

(0 < α1 < 1), to make max j kp j k1 /P j = 1, j ∈ [1, L]. Set Psum = α1 p̆(n)

4)

5)

(n)
Elseif max j kp j k1 /P j ,j ∈ [1, L], is smaller than one, scale p̆

(n+1)
(n)
(α2 > 1), to make max j kp j k1 /P j = 1, j ∈ [1, L]. Set Psum =
(n+1)
(n)
Repeat steps 2) − 4) until Psum − Psum < ε.

(n)

1

;

with a scalar α2 , where

α2 p̆(n)1;where I is (LK + 1) × (LK + 1) identity matrix, and 0 denotes all-zero vector with the size of (LK + 1) × 1.
Following, we further derive (3.16) to an equation consisted of Psum and p̆, which is
1Psum

1T Cψ p̆ +1
Psum

1T Cu = λmax .

(3.17)

Because that each element of the vector 1T Cψ is a positive real value, it is easy to conclude that each element ofp̆ is a strictly monotonically increasing function of Psum .
(n)Following Proposition 1, the detailed power scaling method can be described as: If max j kp j k1 /P j , j ∈ [1, L],
is larger than one, we scale p̆(n) with a scalar α1 where (0 < α1 < 1), and α1 reaches its maximum when
(n)

(n+1)

max j kp j k1 /P j , j ∈ [1, L], is back to one, then, update Psum

= α1 p̆(n)(n)

1; Otherwise, if max j kp j k1 /P j ,j ∈ [1, L], is smaller than one, we scale p̆(n) with a scalar α2 where (α2 > 1), and α2 reaches its minimum when
(n)

(n+1)

(n+1)

(n)

max j kp j k1 /P j , j ∈ [1, L], is reach to one, then, update Psum = α2 p̆(n) . Finally, if Psum − Psum < ε, then1
the outer iteration is said to have converged and the entire algorithm stopped, where ε is a small positive value
very close to zero. To sum up, based on the above analysis, the proposed bi-layer iterative algorithm is given by(1)
Algorithm 1, where ε = 10−6 , and Psum can be arbitrarily chosen from the range of (0, ∑Lj=1 P j ].
3.3.2

Computational Complexity and Convergence Analysis

The computational complexity of Algorithm 1 can be analysed as follows: For the algorithm in the inner layer, as
shown in [SB04], the main complexity in each iteration involves the formulation of ψ, the eigenvalue decomposiM×M , where, if L
tion of D, and LK times generalized eigenvalue decomposition of a matrix
pair with
 3 the size of R3
2is selected from a small integer, their worst-case complexity are O MK , O K and O M K , respectively.
Assume that K = M and Nin represents the number of iterations for the algorithm, the overall worst-case inner
layer complexity is O Nin M 4 . For the outer layer process, the complexity just involves certain element-wise
multiplications and additions of p̆, which can
by comparing with M 4 . Then the whole computational be ignored
4complexity of the proposed algorithm is O Nout Nin M , where Nout denotes the number of iterations for the outerloop. In comparison, the optimal solution (cf. [TPW11]) of the problem (3.6) involves the bi-section search anditeratively
solving a set of SDP problems. In this case, referring to [LY06], the whole computational complexity isO log (amax ) log (1/ε) M 6.5 , where amax is the upper-limit of the bisection search.
For the convergence analysis of the proposed algorithm, the convergence property of the inner layer process has
been well investigated in Section IV.A of [SB04]. Here, we mainly focus on analyzing the convergence of the outerlayer process. According to Proposition 1, with the strictly monotonic property between Psum and p̆, the proposed
algorithm iteratively scales the downlink transmit-power vector p̆(n) , and terminates at the point p̆(∗) . The stopping(∗)
condition is: max j kp j k1 /P j = 1, j ∈ [1, L] after the nth inner layer process. In this case, further iterations would
(n+1)

(n)

not change the transmit-power vector p̆, i.e., we have kp̆(n+1) k1 = kp̆(n) k1 (or equivalently |Psum − Psum | = 0).
The convergence is therefore proved.

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D1.4, Version 1.0
25
Tajer et al. algom.
The proposed algom.

Sum−rate (bit/s/Hz)

20

15

10

5

0

5

10

15

20

25

The number of outer loop iterations

Figure 3.1: The sum-rate performance vs. the number of outer loop iterations, where P1 = P2 = 40dBm (normalized
by noise) [HMY+14].

3.4

Numerical Results

In this section, we investigated the performance of the proposed reduced-complexity coordinated beamforming bycomparing with two baselines. The first one is the joint beamforming and power allocation algorithm in [TPW11],
which led to the optimal solution of the problem (3.6); The second one is the algorithm for solving max-min SINR
problem under sum-power constraint (e.g., see [SB04; HZB+12]). Throughout the entire experiments, all channels
were generated as independent Rayleigh fading, which remained static over the transmission of each block. The
variances of inter-cell channel links were configured as 0.1 so that the coordinated network was possible to beconsidered as weak cross-talk interference. Assumed that there were L = 2 RSs, and each of them was equippedwith M = 4 antennas and served K = 3 users. 3 was set up as the target SINR per active user (i.e., 2 bit/s/Hz per
active user). The SNR was defined as the target transmit-power normalized by noise ratio.
Experiment 1: The objective of this experiment was to examine the convergence behaviors and the rate performance of the proposed bi-layer iterative algorithm. Fig. 3.1 illustrated the convergence behaviors of the proposed
algorithm and Tajer et al.’s algorithm in [TPW11] for a random channel realization. The results showed that the
proposed algorithm only needed around 3 outer loop iterations to reach the stable point, which revealed fasterconvergence by comparing with Tajer et al.’s algorithm. Fig. 3.2 illustrated the sum-rate performance and power
consumption for different algorithms. As shown in Fig. 3.2 (a), the proposed algorithm achieves very close sumrate performance to Tajer et al.’s algorithm. For the probability of power consumption, as shown in Fig. 3.2 (b), one
RS’s power constraint indeed satisfied with equality. In this case, we normalized the actual transmit-power by thetarget transmit-power. Moreover, in order to highlight the algorithm that solves the max-min SINR problem undersum-power constraint had more flexibility for power allocation, we limited P1 = 0.1×SNR and P2 = 1.9×SNR.
Experiment 2: Mathematically, unlike the power minimization problem, the problem (3.6) can always lead to asolution. However, by formulating the actual SINRs, if the ratios SINR( j,k) /γ ( j,k) for all active users were lessthan one, the user switch-off strategies should be implemented to guarantee all active users’ target SINRs. Hence,in this experiment, we aim to examine the different user switch-off strategies to guarantee all active users’ target
SINRs. There are several user switch-off strategies being examined: 1) switching off the user with the largest
received interference power; 2) switching off the user with the smallest received signal power; 3) switching off theuser with the smallest signal to signal to leakage plus noise ratio (SLNR); 4) switching off the user with the largest
leakage interference power. As shown in Fig. 3.3, switching off the user with the lowest received signal power
strategy can always lead to a better performance by comparing with other strategies.

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D1.4, Version 1.0

(a)

(b)

Tajer et al. algom.
The proposed algom.
Algom. for sum−power const.

25

Tajer et al. algom. Tx 1
Tajer et al. algom. Tx 2
The proposed algom. Tx 1
The proposed algom. Tx 2

1.2

Probability of power consumption1

Sum−rate (bit/s/Hz)

20

15

10

5

0

0.8

0.6

0.4

0.2

0

10

20

30

40

0

50

0

SNR (dB/transmitter)

10

20

30

40

50

SNR (dB/transmitter)

Figure 3.2: (a) The sum-rate vs. SNR; (b) The probability of power consumption vs. SNR. For both (a) and (b), assume
that P1 = 0.1 × SNR and P2 = 1.9 × SNR [HMY+14].

(a)

(b)

4

1
0.9

Probability of the number of active users

Average rate (bit/s/Hz/active user)

3.5

3

2.5

2

1.5

1
Received interf. power
Received signal power
SLNR
Leakage interf. power

0.5

0

0

10

20

30

40

SNR (dB/transmitter)

0.8
0.7
0.6
0.5
0.4
0.3
0.2
Received interf. power
Received signal power
SLNR
Leakage interf. power

0.1

50

0

0

10

20

30

40

50

SNR (dB/transmitter)

Figure 3.3: (a) The sum-rate vs. average rate; (b) The probability of the number of active users vs. SNR. For both (a)and (b), assume that P1 = 1 × SNR and P2 = 1 × SNR [HMY+14].

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D1.4, Version 1.0

Conclusion

This document described the current research status of RESCUE regarding multiple access and multi-antenna
technologies. After a summary of the requirements on the employed waveforms and MA schemes for RESCUE
scenarios, CSMA/CA was introduced in detail. The technique and rationale behind CSMA/CA was presented andit was stated that CSMA/CA is used as the MA scheme used for the demonstrator implementation in WP3. Apartfrom CSMA/CA, novel MA schemes are investigated in WP1.
GFDM was shown to be in particular suitable for the scenarios that are targeted by RESCUE applications. Its
robustness against asynchronicity provides means to efficiently cope with non-synchronized access and misalignments. It was shown, that GFDM can easily work together with CSMA/CA to provide a robust PHY scheme
for P2P transmissions. Several investigations have shown that GFDM can outperform the wide-spread OFDM
system in both orthogonal and synchronized and non-synchronized access. A method was presented that exploits
the block structure of GFDM to achieve transmit diversity regardless of the non-orthogonality of the underlying
GFDM waveform. The scheme was shown to outperform GFDM in a fading multipath environment. Furthermore,when non-synchronized access in GFDM occurs, a simple yet effective method to estimate and compensate timeand frequency misalignment has been presented and its performance was evaluated.
Moreover, the non-orthogonal multiple access scheme IDMA was presented. With IDMA, all users transmit at thesame time-frequency resource and can only be distinguished by their respective interleaver. IDMA employs low
code rates per user, however the system’s spectral efficiency can be raised above the capacity that is achievable byorthogonal MA schemes such as TDMA. Two detection modes are available for this scheme. In SUD, only one
user is decoded at a time, and the signal from other users is just considered as noise. In this situation, the low
code rates of the users help to operate in very low SINR regions. For SUD the decoding complexity is very low
since only a single data stream is to be detected. SUD can be efficiently applied in unpredictable environments,
where the SNR is often very low. As a second operation mode, IDMA offers MUD, where the decoder jointly
detects all transmitting users at the same time. This is achieved by using decoded information from all users toapply soft-cancellation at the receiver to decode a better estimate of the transmitted sequences. Then, iterations arecarried out to finally decouple all users. This operation mode is more complex, however its achievable rates areabove the rates of orthogonal MA schemes.
We have shown an algorithm with reduced complexity to solve the max-min SINR problem with per-RS power
constraints. This algorithm distributes transmit power among several relay stations to achieve optimal performancein multi-relay coordinated beamforming scenarios to provide a fair distribution rates per user. It was shown that the
proposed algorithm can reach the same performance as previous works, however, with a significantly reduced computational complexity as the iterations converge faster. Numerical evidence was provided to ensure the correctnessof the proposed algorithm.
This deliverable provides the baseline for further research in RESCUE regarding multiple access und multi-antenna
techniques. In future work, more aspects on the combination of GFDM and IDMA are to be investigated and thebenefits shall be analyzed. Additionally, spatial multiplexing for GFDM will be analyzed in terms of spectral
efficiency and robustness in the unpredictable environments of RESCUE.

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