5G-EnhanceNewsletter

January 2021
Issue 2

Proje ct we bsite : h ttp:/ /5 g -enh a nce .eu/
Pa rtn e rs:- EU re se arch te a m ( VTT, F HG, UO ULU an d
A cce lle ra n )- Ja pan re se arch tea m (T UA T, NI CT, UEC,CA TV , JCTA and RWJ )INSIDE THIS ISSUE

Introduction to 5G-Enhance

1. Introduction to 5GEnhance

2. Advanced spectrum andnetwork resourcesmanagement for eMBB

3. Conclusion

5G Enhanced Mobile Broadband Access Networks in Crowded
Environments (5G-Enhance) is a Horizon 2020 joint projectcomposed of EU research team (VTT, FHG, UOULU and
Accelleran) and Japan research team (TUAT, NICT, UEC,
CATV, JCTA and RWJ). 5G-Enhance intends to develop andexecute large scale trial activities on actual testbed in EU andJapan.
A key objective for 5G-Enhance is to define and evaluate
interoperable 5G eMBB and efficient network solutions indense area. In order to fulfil this the 5G-Enhance consortium
has identified a number of key project objectives; each ofwhich is associated with a distinct set of research andinnovations. These specific objectives are:

“5G-Enhance develop
and execute large scale
trial activities on actual
testbed in EU and Japan.”Objective 1: Develop, plan and execute large scale trial
activities on actual testbeds between EU and Japan.
Objective 2: Clarify the design and specification required forthe demonstration and trials based on two wirelessapplications with the 5G network.
Objective 3: Development of 5G enhanced mobile broadband
technologies in dense area for achieving the requirements (5G
KPI) of the two applications.
Objective 4: Establish long-term research collaboration
between leading industry players, top research institutes anduniversities in Europe and Japan

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2. Advanced spectrum and network
resources management for eMBB (WP4)
In this working package, we develop advanced spectrum and network
resource management for the MBB to realize highly efficient resource
usage under the limited spectrum and network resource. We consider
the environment of multiple micro-operators or local 5G operators
sharing the spectrum. The spectrum and network resources are
dynamically utilized according to the surrounding radio environment,
network environment, and user demand. In order to obtain the radio
environment, we consider using a spectrum database, in which thespectrum measurement information gathered by mobile terminals isstored. The datasets stored at the database are statistically processed
and the radio environment map is generated for predicting the service
area and estimating the interferences among operators. In the field test

“We develop
advanced spectrum
and network resource
management for the
MBB to realize highly
efficient resource
usage under the limited
spectrum and network
resource.”

implemented in WP5, the idea of spectrum database based on the measurement data obtained by each
mobile terminal is used for sharing the network between macro-operator base stations and micro-operator
base stations with the idea of multi-path TCP discussed in WP4. Finally, the spectrum efficiency withconsidering multiple operators is confirmed in the field test of WP5.

2.1

Network Sharing Architecture

Spectrum and network sharing are representing a set of technologies that allows several operators to sharethe same spectrum, RAN or CN. In one form or another, this technology has been present in 3G and 4G systems
since Release 6 of 3GPP specifications [TS 23.251]. The key motivation for network sharing by conventional
operators has been the reduction of deployment and maintenance cost.
Compared to 3G and 4G, where one system was designed to enable all intended use cases, 5G system enables
diversified deployments, where some parts of the system are designed for specific use cases. Furthermore,
due to the specific spectrum and usage regulations, in some regions of the world, there is the possibility to
deploy private 5G networks within the specific use case location and to share these networks with other private
or public operators.
Especially towards addressing eMBB and URLLC, there is a strong need to have high-end technologies being
deployed close to the location of the subscribers in order to be able to provide the extra capacity required
as well as the low latency and the reliability of the communication. This breaks the current centralized
network model, where a uniform operator is deployed across a country, towards a more granular set of
deployments, where local micro-operators (which could also be part of a wide area operator) provide
enhanced services in a specific location for the specific use case need.

“WP4: Advanced spectrum and network resources management for eMBB covers
Network Sharing Architecture, Spectrum Measurement and Learning, Spectrum
Resource Management based on Database, and Link Aggregation.”

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Page 3As a result, there could be deployments, including RAN and CN, tailored to a specific group of use cases oreven a subgroup within, for example, eMBB group of use cases and addressing the specific deployment
requirements like enterprise networks within buildings or public networks in airports or train stations.
In such a situation, where there is a need to deploy multiple RANs and/or CNs for different users even in thesame geographical area, the importance of spectrum and network sharing grows considerably. This ismultiplied by the need of high end of the 5G technologies to be able to supply the response to the specificcapacity, delay and reliability requirements, ultimately resulting in very high costs. Because of these reasons,network sharing was included in the baseline Release 15 of 5GS System Architecture [TS 23.501].
This also brings the concept of micro-operator in play. In this concept, a third-party could deploy a localized
system tailored to the use case in its area. The use cases include enterprise network deployments withinbuildings, temporary private network deployments, or third-party deployments in very dense urban areas
such as train stations.
The micro-operator may offer local services only to a small set of dedicated devices which use only the local
services. In this case, the micro-operator only requires a part of the spectrum to use, the rest being run inparallel with the wide area operator networks. This situation is interesting for safety and security as well as
for logistics use cases.
Furthermore, a more realistic case would be to offer the local services to users that pertain to the wide area
operators. In this case, the usage of the micro-operator network services can be augmented by the usage of
the wide area services, depending on the service agreements between the micro operator and public 5G
operators.
[Shared Resources]
This section describes and analyses the resources which could be shared, in order to set up the basis for thefurther discussion.
Spectrum Sharing – one of the critical resources in the different dense communication areas is the spectrum.
While reserving specific spectrum for specific deployments at a country level would enable the efficient
deployment of the specific operator network, due to the limitations in the number of base stations deployed
and on coverage, most of the spectrum remains unused. Especially in dense urban areas, it is utmost
important that all the spectrum possible will become available for providing increased capacity to thedevices. Hence, the most important resource which will have to be shared and properly allocated is the
spectrum. Please note that spectrum sharing refers to the usage of the same spectrum in different locations
not to the co-existence and non-interference in the same spectrum while having multiple active networks.
RAN Sharing – in order to reduce the number of base stations deployed in specific locations, operators may
share the RAN equipment. The most common option used at the moment is to advertise all of the networks
from the same logical base station which in turn will forward the requests to the dedicated core networks.
RAN sharing is currently in use in areas with low density of communication where the wide area operators do
not see the need to compete with other operators in deploying their own base stations. Instead, for denseareas, such a solution is highly unattractive, as it does not provide the extra compute capacity needed for theoperators and it does not enable the local services, due to the requirement that all the data traffic will be
forwarded to the specific operators sharing the RAN.

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Core Network Sharing – especially for high-capacity, localized deployments, it is very important that most of
the data traffic which has minimal commercial relevance for the wide area network operators will beprocessed or offloaded locally. Similarly, the signaling in a dense area should remain local to the network
provider in the specific area as not to overburden the wide area networks and to offer a low-delay service.
These features can be solved with a local core network which is shared between the multiple wide area
network operators. Please note that core network sharing refers to the sharing of a core network by multiple
other wide area core networks and not only to the existing state of the art use case where a core network isshared by multiple applications (the basis for network convergence since 2G).
For all of these resources, the sharing is done based on a scheduling algorithm which enables the proper
allocation of the shared resource towards the specific communication needs.
[Sharing Architectures]
In Release 15 of 5GS 5G MOCN configuration is specified as shown in Figure 1.1. In this configuration multiple
5G CNs are connected to the same NG-RAN. PLMN IDs of participating operators are broadcasted within theservice area of the shared RAN and a UE can select any of these PLMNs if it has corresponding subscription.

Figure 1.1. 5G MOCN network sharing configuration [TS 23.501].
Local spectrum availability is critical for the establishment of micro operator networks which can beaccomplished through spectrum sharing. Figure 1.2 illustrates the high-level architecture from [MatinmikkoBlue et al. 2018] for micro licensing where the regulator defines the sharing rules for local spectrum micro
licenses. If there are incumbent spectrum users, they are protected from harmful interference from themicro operators. The micro operator has a network management system with a spectrum manager that
controls its spectrum access according to spectrum availability information through spectrum repository andco-existence manager. The co-existence manager is a new entity to coordinate between different license
holders including incumbents and micro licensees with different levels of spectrum access rights.

5G-EnhanceNewsletter no.2

Page 5Figure 1.2. High-level architecture for micro operator concept with micro licensing (modified from
[Matinmikko-Blue et al. 2018]).
5G RAN Sharing
5GC A

5GC X

5G CN Sharing
5GC B

5GC A

5GC X

X, F_X

5GC B

X, F_X

A, F_X

A, F_X
A, F_AA, F_A
B, F_X

B, F_X
B, F_B

Operator A Frequency

Shared Higher Frequency

Operator B Frequency

B, F_B

Operator A Frequency

Shared Higher Frequency

Operator B Frequency

Figure 1.3 Function enhancements for micro operator support for eMBB.
Using RAN sharing and/or CN sharing users from public PLMNs could have access to services provided by amicro-operator network in localized geographic areas. In addition to network sharing, spectrum sharing
between public PLMNs and micro-operator network could be possible in specific frequency bands subject tonational and international radio regulations.
Figure 1.4 shows an example system architecture to implement micro-operator scenario using network sharing
technology.

5G-EnhanceNewsletter no.2

NSSF
Nnssf

Page 6

NEF
Nnef

NRF
Nnrf

PCF

AF

Npcf

Naf

Namf

UE

(R)AN
Shared network

N3

SMF

Nnef
Nausf
AUSF

PCF
Npcf
Nnrf
NRF

Nnssf
NSSF

N
4

N2

NEF

Nudm

Nsmf

AMF

N1

UDM

UPF

N6

DN

Participating operator network or shared network

Participating operator network

Figure 1.4 System architecture to implement micro-operator scenario using network sharing
technology.
The left side of Figure 1.4 shows the minimum set of functions provided by a shared network. If only this set offunctions is shared, this corresponds to RAN sharing. The right side of Figure 1.4 shows the minimum set offunctions provided by a public PLMN. The middle part corresponds to CN sharing. How they are split between
a public PLMN and a shared micro-operator network is flexible and depends on particular deployment.

2.2

Spectrum Measurement and Learning

The structure for spectrum measurement and learning system is shown in Figure 1. The measurement system
includes both high accuracy and portable mobile spectrum sensors. The measurement system accumulates
channel usage through the utilisation of accurate and efficient detection techniques to form the database. The
database is used to create a flexible and scalable spectrum usage model and train the spectrum prediction
model. Based on information from both the channel modeling and prediction, the spectrum manager allocatesthe spectrum between micro operators. In this task, the spectrum measurements, database creation andlearning for 5G systems are discussed to improve spectrum efficiency and reliability for system users.

Fig 3.1 System structure for spectrum measurement and learning.

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Page 7[Development of Cooperative Spectrum Usage Measurement System Prototype for Smart Spectrum
Access]In smart spectrum access (SSA), it is required to realize high-accurate spectrum usage measurements over the
long-term, broadband, and wide area. Figure 3.2 shows the cooperative spectrum usage measurement systemprototype that can meet the above requirements. The developed measurement system enables an adaptive
measurement plane (time-frequency plane) setting over the time and frequency axes synchronously. The
proposed system utilises scheduling (in both time and frequency domains) between measurement sensors to
obtain accurate spectrum usage information. Figure 3.3 shows the noise floor estimation method using FCME
algorithm (in frequency domain) and FCME in both time and frequency domains ( t-ED-mean). As can beappreciated, the normal FCME cannot achieve the desired false alarm rate, which limits the detection
accuracy.

Figure 3.2 Cooperative spectrum usage measurement system prototype.

Figure 3.3 Noise floor estimation.
Publications[1] Hiroki Iwata, Kenta Umebayashi ” Development of Cooperative Spectrum Usage Measurement System
Prototype for Smart Spectrum Access”, IEICE Tech. Rep., SR2019-16, pp. 97-103, May. 2019.

5G-EnhanceNewsletter no.2

Page 8[Design of spectrum usage detection in wideband spectrum measurements]
There is a broad range of wireless systems in a considered broad measurement frequency band, such as from
60MHz to 6GHz, therefore a general design framework in the measurement frequency band is desired. In the
proposed design framework, we at first define two models in terms of the wideband spectrum usage (SPU)
and the spectrum usage detection (SPUD) process. In addition, the proposed design procedure determines theadequate choice of parameters for the SPUD model based on given parameters of the SPU model in the target
frequency band. Numerical evaluation based on computer simulations shows the validity of the design
framework and design procedure.
Figure 3.4 shows the time and frequency resolution settings where the mask and FFT process in one time slot
is demonstrated. The mask indicates the area over which the observed power spectrum in the tiles isaveraged. The averaged energy is used as a decision variable in ED. The mask is designed for each subchannel
as shown for the lth sub-channel in Figure 3.4. The shape of the mask is a rectangle and has two components,
namely the mask frequency bandwidth and mask time duration. Figure 3.5 shows the missed detection
probability versus SNR for different FFT size (resolution). The PMD performance obtained by the proposed
design procedures is the best, which confirms the proposed approach validity.

Figure 3.4: Mask for ED and FFT process with anof SNR PFA = 0.01 for different FFT sizes.

Figure 3.5: PMD performance as a overlapping. function

Publications[1] K. Umebayashi, Y. Tamaki, M. López-Benítez and J. J. Lehtomäki, "Design of Spectrum Usage Detection in
Wideband Spectrum Measurements," in IEEE Access, vol. 7, pp. 133725-133737, 2019.
[A Study on High-Efficiency Energy Detection-Based Spectrum Measurements]
In this work, we investigate an energy detection (ED)-based spectrum measurement, in which the noise floor(NF) estimation is a key functionality for the appropriate ED threshold setting. Typically, the NF has the slowly
time-varying property and frequency-dependency. Several NF estimation algorithms including forward
consecutive mean excision (FCME) algorithm-based method have been proposed. However, these methods did
not deeply consider the slowly time-varying property of the NF and are computationally inefficient. Accordingly,
we propose a computational complexity reduction algorithm based on NF level change detection. This algorithm
is computationally efficient since it skips the NF estimation process when the NF does not change. Figure 3.6
shows the probability of detection as a function of SNR. The probability of detection of the proposed method
almost coincides with the ones of the two-dimensional FCME algorithm-based NF estimation since the proposed
method achieves the same NF estimation performance approximately as of the two-dimensional FCME
algorithm-based NF estimation.

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Page 9Computational complexity of ED based measurement with the proposed method or the conventional twodimensional FCME algorithm-based NF estimation is given in Figure 3.7. The proposed method can reduce thecomplexity by around 1/4. This reduction is because the number of NF estimation executed is reduced.

Figure 3.6 Probability of detection as a function of SNR.

Figure 3.7 Computational complexity.

Publications[1] Hiroki Iwata, Kenta Umebayashi, Satya Joshi, Ahmed Al-Tahmeesschi, Miguel López-Benítez and Janne J.
Lehtomäki,"A Study on High-Efficiency Energy Detection-Based Spectrum Measurements," IEEE WirelessCommunications and Networking Conference Workshop (WCNCW) - International Workshop on Smart Spectrum(IWSS), Seoul, South Korea, Apr. 2020
[2] Yoshinori Yahiro, Hiroto Ebisawa, Kenta Umebayashi ” study on evaluation method by smart phone
application in WLAN channels,” IEICE Tech. Rep., SR2019-19, pp. 113-118, May. 2019.
[Energy Detection for M-QAM Signal]
In this work, we address energy detection for M-ary quadrature amplitude modulation (QAM) signal. Typically,
statistics of random signal is approximated by the Gaussian distribution. However, this may not be able toprovide accurate detection probability. For this issue, a previous work proposed to calculate detection
probability for given M analytically. This work demonstrates that the distribution of observed energy can beapproximated to one distribution regardless of M. In addition, a range where the approximation is applicableis shown. Figures 3.8 and 3.9 show the energy distributions for common M values and different SNRs (signal tonoise ratio). It can be concluded that the proposed method is more appropriate for larger M and lower SNR.

Figure 3.8 Energy distributions for each M where

Figure 3.9 Energy distributions for each M

SNR = 0dB.

where SNR = 10dB.

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Publications[1] Shun Ishihara, Kenta Umebayashi, Janne J. Lehtomäki、”Energy Detection for M-QAM signal” IEICE Tech.
Rep., SR2019-103, pp. 83-86, Dec. 2019.
[Spectrum usage model for smart spectrum]
Modeling the stochastic behavior of observed duty cycle (O-DC) is an important research question as this canlead to improved DSA performance. Typically, a single well-known distribution, such as Beta distribution, isused for modeling the O-DC. Due to the limitation of the number of parameters in the single distribution, theaccuracy of the typical model is not acceptable. For this problem, this work proposes to use mixture distributionin which more than one distribution is employed. The accuracy obtained by mixture distribution can be
improved if the number of distributions and their parameters are set appropriately. For this problem, we employ
a nonparametric Bayesian model (NPBM) in which the number of distributions can be infinite (it has no upper
limit). However, conventional NPBM has low accuracy in some cases. To overcome this issue, in this paper, we
propose a novel method called modified NPBM method for setting the parameters properly. We show the validityof the proposed method based on long-term spectrum measurement results.
As can be appreciated from Figure 3.10, the single Beta distribution achieves poor accuracy compared to the
NPBM1 and NPBM2 (the proposed one). This observation concludes the validity of mixture distribution and NPBM
based approach for LTE bands. In Figure 3.11, the distributions are obtained by the measurements in the WLAN
frequency band. Both single Beta distribution and NPBM1 based distribution achieve poor accuracy. On theother hand, the proposed algorithm, NPBM2, can provide an accurate fit

Figure 3.10 Estimated PDFs by single Beta
and NPBM for WLAN.

Figure 3.11 Estimated PDFs by single Beta distribution
distribution and NPBM for LTE.

Publications[1] K. Umebayashi, "Spectrum usage model for smart spectrum," 2019 IEEE/CIC International Conference on
Communications Workshops in China (ICCC Workshops), Changchun, China, 2019, pp. 48-53.
[2] Kento Yamada, Kenta Umebayashi ” A study on widely acceptable model for spectrum usage”、 IEICE Tech.
Rep., SR2018-119, pp. 141-147, Jan. 2019.

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Page 11[Duty Cycle Estimation using Auto-Regressive Approach]In this study, we investigate AR (Auto-Regressive) modeling to predict the duty cycle (DC), which is spectrum
occupancy rate in time domain. Typical AR model based has two issues in the prediction: time-varying ofstatistics of DC and the existence of time period with low-correlation. To overcome the issues, we propose a
DC status-based AR model in which two statuses (high DC and low DC) are considered. Specifically, the proposedmodel classifies the states of DC value (high or low) by threshold and two models are set under each status.
Figure 3.12 shows the DC obtained from experiments and DC prediction using the conventional AR approach. As
it can be appreciated, the traditional AR approach underestimates the actual DC. Figure 3.13 shows the
proposed two state AR method. As it can be appreciated from Figure 2, the proposed method significantly
enhances the prediction accuracy when compared to conventional AR especially at high DC scenario.

Figure 3.12 Conventional AR model prediction.

Figure 3.13 Proposed AR model.

Publications[1] Kohei Okawa, Hiroki Iwata, Kenta Umebayashi, Janne J. Lehtomäki, Miguel López-Benítez, Satya Joshi、”A study on Auto-Regressive modeling of Duty Cycle”、 IEICE Tech. Rep., vol. 119, no. 325, SR2019-29, pp. 5964, July. 2019.
[Duty Cycle Estimation using Deep Learning Approaches]
A pro-active spectrum usage prediction is a key technique in decision making and spectrum selection for
dynamic spectrum access systems. This work focuses on the estimation of the duty cycle (DC) metric to reflect
spectrum usage. The prediction is formulated as a time-series regression problem. Deep neural networks (DNNs)
is selected to obtain accurate predictions of channel usage. Namely, Multilayer perceptron (MLP), Long shortterm memory (LSTM) and a hybrid model based on convolutional neural network followed by an LSTM (CNNLSTM) layer are selected. The hyper-parameters selection has been optimised utilising both grid search andmulti-stage grid search. Moreover, in many cases, the spectrum usage is measured on a smaller time scale fromthe actual required one. Hence, down-sampling and averaging is required. Averaging operation results inflattening the data and losing essential features to assist DNN to predict the channel usage. We show what is
the minimum required time resolution to have a pro-active prediction system. Then, we propose utilisingfeature engineering to improve prediction accuracy. All the proposed DNNs approaches are trained on real-life
measurements.

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As can be appreciated from Figure 3.14, when utilising a small averaging window of Tc = 1 sec, the accuracy of
prediction is high and the DNN models can predict the actual DC with high accuracy and without having timedelayed predictions. Unfortunately, this observation does not hold when Tc = 5 sec. As can be seen in Figure
3.15, all the DNNs provide an inaccurate prediction. This indicates that the input spectrum usage data is losing
some essential features, rendering it difficult to have a pro-active estimation of future spectrum utilisation.
Figure 3.16 shows the prediction accuracy when DC with other statistical input features are used. Adding input
features significantly assists the system to achieve accurate predictions in a pro-active manner. The
experimental evaluation demonstrated a high potential of DNNs to learn from previous spectrum usage andaccurately predict the spectrum usage.

Figure 3.14 Prediction Accuracy for only DC

Figure 3.15 Prediction Accuracy for only DC

as input (Tc = 1).

as input input (Tc = 5).

Figure 3.16 Prediction Accuracy for only DC as input (Tc = 5).
Publications[1] Ahmed Al-Tahmeesschi, Kenta Umebayashi, Hiroki Iwata, Miguel López-Benítez and Janne J. Lehtomäki,”Applying Deep Neural Networks for Duty Cycle Estimation,” 2020 IEEE Wireless Communications andNetworking Conference, Seoul, South Korea, April 2020.

5G-EnhanceNewsletter no.2

2.3

Page 13Spectrum Resource Management based on Database

The basic structure of spectrum resource management system with spectrum database is shown in Fig. 3.1. In
this database, the radio environment information such as RSSI, location, time, packet success, packet error, isgathered by active mobile nodes like smart phones, vehicles and wireless LAN terminals. The gathered
information is statistically analyzed. The statistical data are used for improving the reliability, spectrum
sharing, area design, and so on. In this task, the spectrum resource management for 5G systems are discussed
for improving the spectrum efficiency and satisfying the demand of users.

Fig. 3.1 System structure of spectrum resource management based on spectrum database.
[Adaptive Band and Power Control for Spectrum Shared Mobile Systems]
In this research, we propose an algorithm that utilizes spectrum band divide allocation and power control incooperation with measurement-based spectrum database for a private 5G environment. Figure 3.2 shows thesystem model of the database. The radio environment information such as the received signal power of theown cell and the interference cell, the spectrum band, the Cell ID, etc., which are observed from the mobile
devices is stored in the database. From the data sets, the average Signal-to-Interference plus Noise power Ratio(SINR) is derived for each square mesh, and the probability distribution of the mesh with the lowest average
SINR is estimated and the cell edge throughput is derived. In the proposed method, from the estimatedprobability distribution and throughput, the throughput can be maximized by utilizing an algorithm thatcombines power control and spectrum band divide allocation to satisfy the permissible outage probability andthe lower limit of throughput.
Figures 3.3 and 3.4 show the simulation results. From Fig. 3.3, the proposed method shows a higher averagethroughput than the conventional method (power control only). From Fig. 3.4, it can be confirmed that thepermissible outage probability is satisfied.

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Figure 3.2 System model.

Figure 3.3 Average throughput.

Figure 3.4 Maximum outage probability.

Publications[1] H. Nakajo, Y. Aoki, K. Katagiri and T. Fujii, “High Density Spectrum Sharing Method among Micro Operators
considering Spectrum Database," IEEE WCNC 2019 International Workshop on Smart Spectrum (IWSS),
Marrakech, Morocco, April 2019.
[2] H. Nakajo and T. Fujii, “Adaptive band and power control for spectrum shared mobile systems,” Proc.
SmarCom 2019, SR2019-73, vol. 119, pp. 25-26, New Jersey, USA, November 2019.
[Spectrum Sharing by Dynamic Bandwidth Allocation based on CQI Information]
In this research, we propose a measurement-based spectrum database (MSD) construction with holding
statistical information of transmittable bits and a spectrum sharing method using MSD. The system model isshown in Fig. 3.5. In MSD construction, the transmittable bits per second of user equipment (UE) can bepredicted from the probability distribution of channel quality information (CQI). MSD calculates the number ofresource blocks (RBs) required to achieve the target number of transmission bits per second of UEs. Then, thedynamic bandwidth allocation is possible by allocating the appropriate bandwidth to evolved Node Bs(eNodeBs). Simulation results are shown in Fig. 3.6. By our proposed method (Dynamic Allocation in Fig. 3.6),
a target UE can continuously satisfy a target number of transmission bits of 5.0 [Mbits]. Moreover, the total
bandwidth used by the target UE and existing UEs that originally exist in the coverage of eNodeBs is 20 [MHz]or under; in brief, the total bandwidth is less than the maximum available bandwidth of the entire system. In
future, the operation when the target UE cannot achieve the communication requirement and the operation
when the dynamic allocation is executed in a shorter cycle will be additional study subjects.

5G-EnhanceNewsletter no.2

Fig. 3.5 System model.

Page 15

Fig. 3.6 Simulation results.

Publications[1] Akihisa Abe and Takeo Fujii, “Dynamic Bandwidth Allocation for Spectrum Shared Private Mobile Networks
Using Prior Measured CQI Information," 2019 IEEE International Symposium on Dynamic Spectrum AccessNetworks (DySPAN), pp. 1-6. Newark, New Jersey, USA, Nov. 2019.
[2] Akihisa Abe, Yuya Aoki and Takeo Fujii, “Time Series Analysis of Multiple Primary Users by Cooperative
Sensing based on SDAR Algorithm,” Proc. SmartCom 2018, SR2018-80, vol. 118, pp.49-50, Bangkok, Thailand,
Oct. 2018.
Domestic conference[1] Akihisa Abe, Takeo Fujii, “Dynamic Bandwidth Allocation Using Prior Measured CQI Information for Spectrum
Sharing in Private Mobile Networks,” IEICE Tech. Rep., vol. 119, no. 325, SR2019-107, pp. 109-115, Dec. 2019.
[Investigation of radio observation and accuracy improvement method using smartphone by crowdsensing]In this study, we implement the observation experiment focusing on measurement errors caused by working
scenarios and smartphone types in the cellular environment. Also, we propose and evaluate the correction
method of observed values using simple linear regression. Figure 3.7 shows the walking route. In the observation
experiment, using one measurement instrument for area evaluation and three types of smartphones, weobserved RSRP in three scenarios: 1. In hand, 2. In the pocket, 3. In bag. In the proposed method, theobservation data can be corrected by uploading the observation data to the database, acquiring the data after
filtering, deriving the regression line, and generating predicted values. Figures 3.8 and 3.9 show histograms of
RSRP before and after correction. It can be seen that RSRP distribution after the correction has smaller errors
between predicted values and ground-truth and approaches 0 dB than before the correction. In addition, morethan 75% of the error between predicted values and ground-truth is distributed within ±5 dB for all smartphones
and observation scenarios. This demonstrates the usefulness of the correction of the observed value by the
proposed method. In future work, we plan to design the algorithm that derives parameters corresponding to
the wireless environment while grasping observation scenarios.

5G-EnhanceNewsletter no.2

Figure 3.7 Walking route.

Page 16

Figure 3.8 Before correction.

Figure 3.9 After correction.

[Frame-Theoretic Non-Orthogonal Multiple Access]In this work, we proposed a new non-orthogonal multiple access (NOMA) based on Frame theory, which is named
massively-concurrent NOMA (MC-NOMA), to accommodate massive users where every user can exploit wholeavailable radio resources. Fig. 3.10 depicts downlink MC-NOMA systems, where a base station transmits the precoded signals to users. In order to mitigate the inter-user interference, the precoding matrix for MC-NOMA
systems is designed based on frame theory. Specifically, an iterative algorithm has been proposed using convex
relaxation. Thanks to the designed precoder, MC-NOMA can achieve higher spectral efficiency than theconventional NOMA schemes. Fig. 3.11 shows the achievable rate of our proposed NOMA, where six users exist
and four orthogonal radio resources are available. As seen from the figure, MC-NOMA achieves higher spectral
efficiency than the conventional scheme, e.g., PDMA and SCMA. As increasing the number of modulated signals,
the performance gain becomes larger. As future work, we would investigate a low-complexity detector for MCNOMA systems with a large number of users.

Figure 3.10 System model.

Figure 3.11 Comparison of achievable rate.

Publications[1] R.-A. Stoica, G. T. F. de Abreu, T. Hara, and K. Ishibashi, “Massively Concurrent Non-Orthogonal Multiple
Access for 5G Networks and Beyond,” IEEE Access, vol. 7, pp. 82080-82100, July 2019. DOI:
10.1109/ACCESS.2019.2923646.
[1] T. Hara, R.-A. Stoica, K. Ishibashi, and G. T. F. de Abreu, “On the Sum-Rate Capacity and Spectral Efficiency
Gains of Massively Concurrent NOMA Systems,” IEEE WCNC2019, pp. 1-6, April 2019.

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Page 17[Scheduling for Multi-User MEC Systems]
In this research, we tackle the user scheduling method for multi-user MEC systems, where users have tasks to
be processed. Different from legacy wireless communication systems, task uploading, task processing, result
downloading need to be considered during scheduling as shown in Fig. 3.12. Firstly we have proposed a taskscheduling strategy based on Johnson’s algorithm to minimize the total completion time. Fig. 3.13 shows thetotal completion time of the proposed strategy. As seen from the figure, the proposed strategy can significantly
reduce the total completion time. Secondly, the scenario where there are two types of users, i.e., MEC users
and legacy users. The objective of MEC users is to complete the task processing within a hard deadline whilethat of legacy users is to maximize the throughput. By proposing the scheduling metric that balances two
different metrics, the two different aims are realized.

MEC Server at BS

MU u

・・・

Figure 3.12 Multi-user MEC system.

Figure 3.13 Comparison of total processing completion time.

Publications[1] R. Kobayashi and K. Adachi, “Radio and Computing Resource Allocation for Minimizing Total Processing
Completion Time in Mobile Edge Computing,” IEEE Access, vol. 7, no. 1, pp. 141119-141132, Dec. 2019[2] S. Mikami and K. Adachi, “A Study on User Scheduling Method for Mobile Edge Computing System,” IEICE
General Conference, B-5-18, Mar. 2020.
[LTE signals observation experiment in Ehime prefecture, Japan]
In this experiment, we measured LTE signals with smartphones to generate averaged channel quality indicator(CQI) maps and averaged reference signal received power (RSRP) maps. The experiment is held in Ehime
prefecture, Japan in 8-10/Nov./2019, and Android smartphones (ZenFone Live L1) and a measurement
application developed by us are used to measure RSRP and GPS information. CQI information is obtained by an
evolved Node B (eNodeB) which is built for this experiment with operating in bandwidth of 2.6 [GHz]. Observed
information is statistically processed in database, and created CQI/RSRP maps are shown in Fig. 1 and 2. Notethat CQI maps have fewer samples than RSRP’s because CQI values can be obtained only when setting theeNodeB to a data-storing mode. As shown in Figs. 3.14 and 3.15, meshes which are closer to the eNodeB tend
to have higher CQI and RSRP values though some area is fluctuated by shadowing. In future, we examine aspectrum sharing method based on the generated maps with considering radio environment in accordance withusers’ positions.

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Figure 3.14 CQI map (10[m] mesh).

2.4

Page 18

Figure 3.15 RSRP map (10[m] mesh).

Link Aggregation

Task 4.4 is focused on research of link aggregation based on MP-TCP. It is expected to contribution to Trial 2
and to joint trial.
Compared to TCP, MP-TCP is a function that enables data transmissions simultaneously via multi-connections
using many IP addresses. While conventional MP-TCP is oriented to wired network, within Task 4.4 we plan to
improve MP-TCP kernel to add the following new functions:•

Extension to transmit overlapping SYN packets•

Improvement of fallback from MP-TCP to TCP•

BBR-based MP-TCP•

Multi-connections using single IP address•

MultiMob and Delay aware functions.

[Extension to transmit multiple SYN packets]
MP-TCP Linux kernel has a function to transmit multiple SYN packets. Each interface is sending a SYN packet
before making the first connection. In a conventional procedure, the same sender and receiver keys are used
to identify an MP-TCP session. However, a malicious third person may add an undesired path by receiving the
SYN packet. To prevent such attack, a new function is proposed to be added to MP-TCP kernel. Instead of using
the same key on each path of MP-TCP, unique key is used on each path. This is enabled by transmitting multiple
SYN packets

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Figure 4.1 Extension to transmit multiple SYN packets.
[Improvement of fallback from MP-TCP to TCP]
Although the previous extension added to MP-TCP Linux kernel also enables a fallback from MP-TCP to usual
TCP, it is too time-consuming because the conventional process finishes the MP-TCP connection and thenreconnects with TCP by using a Linux’s userland socket. The proposal is to enable a fallback from MP-TCP to
TCP without reconnection with Linux’s userland socket. With this enhancement, MP-TCP can act like usual TCP
because the userland socket is uniquely bound to a kernel socket.

Figure 4.2 Improvement of fallback from MP-TCP to TCP[BBR-based MP-TCP]
This feature is to port a Bottleneck Bandwidth and RTT (BBR) congestion control algorithm from Linux kernel
4.9 to Linux kernel 4.1.26. Also the proposal is to use BBR for MP-TCP.

Figure 4.3 BBR-based MP-TCP.

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Page 20[Multi-connections using single IP address]
If dual connectivity is performed at lower layers (PHY and MAC), then single IP address may be used, which
prevents from using MP-TCP. Update to TCP header is proposed to allow using MP-TCP even in this situation.

Figure 4.5 Multi-connections using single IP address.
[MultiMob and delay aware functions]
MultiMob is an extension of MPTCP to enable BBM (Break-Before-Make) handover to decrease energy
consumption.
MultiMob includes three main features:•

Restricting a second connection•

Connection quality monitoring•

Fast new connection mechanism.

Figure 4.6 MultiMob and delay aware functions.

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3. Conclusion
One research activity of 5G-Enhance is to develop advanced spectrum and network resource management for
the MBB to realize highly efficient resource usage under the limited spectrum and network resource. The
specific objectives of this work package are to- Define network sharing architecture for 5G-Enhance- Develop spectrum management and learning scheme- Develop Spectrum resource management based on database- Study Link aggregation
In this newsletters, we introduced 5G-Enhance WP4 research works and results.