Beyond 5G White Paper
Supplementary Volume“Cell-Free / Distributed MIMO ”

Version 1.0
March 7, 2024
Beyond 5G Promotion Consortium
White Paper Subcommittee

Preface .............................................................................................................................. 4
1

Research activities towards wide area deployment of Cell-Free massive MIMO ... 6
1.1

Introduction ............................................................................................................ 6

1.2

User centric clustering for scalability and its demonstration trial .................... 6

1.3

IFoF based mobile fronthaul for cell-free massive MIMO .................................. 7

1.4

Pilot decontamination for capacity enhancement of cell-free massive MIMO .. 8

1.5

Energy efficiency improvement for AP on/off switching ..................................... 9

1.6

Conclusion ............................................................................................................ 10

Acknowledgements ....................................................................................................... 102High-frequency Band Distributed Antenna System ............................................. 12
2.1

Introduction .......................................................................................................... 122.2

High-frequency Band Distributed Antenna System ......................................... 12

2.3

Deploying Distributed Antennas Technologies ................................................. 14

2.4

Control of Distributed Propagation Paths ......................................................... 15

2.5

Multi-user Transmission by analog beamforming formed DAs as Distributed
Cooperative MIMO [8] ......................................................................................... 16

2.6

Conclusion ............................................................................................................ 17

Acknowledgements ....................................................................................................... 17
3

Real-Time Simulator for 6th Generation Mobile Communication System Using
Distributed MIMO ................................................................................................. 19

4

3.1

Introduction .......................................................................................................... 19

3.2

Overview of 6G Simulator ................................................................................... 19

3.3

6G Simulator Using Distributed MIMO in an Outdoor Urban Scenario ........ 20

3.4

Conclusion ............................................................................................................ 23

Distributed MIMO Technologies for High Frequency Bands ................................ 24
4.1

Introduction .......................................................................................................... 24

4.2

Overview of Distributed MIMO System ............................................................. 24

4.3

RoF Transmission ................................................................................................ 25

4.4Inter-AP Coordinated Transmission .................................................................. 26

4.5

Mobility-Prediction-based Antenna-Beam Coordination .................................. 28

1

4.6

Conclusion ............................................................................................................ 29

Acknowledgements ....................................................................................................... 29
5

Distributed Antenna Technology (High-density Distributed Antenna System, andTransmission Point Sharing Control) .................................................................... 30
5.1

Introduction .......................................................................................................... 30

5.2

Ultra-High Density Distributed Antenna .......................................................... 30

5.3

Transmission point Sharing technology ............................................................. 32

5.4

Conclusion ............................................................................................................ 34

Acknowledgements ....................................................................................................... 34
6

User Cluster-centric Approach for Cell-free Massive MIMO Systems ................. 35
6.1

Introduction .......................................................................................................... 35

6.2

UCC-based CF-mMIMO ...................................................................................... 35

6.3

Performance Evaluation ...................................................................................... 37

6.4

Conclusion ............................................................................................................ 39

Acknowledgements ....................................................................................................... 40
7

Low-Complexity User-Centric TRP Clustering Method in Downlink Cell-Free
MIMO with Regularized ZF-Based Beamforming ................................................. 41

8

7.1

Introduction .......................................................................................................... 41

7.2

Proposed TRP Clustering Method ...................................................................... 42

7.3

Numerical Results ............................................................................................... 44

7.4

Conclusion ............................................................................................................ 46

Toward Practical Cell-Free MIMO Network ........................................................ 48
8.1

Introduction .......................................................................................................... 48

8.2

Fronthaul and Backhaul Limitations................................................................. 49

8.3

Network-Assisted Full Duplex ............................................................................ 50

8.4

Other Necessary Technologies ............................................................................ 51

8.5

Conclusion ............................................................................................................ 52

Acknowledgements ....................................................................................................... 522

【Revision History】
Ver.
Date
1.0 2024.3.7
Initial version

Contents3

NotePreface
The implementation of 5G mobile communication systems in society has been in fullswing since around 2020. Mobile communication systems have become a social
infrastructure that connects not only people but also things like the Internet of Things(IoT). A cyber-physical system (CPS) in which people interact with each other, peopleinteract with things, and things interact with each other through cyberspace has cometo have great significance in various aspects of social life. In the next-generation mobile
communication system, it is expected that the communication network centered on themobile communication system will function as the basic infrastructure of society in the
future. Then, the evolution of the mobile communication systems continued to berequired as the basic infrastructure for resolving various social issues, and to provide
safe and prosperous living.
Cell-Free MIMO, also known as distributed MIMO, is one expected elemental
technology to evolve the mobile communication systems drastically from various aspects
by cooperating the distributed antennas. It has 2 major advantages for the evolution of
the current system. One is to reduce the interference among the cells. As the growth ofthe mobile traffic, it has been required to increase system capacity and the cells
densification have been required. But that increases interference at cell edge especiallyin the lower frequency band, and the communication quality would be more location
dependent and it limits the service quality. The other is to keep a connection by utilizing
multiple distributed antennas even under the severe radio propagation environment byshadowing and blockage as well as large pathloss, especially in the higher frequency
band.
However, there still remains technical issues to realize and deploy it in reality. The
major issues are as follows.
 To reduce complexity of the signal processing for improving scalability of thesystem when accommodating a large number of UEs in a large-scale network withmassive number of distributed antennas.
 To increase fronthaul capacity or to reduce fronthaul load with realistic
deployment cost and energy consumption.
 To provide wide coverage in a higher frequency band including sub-THz bandwith sophisticated and highly efficient beamforming technology at each antenna site.
 To enhance operation flexibility to satisfy diversified requirement of user’s withkeeping the total performance.
 To demonstrate the various concept of these technologies with testbed in a realenvironment.
Hence, research and development activities have been conducted actively in Japan, in
telecom operators, network vendors, and academia from various aspects, and this white4

paper introduces recent activities including results of demonstration trials andsimulation results. Concretely, the following contents are introduced:

Title1 Research activities towards wide area deployment of Cell-Free massiveMIMO

Title2 High-frequency Band Distributed Antenna System

Title3 Real-Time Simulator for 6th Generation Mobile Communication System
Using Distributed MIMO

Title4 Distributed MIMO Technologies for High Frequency Bands

Title5 Distributed Antenna Technology (High-density Distributed Antenna
System, and Transmission Point Sharing Control



Title6 User Cluster-centric Approach for Cell-free Massive MIMO Systems



Title7 Low-Complexity User-Centric TRP Clustering Method in Downlink CellFree MIMO with Regularized ZF-Based Beamforming

Title8 Toward Practical Cell-Free MIMO Network

This White Paper was prepared with the generous support of many people who
participated in the White Paper Subcommittee. The cooperation of telecommunicationsindustry players and academia experts, as well as representatives of various industries
other than the communications industry has also been substantial. Thanks to everyone’s
participation and support, this White Paper was able to cover a lot of useful information
for future business creation discussions between the industry, academia, andgovernment, and for investigating solutions to social issues, not only in thetelecommunications industry, but also across all industries. We hope that this White
Paper will help Japan create a better future for society and promote significant global
activities.
Issei Kanno KDDI Research Inc.

5

1
Research activities towards wide area deployment of Cell-Free
massive MIMO
Issei Kanno KDDI Research Inc.
Noboru Osawa KDDI Research Inc.
Masaaki Ito KDDI Research Inc.
Hiroyuki Shinbo KDDI Research Inc.

Abstract —This article introduces research activities for realizing cell-free massive
MIMO especially for its large-scale deployment aiming to provide great wireless quality
everywhere in a wide area. Concretely, the activities include: (1) user centric clustering
to have scalability of the signal processing at a central site including its demonstration
trial, (2) IFoF (IF over fiber) fronthaul technology to reduce deployment cost and toensure installation easiness, (3) pilot decontamination scheme to enhance the capacity
for increasing the number of spatial multiplexing, and (4) AP (access point) on/off
switching technology for energy efficient operation.
1.1

Introduction

Cell-Free massive MIMO is a promising technology for improving performance of
wireless communication from various perspectives by distributing a large number of APs
in an area and operating them cooperatively with signal processing at a central site, and
it is regarded as one on the fundamental technologies for future communication systems
such as 6G [1]. As a communication operator’s perspective, it is expected to deploy
service area with great wireless communication quality and provide communication
services of various use cases including mission critical one everywhere in the area. In
addition, it is also expected to reduce cost and workload for cell planning andmaintenance of the service area because the interference among densified cells can be
mitigated by the signal processing and autonomous coordination at the central site inthis technology. A great deal of research has been conducted including the relevant
technologies such as distributed MIMO and CoMP (Coordinated Multi-Point). However,in terms of its wide area deployment with distributing large number of antennas, there
still remain various technical issues. This article introduces our several research
activities for the issues.
1.2

User centric clustering for scalability and its demonstration trial

One critical issue for deploying large-scale cell-free massive MIMO is its scalability,
which requires limiting computational complexity in order to operate on a large scale atthe central site. In the large-scale network, accommodating many APs and UEs (userequipment), the computational complexity of the signal processing for precoding andmulti-user detection grows prohibitively, which is not practical. To reduce the complexity

6effectively, we have focused on user centric clustering and its signal processing, which
limits the number of APs to process signals of each UE with reducing the interference toother UEs by the partial processing. For the operation, it is efficient and simple to selectthose APs which are located closely to each UE individually at each moment. This canbe viewed as if the selected APs are operated to customize the service area for the UE ateach moment, and that is called as user centric clustering. To demonstrate this concept,
we have developed a testbed and conducted a field trial [2]. In the trail, the testbed is
operated with 8 APs at 4.5GHz frequency band, and 4 UEs are spatially multiplexed in
downlink by utilizing the user centric clustering and partial MMSE precoding. The
results show that those schemes are operated successfully, and each UE can obtainbetter throughput everywhere in a target area compared to cellular operation mode,
where each AP is operated independently for a UE. Note that the throughput
corresponds to that of one UE that is moving along the grids, where the rest 3 UEs arelocated in a fixed position.

Tennis court in KDDI Research

Cell-Free MIMO
With User-centric clustering

Cellular(non cooperative operation)

Commercial
Smart phone×4

Antenna

Antenna×2(Distributed
to 4sites)

Throughput degradation
at cell edge
Antenna

Low

Downlink
Throughput

Throughput improvementby cooperating antennas

High

Fig. 1-1 Demonstration trial of cell-free massive MIMO with user centric clustering
1.3

IFoF based mobile fronthaul for cell-free massive MIMO

Another critical issue for the large-scale cell-free massive MIMO is to deploy thefronthaul in a realistic cost whereas its required capacity and link further increases..
Recently, ARoF (Analog RoF), which achieves large capacity by converting analog RF
waveform to the optical signal, has been attracted much attention of its applicability tofurther wideband transmission targeting 6G. It is noteworthy that such ARoF schemes
do not require ADC/DAC at each AP site, and it is also beneficial to reduce power
consumption and implementation scale of each site. Then, it can relax the installation
requirements at an antenna site. As one type of ARoF, we have focused on intermediate
frequency over fiber (IFoF) [3]. The RF waveform is converted to the IF and thenconverted to optical signals, and the optical signal is converted to IF and then converted
to RF signal at AP site. Applying different IF to convert each signal stream for a MIMO
layer, it can be multiplexed by FDM in a single optical fiber, which drastically improve
efficiency. In case of cell-free massive MIMO, the multiplexing streams, that are7

generated at the central site, are hauled to the vicinity of the AP sites with a single fiber,
and then they are split to each site. That reduces the number and total length of opticalfibers required to be installed. In addition, cost of the E/O and O/E converter is high formmWave frequency band, and it can save the device cost by the conversion of the IF.
This IFoF based mobile fronthaul concept has been demonstrated in [4]. In thedemonstration experiment, a millimeter-wave (28 GHz) wireless communication
environment was established by the 5G signaling tester, assumed as central site,
multiple antennas at different position, regarded as distributed APs and commercial
smartphone, with the IFoF based mobile fronthaul. In the communication tests, the linkamong central site and the smartphone through the fronthaul and wireless propagation
channel is established successfully. Then, it is confirmed that the stable throughput canbe obtained even under the tree blockage by the macro diversity effect with the
distributed APs.

IFoF signal

Central Site (DU)

Antenna sites
(RU)

Freq.
#1 #2
IF signal #1
IF signal #2

IF signal #n#n
FDM signaltIF ➔ RFFDM signaltt
IF-MUX

H-RLC
L-RLC
H-MAC
L-MAC
H-PHY
L-PHY
IF

E/O

O/E
Optical Fiber

RF signals

IF
DEMUX
＋
Frequency
Conversion
Split to different sites

Fig.1-2 Concept of IFoF based mobile fronthaul
1.4

Pilot decontamination for capacity enhancement of cell-free massive MIMOIn the large-scale cell-free massive MIMO, it is required to accommodate a large
number of UEs and capability to higher order spatial multiplexing is desired. Due to thedegrees of freedom of the large number of distributed antennas, it has a potential tomultiplex a large number of UEs. However, the overhead of the reference signals could
be a bottleneck to increase the number of spatial multiplexing. For example, current
3GPP standard utilizes orthogonal demodulation reference signal (DMRS), and its
number of ports is limited to 24 in 3GPP at the end of rel.18. Therefore, reuse of thereference signal is inevitable for that purpose, which eventually induces pilot
contamination, and the total capacity cannot be increased due to the severe channel
estimation error. In addition, cell-free massive MIMO has no clear boundaries in an area,
it is hard to apply a cell-based pilot reuse restriction.
To resolve the pilot contamination issue, we have investigated a digital signal
processing-based pilot decontamination scheme, which accepts reuse of the identicalpilot between UEs. Fig. 1-3 shows a concept of the proposed pilot decontamination
method and its simulation results. The proposed method exploits difference of antenna8correlation between same-pilot UEs due to the received angle difference. In the receiver,the contaminated pilot is transformed into angle domain, then a target pilot andinterference pilot are separated in the angle domain. The proposed decontamination
requires estimation of antenna correlation information as angle domain information inadvance. The simulation was conducted with parameters that area size is 2×2 km2, thenumber of APs is 25, the number of antennas at each AP is 16, and the carrier frequency
is 3.5 GHz, respectively. We adopted two types of pilot assignment method, one is random
assignment, and the other is the assignment minimizing contamination under thedecontamination method [5]. In the case of random assignment without ourdecontamination method, the sum SE decreases as the number of UEs increases, due tothe channel estimation error caused by the contamination. On the other hand, the caseof random assignment with decontamination improves the sum SE compared withconventional case. Furthermore, the sum SE of decontamination with ingenious
assignment approaches to that of ideal estimation.

(a) Concept of pilot decontamination(b) Simulation results
Fig. 1-3 Pilot decontamination for increasing capacity in cell-free massive MIMO
1.5

Energy efficiency improvement for AP on/off switching

For many years, spectral efficiency (SE) has been the primary performance indicatorin mobile communication systems. Recently, however, energy efficiency (EE) is also paid
attention to by both academia and industry as a key performance indicator of 6G. In
large-scale cell-free massive MIMO, since the huge amount of energy would be consumed
in the whole network, the EE improvement is requisite. Most general way to improve EE
is reduction of power consumption because EE is given as data rate (multiplication of
bandwidth and SE) divided by power. Note that, when we maximize EE, a constraint ofminimum SE is needed to guarantee a certain level of SE since there is a trade-off
between improving SE and reducing power.
One promising technique to improve EE of cell-free massive MIMO is AP on/off
switching. This adaptively puts the APs into sleep mode, which have little contributions

9

to the performance of UEs according to the multiplexed UEs at each moment as shownin Fig. 1-4(a). Color lines denote the user centric cluster for each UE, and the gray AP isin sleep mode. To establish an efficient scheme, we have investigated efficient algorithms
for joint processing with the AP on/off switching and UCC to improve EE under theconstraint about required data rate of UEs [6]. A performance of total EE is shown in
Fig. 1-4(b). Red solid line shows the performance with the proposed greedy ASO
algorithm. The proposed algorithm executes ASO and UCC alternately to obtain better
EE performance with realistic computational complexity. On the other hand, blue line
shows the performance with conventional ASO algorithm, where fixed number of APs
are put into sleep mode in advance of UCC. As can be seen from Fig. X-4(b), 50-percentileof the performance is improved by 92%.

(a) Concept(b) Simulation results

Fig. 1-4 AP on/off switching for Cell-Free massive MIMO
1.6

Conclusion

In this article, research activities for deploying large-scale cell-free massive MIMO are
introduced. Concretely, the following topics are introduced: (1) user centric clustering tohave scalability and its demonstration trial, (2) IFoF based mobile fronthaul technology
to reduce deployment cost, (3) pilot decontamination scheme to increase the capacity forincreasing the number of spatial multiplexing, (4) AP on/off switching control for energy
efficient operation. Including these technologies, it is expected to evolve standard tooperate cell-free, or distributed MIMO, more effectively in 3GPP, O-RAN, etc.
Acknowledgements
The

part

of

the

contentsareobtained

from

the

commissioned

research(JPJ012368C00401) by National Institute of Information and Communications
Technology (NICT), Japan.

REFERENCE[1] ITU-R, M2516-0, “Future technology trends of terrestrial international mobile
telecommunications system towards 2030 and beyond,” Nov. 2022.

10

[2] KDDI Research Inc., “Successful outdoor experiment employing User-centric RAN
that can provide stable communication to each customer in the area,”
https://www.kddi-research.jp/english/newsrelease/2023/052302.html[3] S. Ishimura, B. G. Kim, K. Tanaka, K. Nishimura, H. Kim, Y. C. Chung, et al.,"Broadband IF-over-fiber transmission with parallel IM/PM transmitter overcoming
dispersion-induced RF power fading for high-capacity mobile fronthaul links", IEEE
Photonics J., vol. 10, no. 1, Feb. 2018.
[4] KDDI Research Inc., "World first successful demonstration of wireless network
deployment methodology for Beyond 5G," Oct. 2021. (Press Release)https://www.kddi-research.jp/english/newsrelease/2021/100701.html[5] N. Osawa, F. Göttsch, I. Kanno, T. Ohseki, Y. Amano, K. Yamazki, and G. Caire,“Overloaded Pilot Assignment with Pilot Decontamination for Cell-Free Systems,” inProc. IEEE WCNC 2023, April 2023.
[6] M. Ito, I. Kanno, Y. Amano, W. Y. Chen, T. Choi, and A. F. Molisch, “Joint AP On/Off
and User-Centric Clustering for Energy-Efficient Cell-Free Massive MIMO Systems,” inProc. IEEE VTC 2022 fall, Sep. 2022.

11

2

High-frequency Band Distributed Antenna System
NTT Access Network Service Systems Laboratories, Daisei Uchida
NTT Access Network Service Systems Laboratories, Takuto AraiNTT Access Network Service Systems Laboratories, Shuki Wai
NTT Access Network Service Systems Laboratories, Mizuki Suga
NTT Access Network Service Systems Laboratories, Hideki Toshinaga
NTT DOCOMO 6G Network Innovation Department, Satoshi Suyama
NTT DOCOMO 6G Network Innovation Department, Juling Huiling
NTT DOCOMO 6G Network Innovation Department, Kiichi Tateishi

Abstract— This article introduces a high-frequency band distributed antenna systemthat achieves 100 Gbps or more, which is the target of extreme-high-speed and extreme
communication capacity wireless transmission for 6G. Specifically, we describe thesystem concept and required technologies. Moreover, we introduce the demonstration
experiment results of A-RoF technology, beam selection technology for ultra-narrow
beams, and distributed cooperative MU-MIMO technology using analog beams as astudy of the required technologies.
2.1

Introduction

100Gbps or more is currently the 6G target for extreme high speed and highcommunication capacity [1]. To stably achieve wireless transmission speeds of 100 Gbps
or more, a signal bandwidth of 1 to 10 GHz is required, making it necessary to utilize themillimeter wave (mmWave) to sub-terahertz (Sub-THz) bands [1]．
This article introduces a high-frequency band distributed antenna system as apromising means of utilizing these high frequency bands for mobile communications. We
describe the system concept, the required technologies, and a study of the issues involved.
2.2

High-frequency Band Distributed Antenna System

Since the sub-terahertz band emphasizes direct links more than millimeter waves, itis necessary to arrange base stations to ensure “line of sight (LoS)” rather than“proximity”. A distributed antenna system is expected to achieve this [1-2]. Figure.2-1
shows the system concept of this system. A base station (BS) consists of one central
station (CS) and multiple distributed antennas (DAs). DAs are set in a distributedmanner so that radio waves can propagate in multiple directions to provide terminal
coverage, and radio signals transmitted or received at any antenna can be modulated
and demodulated by one CS. By switching antennas at the L1 layer, this system canachieve stable high-frequency band wireless transmission even in the fluctuating
shielding environments created by human bodies and cars, etc.

Furthermore, by

providing site diversity from multiple directions, this system is also expected to realize

12DAs placement designs that significantly reduce shielding loss margins while realizing
uniform wireless transmission quality within the services area.
We believe that three technologies are required to realize this system. Figure. 2-2
shows conceptual diagrams of these technologies. The first is distributed antenna
deployment to ensure LoS propagation paths, with high probability for mobile terminals.
The second is distributed propagation realized by the switching of communication
established on ultra-narrow beams to follow the movement of user terminals. The thirdis distributed cooperative MIMO, which utilizes path control technology to handle thelarge-capacity simultaneous transmission demanded by many user terminals [1]. In thenext chapter, we describe a study on each technology.

Fig. 2-1 High-Frequency Band Distributed Antenna System

Fig. 2-2 Technologies issues for high-frequency band distributed antenna system

13

2.3

Deploying Distributed Antennas Technologies

We describe analog radio over fiber (A-RoF) as a means of implementing DAstechnologies. A-RoF is highly effective way of connecting a large number of DAs to a CS
in distributed antenna systems. Studies have been conducted to apply A-RoF to

distributed antenna systems [3-4]. Compared to coaxial cables, connecting DAs and CS
with optical fiber offers the significant advantages of low attenuation and long-distancetransmission of wireless signals, especially in high frequency bands. Furthermore,analog-digital conversion functions can be reduced compared to conventional BS,
because wireless signals are directly converted into optical signals without analog todigital conversion. This is expected to realize DAs that are smaller and have lower power
consumption.
To confirm the feasibility of A-RoF for application to actual wireless systems, a 5G
signal transmission experiment was conducted [5]. Experimental overview and results
are shown in Figure. 2-3. Optical signals were transmitted via 20km single mode fiber(SMF) using C band. The signal was received by the receiver antenna after 6.3m airtransmission. The wireless signals used the 5G FR2 signal format, and so had 28.25GHz
center frequency, 100MHz bandwidth, 60kHz subcarrier spacing and 64QAM
modulation format.
The measured results confirmed the impact of A-RoF transmission on signal quality.
The error vector magnitude (EVM) value after 20km A-RoF transmission was 2.39% andthe 5G signals finally received after 6.3m air transmission had EVM value of 2.71%.
These results reveal that the measured EVM values after 20km A-RoF and 6.3m airtransmission are sufficient meet 3GPP requirement of 8% or less for the 64QAM format.
The experiments confirm that A-RoF has the potential to realize effective high-frequency
band distributed antenna systems.

Fig. 2-3 Experimental overview and results.

14

2.4

Control of Distributed Propagation Paths

We introduce two analog beam controlling technologies to permit the manipulation of
the distributed propagation paths.
2.4.1

Fast Beam Search Based on Recorded Beam Combinations [6]When conducting beam search in a high-frequency band distributed antenna system,
radio resources used the beam search are consumed in proportion to the number of DAs.
In order to solve this problem, we are considering a method that does not search all
beams for all DAs except one DA. Figure. 2-4 shows the concept of this method. Owing
to the dominance of LoS paths in this system, optimal beam combinations of each DA fora user equipment (UE) depend on the UE position, and so the combination is expected
be limited in entire area. Based on this consideration, we have assessed a beam searchmethod that considers only those beam combinations selected at each DA in the past.
After detecting the optimal beam for the first DA, the other DA searches consider only
those beam that were selected in the past when the first antenna's beam was selected as
the optimal beam.
We evaluated the number of beam searches and the downlink transmission capacityof the selected beams using this method. Figure 2-5 shows the simulation results. The
simulation model is as follows. In a 100m×100m×15m area, DAs are set on the ceilingat equal intervals and a UE is randomly set across the area. The carrier frequency bandis the 100-GHz band and the signal bandwidth is 8GHz. The DA has 2-dimensional array
antenna with 16×16 elements while the UE is equipped with 2 antennas. The channel
model is free space propagation. It was assumed that beam combinations would be
sufficiently recorded for the entire area. Fig.2-5 shows that this method can reduce thenumber of beam searches by more than half while maintaining the downlink
transmission capacity compared to the method of hierarchical searching beams for all
DAs.

Fig. 2-4 Proposed partial beam searchbased on recorded beam combinations.

Fig. 2-5 The number of search beams
versus the number of DAs

15

2.4.2

Beam Search Method with Adaptive Beam Width Control Based [7]We are considering a beam search method that adaptively changes the beam width
according to distance from the DA to the obstacle blocking high-frequncy bandtransmission , such as a building. The concept of this method is shown in Fig. 2-6. Thismethod realizes fast initial access by reducing the number of candidate beams used inbeam search by widening the beam width according to the distance to each obstacle.
We have conducted an experiment on this method by using a 60-GHz band radio device
compliant with IEEE 802.11ad.

Figure 2-7 shows the Received Signal Strength

Indication (RSSI) at each measurement point when using this method. In this method,
the beam width was changed to 16 degrees, 26 degrees, and 42 degrees depending on the
distance of the obstacle. For comparison, RSSI were also measured with the beamwidth
fixed in all directions. In the method shown in Fig.2-7, the arrow directions and colors
represent the beam direction and beam width. The red, green, and blue lines represent
beam width of 16deg., 26deg., and 42deg., respectively.

As shown in Fig. 2-7, thismethod can reduce the number of beam searches from 25 to 12 while maintaining almost
the same RSSI value compared to when the beam width is fixed at 16 degrees. Therefore,
this method can maintain the size of the coverage area while reducing the overhead
incurred by beam search.

Fig. 2-6 Concept of this method.

2.5

Fig. 2-7 RSSI measurement result when using
this method and when beamwidth is fixed.

Multi-user Transmission by analog beamforming formed DAs as Distributed
Cooperative MIMO [8]We describe multi-user transmission demonstration experiment that used a 40-GHz
band distributed antenna prototype to study distributed cooperative MIMO technologies.
If interference between UEs can be suppressed by DAs, this system can provide multiuser transmission to UEs by connecting them to as many DAs as possible. Pre-coding isa conventional technique for suppressing the interference between UEs. However, when
multiple DAs are used for pre-coding, the positions at which the interference can becanceled are constricted, making it difficult to suppress the interference experienced by
UEs.

16

Against this background, we conducted a demonstration experiment of multi-usertransmission by the analog beamforming functionality of DAs to achieve wide directional
interference suppression. This experiment was conducted in a laboratory with 4 pillars
in an area of 29m ×15m. The frequency band used in this experiment was 40-GHz band,
which is higher than the 28 GHz band of 5G mmWave service in Japan. Fig. 2-8 showsthe experimental parameters and results. When pre-coding was applied with wide beams
at both DAs and the UE antennas, the downlink channel capacity decreased by 90%
when the UEs moved. On the other hand, when narrow analog beams were implemented
at both the DAs and UEs antennas without using pre-coding, there was basically no dropin channel capacity from the stationary UE case.

Fig. 2-8 The experimental results of multi-user transmission using 40-GHz Distributed
Antenna system prototype
2.6

Conclusion

We described a high-frequency band distributed antenna system as a means ofrealizing extreme high speed and large communication capacity wireless transmissionthat achieves 100 Gbps or more for 6G. We introduced and described its required
technologies and studied their issues.
Acknowledgements
We are grateful to NEC Corporation for their collaboration in the early stages of thiswork.
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[5] Kota Ito, et.al, “Passive beamformer based remote beamforming scheme for radioover-fiber systems: Experimental demonstration using 28-GHz band reflectarray,”
Optics Communications, vol. 513, June 2022.
[6] S. Wai, et.al, "Fast Beam Search Based on Recorded Combinations for Distributed
Massive MIMO Systems Using Sub-THz Wave Bands," in IEEE Trans. Veh. Technol.,vol. 72, no. 8, pp. 10263-10272, Aug. 2023.
[7] T. Arai, et.al, “A beam search method with adaptive beam width control based onarea size for initial access,” in IEICE Trans. Communications, vol.E106-B, no.4, pp.359366, April 2023.
[8] NTT, “Successful 40 GHz band distributed MIMO demonstration of maintaining
radio communication quality even when interference occurs due to movement of multiple
users ~ Progress in realizing high-capacity communications at event venues andfactories where many radio terminals are active ~”https://group.ntt/en/newsrelease/2023/10/31/pdf/231031aa.pdf

18

3
Real-Time Simulator for 6th Generation Mobile Communication
System Using Distributed MIMO
Kiichi Tateishi, NTT DOCOMO, INC.
Satoshi Suyama, NTT DOCOMO, INC.
Huiling Jiang, NTT DOCOMO, INC.

Abstract— The 6th generation mobile communication system (6G) is expected torealize extremely high-data-rate and high-capacity communications with a peak data
rate of more than 100 Gbps. In order to realize 100 Gbps extremely high-data-rate
communications in 6G, the utilization of so-called sub-terahertz bands such as 100~300
GHz band, which can utilize a dramatically wider signal bandwidth compared to 5G, isexamined. We have developed a 6G system-level simulator (6G simulator) to clarify thefeasibility of 100 Gbps extremely high-data-rate communications by utilizing the subterahertz band as a system. In this article, we introduce an overview of the functions of
this simulator and examples of performance evaluation with distributed MIMO(Multiple Input Multiple Output) technology in outdoor city environment.
3.1

Introduction

In 6G, one of the goals is to provide extremely high-data-rate communications
exceeding 100 Gbps, and to this end, the use of the sub-terahertz band from 100 to 300
GHz is being studied.
NTT DOCOMO has been developing a 6G system-level simulator to evaluate 6G
system performance when using the sub-terahertz band. New Radio Network Topology(NRNT) for constructing distributed radio network topology in the space domain is
becoming an important technical area [1][2]. In NRNT, a variety of technologies arebeing studied including distributed MIMO, use of existing objects such as streetlamps
and lighting fixtures for mounting antennas, development of advanced repeater
technology, use of Reconfigurable Intelligent Surface (RIS) reflectors that candynamically control reflection intensity and direction, the use of moving Base Stations(BSs) , and Mobile Station (MS)-to-MS linking, all of which refer to nodes configuring a
network topology and the technologies adopted for them [1]. This article describes the
6G simulator that introduces distributed MIMO in an outdoor urban environment.
3.2

Overview of 6G Simulator

6G simulator was developed to quantitatively test the 6G requirements and technicalconcepts described in NTT DOCOMO’s 6G white paper [1] and to test the possibility ofapplying the sub-terahertz band from a system perspective [3][4].
This simulator introduces distributed MIMO at the BS for both the Downlink (DL) and
Uplink (UL). In distributed MIMO, a BS consists of multiple Transmission and19

Reception Points (TRPs) corresponding to BS antennas and a central station that
consolidates those TRPs. Here, throughput can be improved through cooperativetransmission/reception at multiple TRPs. In the case of not applying distributed MIMO,
MS performs transmission/reception with only one TRP. This simulator installs multiple
TRPs fixed to positions established beforehand and evaluates DL and UL throughput
characteristics of each MS during (cooperative) transmission/reception between those
TRPs and the MS. In addition, this simulator provides services while a drone TRP moves
along a specific path to reduce propagation loss due to blocking. Robots and self-driving
vehicles are also arranged as MSs in addition to devices possessed by people.
This simulator introduces a massive MIMO antenna consisting of multiple sub-arrays
at each TRP. To simplify the evaluation, it is assumed that the reflection direction by an
RIS reflector can be ideally controlled. In short, the simulator uses the received power
calculated in the above way to ideally switch to the TRP that can communicate with the
MS without delay.
When applying distributed MIMO, transmit precoding will be executed for the subarrays of all TRPs under the central station, but when not applying distributed MIMO,
transmit precoding will be executed only among the sub-arrays within a TRP.
In this article, the simulator uses a Singular Value Decomposition (SVD) precoder fortransmit precoding. The SVD precoder performs an eigenvalue decomposition on thecovariance matrix of the channel matrix and extracts eigenvalues from that matrix indescending order only for a number of rows corresponding to the number of MIMO layersof all MSs being allocated resources.
Reception processing at each TRP and MS performs signal detection by a receiver inwhich the Minimum Mean-Squared Error (MMSE) norm using the channel matrix isused as a basis for Interference Rejection Combining (IRC) (MMSE-IRC receiver). This
simulator computes the block error rate from the received SINR of the detected signal
and calculates throughput on the DL and UL.
3.3

6G Simulator Using Distributed MIMO in an Outdoor Urban Scenario

This simulator simulates an outdoor urban environment. It can execute a simulationbased evaluation at the system level in a scenario that deploys 5G and 6G in such anenvironment. The urban scenario is shown in Fig. 3-1. This environment includes anopen public square surrounded by high-rise buildings. The square includes no reflecting
objects but is lined with trees. People, robots, and self-driving vehicles are either
stationary or moving. Fixed TRPs and RISs are installed on the walls of buildings or onstreetlamps and a drone TRP makes a round trip in the air along a roadside to provide
services to MSs situated away from buildings in the square.
Simulation assumptions are listed in Table 3-1. The center frequency of Sub6,20

millimeter-wave, and sub-terahertz is 4.7 GHz, 28 GHz, and 100 GHz, respectively. The
massive MIMO antenna of a TRP uses a planar array and consists of nine sub-arrays,
each of which performs analog beamforming (BF) of a single beam by an analog circuit.

Drone TRP

Fixed TRP

RIS

Fig. 3-1 6G simulator in an urban scenario.
The duplex mode used here is Time Division Duplex (TDD) with a DL/UL time ratioof 7:3 across all slots. This evaluation environment consists of an arrangement of 22 MSs
with 18 persons possessing an MS and the remaining four MSs mounted on two robots
and two self-driving vehicles. People and robots are moving at 3 km/h while self-driving
vehicles are moving at 60 km/h, both without stopping. In the sub-terahertz band, it isassumed here that a throughput generally greater than 100 Gbps can be achieved with
4 or more layers. The propagation environment is a multipath rayleigh fading channel
model in which spatial correlation among antenna elements is given according to TRP
and MS positions and the antenna configuration.
Table. 3-1 Simulation assumptions
Sub6

Millimeter wave

Sub-terahertz

Center frequency / Bandwidth

4.7 GHz / 100 MHz

28 GHz / 400 MHz

100 GHz / 8,000 MHz

Number of antenna elements in TRP(vertical × horizontal × sub-arrays)144(4×4×9)2304(16×16×9)9216(32×32×9)

Total transmission power in TRP
Sub-array spacing

30 dBm0.5

4

TRP antenna element gain

5 dBi

MS transmission power

23 dBmNumber of antenna elements in MS(vertical × horizontal × sub-arrays)144(4×4×9)MS antenna spacing

0.5

Number of MIMO layers

1, 2, 3, 4, 8

21

30

The results of the MS throughput ratio when not applying distributed MIMO are
shown in Fig. 3-2. The graphs at the left of the figure show throughput ratios for the DL
and UL. Here, the throughputs of 0–1 Gbps, 1–10 Gbps, 10–50 Gbps, 50–100 Gbps, and
100 Gbps or greater are expressed as five different colors with the horizontal axis andvertical axis representing time (slot number) and throughput ratio, respectively. The
colors of the straight lines connecting a TRP and MS in the figure correspond to thesethroughput colors. The graph at the right of the figure shows the throughput for User
Equipment (UE) #22 positioned in front of a high-rise building and circled in black withthe horizontal axis and vertical axis representing time (slot number) and throughput,
respectively.
From Fig. 3-2, the graphs at the left of the figure show that about 70% of the MSs could
achieve a throughput over 1 Gbps in the DL while about 9% could achieve a throughput
over 100 Gbps. The graph at the right of the figure shows that UE #22 could stably obtaina throughput over 100 Gbps in the DL. Users in good environments could enjoy
throughputs exceeding 100 Gbps thanks to the effects of greater bandwidths in the subterahertz band.
Next, Fig. 3-3 shows evaluation results when applying distributed MIMO for the same
installation positions in Fig. 3-2. The graphs at the left of the figure show that about 90%of the MSs could achieve a throughput over 1 Gbps in the DL while about 18% could
achieve a throughput over 100 Gbps. The graph at the right of the figure shows that thethroughput of UE #22 was greater than 100 Gbps in the DL increasing to 190 Gbps at
peak time. These results demonstrate that significantly higher throughput could beachieved compared with the case of not applying distributed MIMO (Fig. 3-2).
The above simulation results clarify that throughput can be greatly improved byapplying distributed MIMO even in an environment in which a spatial multiplexing

0–1 Gbps
1–10 Gbps
10–50 Gbps
50–100 Gbps
100 Gbps–

10 15 20 25 30 35 40 45 50
Geometry (dB)

Fig. 3-2 Evaluation results without distributed MIMO.

22

0–1 Gbps
1–10 Gbps
10–50 Gbps
50–100 Gbps
100 Gbps–

10 15 20 25 30 35 40 45 50
Geometry (dB)

Fig. 3-3 Evaluation results with distributed MIMO.
effect by MIMO cannot be expected due to a relatively small number of reflected waves
and many LOS environments as in an urban scenario.
3.4

Conclusion

In this article, we described the development of 6G simulator that introduces
distributed MIMO in an outdoor urban environment. Looking to the future, we willcontinue to enhance our 6G simulator so that the performance of various 6G technologies
can be evaluated and visualized on the system level and use cases of the 6G era can be
virtually experienced based on that performance.
REFERENCE[1] NTT DOCOMO: “5G Evolution and 6G White Paper, Version 5.0,” Jan. 2023.
https://www.docomo.ne.jp/english/binary/pdf/corporate/technology/whitepaper_6g/DOC
OMO_6G_White_PaperEN_v5.0.pdf[2] Y. Kishiyama et al.: “Trends and Target Implementations for 5G evolution & 6G,”
NTT DOCOMO Technical Journal, Vol. 23, No. 2, pp. 4–13, Oct. 2021.
[3] T. Okuyama et al.: “6G System-level Simulator—Toward 100 GHz Band, 100 Gbps
Extreme-high-data-rate Communications—,” NTT DOCOMO Technical Journal, Vol. 23,
No. 3, pp. 13–26, Jan. 2022.
[4] K. Tateishi et al.: “Enhancement of 6G System-level Simulators,” NTT DOCOMOTechnical Journal, Vol. 25, No. 2, Oct. 2023.

23

4

Distributed MIMO Technologies for High Frequency Bands
Kazushi Muraoka, NEC Corporation
Jun Shikida, NEC Corporation
Masaaki Tanio, NEC Corporation
Toshiki Takeuchi, NEC Corporation
Yasushi Maruta, NEC Corporation

Abstract—We present key technologies for realizing distributed MIMO in highfrequency bands. These technologies include: (1) radio over fiber transmission for costeffective and high-quality signal transmission, (2) inter-access point coordinatedtransmission to reduce interference, and (3) mobility-prediction-based antenna-beamcoordination for improved communication stability in the face of blocking. By integratingthese technologies within distributed MIMO systems, we can facilitate stable highcapacity communication in high-frequency bands while minimizing system costs andcomplexities.
4.1

Introduction

The use of high-frequency bands, such as millimeter waves, is necessary foraccommodating growing mobile traffic but has a risk of communication quality
degradation in the case of blockage. Distributed MIMO, which generates line-of-sightpaths from access points (APs) to mobile terminals (MTs) by deploying multiple APs, ispromising for overcoming this challenge. In the Beyond 5G era, it is expected that such
distributed MIMO will be deployed over a wider area, with effectively coordinatedmultiple APs connected to a centralized baseband unit (BBU). These APs can be installed
outdoors on utility poles, traffic signals, streetlights, etc., and indoors on walls andceilings. With distributed MIMO, MTs can redundantly have line-of-sight paths frommultiple APs, enhancing the communication stability of high-frequency bands. We
introduce the key technologies for distributed MIMO in high-frequency bands.
4.2

Overview of Distributed MIMO System

Fig. 4-1 shows the system concept of distributed MIMO employing analog
beamforming at APs. To deploy distributed MIMO over a wide area, it is necessary to
install numerous APs. Securing installation sites for APs is not easy, as it can be bothtime-consuming and costly. Thus, it is critical to miniaturize the APs and increase thenumber of potential installation sites. The low-layer functional split, in which the digital
signal processing functions traditionally handled by the 5G radio units (RUs) aretransferred to a BBU, is suitable for AP miniaturization. In this case, communication
between the fiber-optic connected APs and the BBUs requires radio over fiber (RoF)
transmission, which transmits radio signal waveforms over optical fibers. If commercial-24

off-the-shelf (COTS) optical transceivers can be used for RoF transmission, theinstallation cost can be reduced. In Section 4.3, we discuss RoF technology [1][2] that
achieves high-quality transmission using COTS optical transceivers.
While installing a large number of APs over a wide area can redundantly secure theline-of-sight paths, excessive interference may occur in some locations. Therefore,
distributed MIMO requires transmission technology that coordinates multiple APs tosuppress interference. To coordinate transmission from the multiple APs, BBUs need tounderstand the received signal level of each AP and each beam at the MTs. However, in
actual mobile communication systems, there is a limit to the number of reports of thereceived power from MTs. In Section 4.4, we will discuss inter-AP coordinatedtransmission [3] that is achievable even with a limited number of reports.
Furthermore, in high-frequency bands, the received power may suddenly drop, and theconnection may be disrupted if an MT goes behind an obstacle. If MTs could always
connect to multiple APs, received power could be maintained, but it would also result in
wasting radio resources and consuming unnecessary power. Hence, we will introduce themobility-prediction-based antenna-beam coordination [4][5], which performs selection of
the combination of APs and beams based on predicting the degradation in reception
quality and allows for joint transmission from multiple APs only when necessary, in
Section 4.5.

Fig. 4-1 Overview of Distributed MIMO System
4.3

RoF Transmission

Among the low-cost RoF methods using COTS optical transceivers, Delta-Sigma RoF
is one of the promising approaches. To make Delta-Sigma RoF more practical, we
introduce the pulse distortion model (PDM)-embedded Delta-Sigma RoF, which
compensates for signal quality degradation caused by waveform distortion.
Delta-Sigma modulation is a modulation technique that outputs quantized signals,
with a feedback configuration that prevents quantization errors from occurring within
the desired signal bandwidth. Delta-Sigma RoF is a RoF transmission method thatapplies Delta-Sigma modulation. Specifically, the radio signal is converted into a 1-bit
signal sequence using Delta-Sigma modulation, then transmitted to the AP through an

25

optical fiber as an optical signal sequence using a 1-bit COTS optical transceiver. At theAP, the original radio signal is regenerated by removing out-of-band quantization errors
with a bandpass filter (BPF). While this method is effective in reducing costs owing to
the use of COTS optical transceivers, there is a challenge of signal quality degradation
at the BPF in the AP, caused by waveform distortion, such as asymmetric pulse
distortion, during the RoF transmission process.
To address this challenge, we present the concept of PDM-embedded Delta-Sigma RoF
in Fig. 4-2 (a). In the first step of this method, the radio signal passed through the BPF
is sent back from the AP to the BBU, and the PDM is generated by comparing it withthe original 1-bit signal sequence of the Delta-Sigma modulation. In the second step, thePDM obtained in the first step is embedded within the feedback loop of the Delta-Sigma
modulation. This approach allows for compensation of not only quantization errors but
also errors caused by RoF transmission distortion, enabling 1-bit modulation thatprevents RoF transmission distortion from affecting the signal quality of thedemodulated radio signal.
In a RoF transmission experiment using a 1 GHz bandwidth orthogonal frequency
division multiplexing (OFDM) signal, we evaluated the residual spectrum obtained byextracting the errors from the original signal within the signal bandwidth. As shown in
Fig. 4-2 (b), the proposed method labeled as “w/ PDM” decreased the error power by anaverage of 6.0 dB compared to the conventional method labeled as “w/o PDM”, resulting
in the in-band Signal to Noise Ratio (SNR) of 30.7 dB. This confirmed that the proposed
method can achieve an SNR of 30 dB or higher, even for a 1 GHz bandwidth signal.

(a) Concept of PDM embedding(b) Measured 1-GHz bandwidth signal

Fig. 4-2 PDM-embedded Delta-Sigma RoF
4.4

Inter-AP Coordinated Transmission

For the inter-AP coordinated transmission method in the distributed MIMO, we adopt
an approach that selects MT and beam based on the reported results of beam reception
quality. Furthermore, in order to enable appropriate MT and beam selection even underconditions where the number of reports of beam reception quality is limited, we present
a method utilizing a database (DB) of beam reception quality [3].

26One of the inter-AP coordinated transmission methods in a distributed MIMO system
adopting analog beamforming is coordinated user and beam selection. This method
selects a combination of an MT and an analog beam for each AP to minimize inter-APinterference. Specifically, the selection process utilizes the beam reference signal
received power (RSRP) at the MT. For each AP, the MT and beam are selected such thatthe difference between the beam RSRP of the serving AP and the beam RSRP of theinterfering AP is greater than a threshold. This allows for reduced interference andimproved user throughput performance.
However, measuring the RSRP of all beams from all APs at MTs and reporting all ofthem to the BBU is not feasible from an overhead perspective. Therefore, we present a
method to estimate the RSRP of all beams from the reported RSRP of a limited numberof beams using an RSRP DB, and use the estimated beam RSRP to select MTs and beams.
Fig. 4-3 (a) shows the conceptual diagram of the RSRP DB. As shown in the figure, theaverage RSRP value of each beam from each AP in each area is stored in the DB. The
DB is constructed by clustering the RSRP values at multiple positions based on
similarity. When selecting MTs and beams for each AP, the appropriate RSRP values
corresponding to the target MT are retrieved from the DB by checking the similarity
between the reported RSRP of a limited number of beams and the clustered RSRP in theDB.
1.0

w/o Coord. Sel. (CS)

0.9

CS w/ all beam RSRP

0.8

CS w/ 4 beam RSRP

0.7

CS w/ 4 beam RSRP + DB

CDF

0.6
0.5
0.4
0.3
0.2
0.1
0.0
0

5

10

15

20

25

30

35

40

User throughput [Mbps](a) Concept of RSRP DB(b) User Throughput PerformanceFig. 4-3 Coordinated MT and Beam Selection with RSRP DB
Fig. 4-3 (b) shows the user throughput performance when applying coordinated MTand beam selection using the RSRP DB. From the figure, it can be seen that when using
only the RSRP of four beams without a DB, the interference reduction effect between
APs is small, and the performance improvement compared to no coordinated selection islimited. However, when using the proposed RSRP DB, the performances are close to theideal case where the RSRP of all beams from all APs are assumed to be available.

27

4.5

Mobility-Prediction-based Antenna-Beam Coordination

When an MT using high-frequency band moves into an environment with blockage, asudden drop in radio communication quality or disconnection may occur as radio waves
do not diffract easily. For distributed MIMO performing analog beamforming, a mobilityprediction-based antenna-beam coordination predicts the occurrence of sudden radio
communication quality degradation due to blocking by predicting the subsequent
position of the MT and can select the appropriate APs and beams [5].
In this method, the radio communication quality, such as RSRP, of each AP and beamat each position is measured in advance, and the combination of optimal AP and beamat each position is learned. During actual operation, the radio communication quality ofeach AP and beam for an MT is continuously observed, and the position of the MT isestimated using machine learning techniques. Furthermore, the subsequent position of
the MT is predicted from the estimated movement track of the MT in the past, and theappropriate APs and beams before the next radio communication quality information is
obtained can be predicted. As a result, even if there is a possibility that the transmission
performance of the AP and beam selected based on the current radio communication
quality information is drastically degraded due to the blocking triggered by the
movement, this method enables stable communication by selecting the appropriate APs
and beams based on the predicted subsequent position of the MT.
In an experimental environment simulating 14 APs shown in Fig. 4-4 (a), we evaluatedthe relative received signal strength (relative to a predetermined level in the device) ofdownlink transmission by using the channel estimate between each AP and terminal
antenna. Fig. 4-4 (b) shows the relative received signal strength when the MT moves
along the route close to the pillars shown in Fig. 4-4 (a). The received signal strength ofthe conventional method, which selects the AP and beam based on the current radio
communication quality, decreased by about 13 dB at the position blocked by the pillar.
On the other hand, in the proposed method, which selects APs and beams based on
mobility prediction of an MT, the received signal strength at the same position is
improved by about 9 dB compared with the conventional method.

(a) Experimental environment and systems(b) Experimental results

Fig. 4-4 Experiment of mobility-prediction-based antenna-beam coordination

28

4.6

Conclusion

We introduced several key technologies that support distributed MIMO for theeffective utilization of high-frequency bands: RoF transmission, inter-AP coordinatedtransmission, and mobility-prediction-based antenna-beam coordination. By combining
these technologies within distributed MIMO systems, we believe it is possible toeconomically build systems that enable stable high-capacity communication even in
high-frequency bands.
Acknowledgements
A part of this research was conducted in collaboration with NTT and NTT DOCOMO.
REFERENCE[1] M. Tanio, N. Ishii, and K. Muraoka, "Wideband delta-sigma radio-over-fiber
embedding a pulse-distortion model for beyond 5G," 2022 IEEE 96th VehicularTechnology Conference (VTC2022-Fall), 2022, pp. 1-5.
[2] Y. Kase, S. Hori, N. Oshima, and K. Kunihiro, "Radio-over-fiber system with 1-bit
outphasing modulation for 5G/6G in-door wireless communication," IEICE Trans.
Electron., vol.E106-C, no.7, pp.405-416, 2023.
[3] J. Shikida, K. Muraoka, T. Takeuchi, and N. Ishii, "Inter-access point coordinateduser and beam selection for mmWave distributed MIMO systems," 2022 IEEE 96th
Vehicular Technology Conference (VTC2022-Fall), 2022, pp. 1-5.
[4] NEC, "World's first successful demonstration of distributed MIMO that continues
wireless connections in the 28 GHz band by eliminating shielding issues,"
https://www.nec.com/en/press/202210/global_20221031_04.html[5] NEC, "Successful 40 GHz band distributed MIMO demonstration of maintaining
radio communication quality even when interference occurs due to movement of multiple
users," https://www.nec.com/en/press/202310/global_20231031_01.html

29

5
Distributed Antenna Technology (High-density Distributed Antenna
System, and Transmission Point Sharing Control)
Takashi Dateki, Fujitsu Limited

Abstract— This article introduces ultra-high-density distributed antenna technology
for higher capacity and technology for sharing radio resources among distributed
antennas deployed at multiple locations and among operators for efficient area
deployment.
5.1

Introduction

One important aspect of cell-free communication technology is to be able to flexibly
provide the high communication performance of 6G when and where it is needed,
depending on the user's activity and their environment. It is necessary to provide highperformance communications on demand in each local area. However, in terms of costand power consumption, the overall processing and power consumption must be reduced
to cover the entire area efficiently. This article introduces ultra-high-density distributed
antenna technology for higher capacity and technology for sharing radio resourcesamong distributed antennas deployed at multiple locations and among MNO for efficientarea deployment.
5.2

Ultra-High Density Distributed Antenna

From FY2015 to FY2018, we have been engaged in research and development of ultrahigh-density distributed antenna systems as a technology to realize high capacity andhave performed various simulations and field experiments under the research project of
the Ministry of Internal Affairs and Communications [1].
Fig. 5-1 shows an example of a computer simulation of the ultra-high-density
distributed antenna system with 16 antennas in an area using the real map and building
information for a part of the central Tokyo. The centralized baseband unit provides
cooperative control for a large number of distributed antenna units densely located in
the area. Significant system throughput improvements can be seen compared to the casewhere each transmission point is operated as a different cell (conventional uncoordinatedsystem). It can also be confirmed that a good communication area is formed where theuser is present without degradation at fixed cell boundaries. In general, a dense cellarrangement is expected to increase capacity, but in practice, inter-cell interference
prevents a sufficient capacity increase. Therefore, we have developed a cooperative radio
resource control technique that improves signal quality by reducing interference among
distributed antennas. The technique is basically distributed multi-user MIMO, in which
the entire signal from many distributed antennas is orthogonalized using algorithms
such as ZF and MMSE, and each user receives only the signal component addressed to30each user [2]. This algorithm is called "dynamic virtual cell control technology" becauseof its image of forming individual cells for each user in order to improve communication
quality by reducing interference for all users communicating simultaneously. Unlike
conventional cell configurations that operate with fixed cell boundaries, this technology
can be seen one of cell-free technologies that automatically forms virtual cells based on
propagation path estimates between distributed antennas and users.

Fig. 5-1 The example simulation results of High density distribute antenna systemwith and without coordination control (dynamic virtual cell control)
To verify the advantage of the ultra-dense distributed antenna system, we developed
the experimental device shown in Fig. 5-2(a). The base station equipment consists of acentral baseband unit and up to 16 RF devices (up to 32 antennas) connected by opticalfibers. The central baseband unit uses an over-the-air (OTA) calibration function to
perform high accuracy channel estimation using the reciprocity between Uplink andDownlink channel, and performs spatial multiplexing transmission of up to 32 streams.
A distributed antenna system using these equipment was constructed in Fujitsu ShinKawasaki Technology Square, Kanagawa, Japan, and indoor field experiments were
performed [1]. To confirm the effect of capacity increase by coordination control of theradio resource, the system throughput was compared when the number of TPs wasvaried from 4, 8, to 16 under the condition that the number of TPs and the number of
UEs were the same. The results are shown in Fig. 5-2(b). When cooperative radio
resource control is applied, the capacity increase is almost proportional to the increasein the number of TPs, and it can be confirmed that the system capacity at 16 TPs is about
4 times larger than that of a simple small cell system (i.e. without coordination control).

31

(a)

(b)

Fig. 5-2 (a)The developed experimental equipment, (b) The capacity evaluation result
for the increasing number of TP
The evaluation result for the case when TPs and UEs were located at one of thecandidate locations is shown in Fig. 5-3(a). Fig. 5-3(b) shows the CDF of user throughput
measured multiple times. The results show that distributed placement can improve theareal system capacity and that the improvement can be enhanced by appropriately
designing the location of the transmission points.
(37,19)(1,19)
TP1

TP2

42 41 40 37 36 33 30 29

28 27 26 23 22 19 18 17

38

16
会
議
室

34
会議室

35

39

24
会議室

32

20
会議室

25

21

48

44

47

54

49

52

TP3

TP4

(37, 1)(25,1)

4

55

53

50
51

会
議
室

15

ディスプレイ

43

45 46

31

3

2
56

1

10

5

9

6
7

8

18m

14
13
11 12

TP5

TP0

(13,1)

(1,1)

36m

(a)

(b)

Fig. 5-3 (a)Area map and the TP location, (b) CDF of the user throughput in theexperimental area
5.3

Transmission point Sharing technology

In the R&D project "Research and Development of 5G Base Station Sharing
Technology" by the Ministry of Internal Affairs and Communications (MIC), a technology
to efficiently achieve high density in urban areas was studied by multiple Mobile
Network Operators (MNO) using shared base stations located at many points in the area.
In this project, the shared radio units (RUs) for multiple MNO frequencies and the radio
resource control techniques that efficiently utilize fronthaul within a limited bandwidth
were developed. This radio resource control is an algorithm that optimally allocates a32

large number of RUs located at multiple points in multiple MNOs in an area so that highcapacity can be achieved in the area while limiting the total fronthaul and radio
resources below a certain value. This reduces the maximum capacity required for central
baseband unit and fronthaul to process multiple cells to cover the entire area, while
focusing resources on the required areas to achieve efficient communication according todata traffic demands [3],[4].
Fig. 5-4 shows an image of the operation of radio resource control in a base station
sharing system. if MNO #A and #B have their own base stations in independent locations,UEs that are far from the cell center will have lower field strength. Or, in an interferencelimited environment, UEs will have a lower SINR due to interference from other cells
communicating in the same band. On the other hand, in a base station sharing system,
each MNO allocates and uses a portion of the system bandwidth from its own location,
allowing all UEs to communicate with nearby base stations without increasing the total
amount of radio resources used by the entire system. This improves system performance
without increasing the total amount of overall radio resources and radio transmission
energy.

Fig. 5-4. An image of the radio resource control in a base station sharing system
Fig. 5-5 shows the simulation results of system throughput using the simulation model
correspond to urban areas in 3GPP. It can be seen that the average throughput issignificantly improved by base station sharing.

Fig. 5-5 Throughput improvement of radio resource sharing

33

5.4

Conclusion

In this paper, we have introduced ultra-dense distributed antenna technology for highcapacity and base station sharing technology for efficient area deployment. In addition,
cell free NW is also related to other technologies introduced in this white paper, such as
the intelligent reflectors and relay technologies, etc. These technological elements should
not only be considered separately to be introduced in different use-cases, but the
integrated system including various technologies should adapt to the dynamicallychanging requirements of the area. The overall picture of such a 6G system has still
unclear. Therefor active research and development is expected to continue in the future.
Acknowledgements
This paper includes a part of results of “The research and development project forrealization of the fifth-generation mobile communications system” and “The researchand development project for 5G Shared Radio Unit (JPJ000254)” commissioned by The
Ministry of Internal Affairs and Communications, Japan.
REFERENCE[1] M. Minowa et al., "5G R&D Activities for High Capacity Technologies with Ultra
High-Density Multi-Band and Multi-Access Layered Cells," 2019 IEEE 89th VehicularTechnology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia, 2019, pp. 1-5[2] Takashi Seyama, Masafumi Tsutsui, Teppei Oyama, Takaharu Kobayashi, Takashi
Dateki, Hiroyuki Seki, Morihiko Minowa, Tatsuki Okuyama, Satoshi Suyama and
Yukihiko Okumura, “Study of Coordinated Radio Resource Scheduling Algorithm for 5G
Ultra High-Density Distributed Antenna Systems,” IEEE Asia Pacific WirelessCommunication Symposium (APWCS) 2016, pp.306-310, Aug. 2016.
[3] T. Kobayashi, et.al. “Function Division Type Resource Control Method for 5G Shared
Radio Unit,” Proceeding of 2022 IEICE General Conference, B-5-12, Mar. 2022 , and B5-13, B-17-5, B-5-58.
[4] T. Kobayashi, et.al., “Radio Resource Management Algorithms for Implementation inRadio Unit Sharing Systems,” IEICE Transactions on Communications J106-B (5), 327337, 2023-05-01

34

6

User Cluster-centric Approach for Cell-free Massive MIMO Systems
Fumiyuki Adachi, Tohoku University
Ryo Takahashi, Tohoku University and Panasonic System Networks R&D Lab. Co.,Ltd.

Abstract— The user cluster-centric (UCC) approach effectively improves the capacityof cell-free massive MIMO systems. This approach introduces the spatial multiplexing
technique, which involves forming user clusters of neighboring users. A certain numberof distributed antennas are associated with each user cluster to enable cluster-wisespatial multiplexing. Additionally, a partial interference suppression multiuser
minimum mean square error (MU-MMSE) multiplexing is designed to suppress only thedominant interference from the neighboring user clusters.
6.1

Introduction

Cell-free massive MIMO (CF-mMIMO), also known as distributed mMIMO, hasrecently attracted great attention as a key technology for 6G systems [1]. In CF-mMIMO
systems, a central processing unit (CPU) provides communication services to users
through the cooperative use of a fairly large number of distributed antennas deployedover the communication service area. To ensure system scalability and address the issue
of inter-user interference, the user-centric (UC) approach is considered in [2]. In the UC
approach, antenna clusters each consisting of a certain number of distributed antennas
near each user are formed, and minimum mean square error (MMSE) interference
suppression is applied. However, as the ratio of users to antennas increases, more users
cause strong interference, thereby reducing the achievable user capacity. To address thisissue, the user cluster-centric (UCC) approach is considered in [3]. This approach utilizes
spatial multiplexing by associating a certain number of distributed antennas with eachuser cluster, consisting of neighboring users, for performing cluster-wise spatial
multiplexing. Inspired by the idea of MMSE interference rejection combining (MMSEIRC) [4], partial interference suppression multiuser MMSE multiplexing (hereafter,
called partial MU-MMSE) is designed to suppress only the dominant interference fromthe neighboring user clusters.
6.2

UCC-based CF-mMIMO

A CF-mMIMO system model is illustrated in Fig. 6-1. A (>>1) distributed antennas
are connected with the CPU via an optical mobile fronthaul to provide communication
services to U ( A) single-antenna users. The UCC and UC approaches are compared inFig. 6-2. In the UCC approach, a user cluster consists of 3 users, and the minimum
required 3 antennas are associated with this user cluster as shown in Fig. 6-2 (a). Anyusers outside this user cluster are interfering users for this cluster. On the other hand,35

in the UC approach, each user is an interfering user to other users. An antenna cluster
is formed by 2 antennas, located close to each user. This is shown in Fig. 6-2 (b).
Antenna cluster
associated with user 1

Usercluster
Antennacluster
associatedwith theuser cluster

CPU

User
Antenna1

12Antennacluster
associatedwith user 2

3(a) UCC approach2

3
Antenna cluster
associated withuser 3(b) UC approach

Optical mobile
fronthaul

Fig. 6-1 CF-mMIMO system model.

Fig. 6-2 Comparison of UCC and UC
approaches.

The UCC approach takes two steps: user clustering and antenna association [3]. User
clustering is done by using a constrained K-means algorithm [5],[6]. U users in the area
are grouped into K (= U / U ) user clusters, each consisting of neighboring users, whereU  denotes the number of users in each user cluster and is determined based on thecomputing power of the CPU. Here, for simplicity, U is assumed to be an integer multipleof U  . In antenna association, a limited number A(A) of distributed antennas areassociated with each user cluster to perform cluster-wise spatial multiplexing. Antenna
association is based on the maximum pathloss criterion, and the same antenna is allowed
to be shared by different user clusters. Let M k  {1, , a, , A} with M k = A represents
the subset of distributed antennas belonging to user cluster k. For detailed user
clustering and antenna association procedures, refer to [3].
The uplink transmission model for UCC-based CF-mMIMO is illustrated in Fig. 6-3.
The baseband equivalent received signal yu of user u (= 1, , U ) belonging to user clusterk (= 1,, K ) is represented asUyu = w uH Dk h u su +  w uH D k h v sv + w uH D k n ,(1)

v =1, v  uwhere w u is the partial MU-MMSE multiplexing weight vector of size A1 , (.)H denotestheconjugate

transpose

operation,

Dk = diag( d1 ,, da ,

, dA)

with

d a = 1(0) if a  M k (otherwise) is the antenna association matrix for user cluster k, andh u (or v ) =  hu (or v )1

hu (or v )aThu (or v )A  is the channel vector. (.)T is the transpose operation andhu (or v )a is the propagation channel gain between user u (or v) and antenna a. su is the

baseband equivalent transmit signal with the transmit power pu . n is the additive noise
vector of size A1 with each element being an independent zero-mean complex Gaussianvariable having variance 2 .
236

Noisen

A antennas

CPU

a =1

…

hu

sU

H

a

Dk

…

su

…

…

h1

…

s1

…

U users

w uH

yu

hU
a=A

Fig. 6-3 Uplink transmission model for user u belonging to user cluster k.
The partial MU-MMSE weight vector w u for uplink transmission is expressed as2w u = pu   pv Dk h v h vH Dk +diag   pv Dk  E hv1
 vP
 vP

 k
 k

  Eh  Eh 
2

va

2

vA

†

T 

  +  2 D  D h , (2)
k
 k u
 



where Pk represents a set of users of user cluster k to which user u belongs and those ofinterfering users in neighboring user clusters, P k represents a set of users except Pk ,and E{.} represents the expectation operation. The interference power from user v  P kistreatedasanequivalentzero-meannoise

havingthevariance  E  h  E  h  with E  h  being the short-term averaged (i.e.,

pv Dk  E hv1


2

2

va

2

T

vA

2

va

averaged over fading) propagation channel gain between user v and antenna a. Note thatwhen U  = 1 , Eq. (2) becomes equivalent to the weight vector of the UC approach [2]. Also
note that the weight vector for downlink transmission is given by w u when assuming
time-division duplex transmission.
6.3

Performance Evaluation

The uplink user capacity is evaluated by computer simulation to compare UCC- andUC-based CF-mMIMO systems. Also compared is the computational complexity, whichis the total number of complex multiplications required for the weight computation for
all U users. For the detailed simulation procedure, refer to [3]. A antennas and U users
are randomly placed in a 1 1 normalized communication area, where A=512 and =8
U = {8,16,32, 64,128, 256,512} . In the UCC approach, the size of user cluster is set to U UCC
and a total of K=U/8 user clusters are formed. The number of antennas associated with
 = 16 . In the UC approach ( U UC
 = 1 ), the size of antennaeach user cluster is set to AUCC
cluster is set to A 'UC = 8 so that the computational complexity becomes equal to the UCC
approach. The user transmit power same (for all U users) is represented by thenormalized transmit signal-to-noise ratio (SNR)  t . This is the received SNR when thetransmitter-receiver distance is equal to a normalized distance of 1. In the simulation,37 t is set to {−60, −50, −40, −30} dB , which provides the received SNR of about 58dB higher,

i.e., {−2, 8, 18, 28} dB when the transmitter-receiver distance is equal to half the average
distance between two neighboring antennas. The propagation channel is assumed to be
characterized by distance-dependent pathloss with pathloss exponent of 3.5, lognormally distributed shadowing loss with standard deviation of 8 dB, and Rayleighfading. Assuming perfect knowledge of the propagation channel, the partial MU-MMSE
weight is obtained using Eq. (2).
The uplink user capacity Cu [bps/Hz] of user u (= 1, , U ) is calculated using thecapacity formula Cu = log 2 (1 +  u ) for a set of user locations of all U users, where  urepresents the received signal-to-interference plus noise ratio of user u and can bederived from Eq. (1). The cumulative distribution function (CDF) of the user capacity isevaluated by randomly changing a set of user locations. Fig. 6-4 compares UC and UCC
approaches in terms of the user capacity at CDF=50%. The UCC approach provides
higher capacity when  t = -30 dB and -40 dB (i.e., relatively high transmit power).
However, when  t = -50 dB and -60 dB (i.e., very low transmit power), the UCC approachprovides slightly lower capacity. This is because such low transmit power makes antenna
cooperation difficult for spatial multiplexing. But, even when  t is set to -50 dB and -60

User capacity @ CDF=50% [bps/Hz]

dB, the UCC approach provides slightly higher capacity when U gets larger.

t= -30 dB

10• Uplink• Partial MU-MMSE• A = 512
 = 8, AUCC
 = 16)
UCC (U UCC


(
U
=
1,
A
○ UC
UC
UC = 8)

□

-40

-50

5

-60

0
8

32

No. of users, U

128

512

Fig. 6-4 Capacity comparison.
The relationship between the user capacity and the computational complexity isshown in Fig. 6-5 with A ' as a parameter when U=512 and  t = -30 dB. The
computational complexity assuming the LDLH decomposition for the inverse matrix

38

operation [7] is shown in Table 6-1. The UCC approach provides higher capacity thanthe UC approach at any level of computational complexity.

User capacity @ CDF=50% [bps/Hz]6 = 512AUCC• Uplink• Partial MU-MMSE• t= -30 dB• U = 5125

 = 512
AUC

4
256

3

256

128
128

64

1

64

32

2

16
4

2

2

32

8

16
8

 = AUCC2)
UCC (U UCC

(
U
=
1)
○ UC
UC

4

□

0
1.E+04

1.E+06
1.E+081.E+10Total no. of complex multiplications

1.E+12

Fig. 6-5 Capacity versus complexity.
Table 6-1 Computational complexity.
Approach

6.4

No. of user
clusters

The total no. of complex multiplications
required for weight computation for all users

U
=U

U UC

 AUC

2 + AUC 1 K A3 − AUC2  KPk  + UC
+ AUC



2
K
3
 k =1




U

U UCC

 AUCC2 + AUCC
 1 K A3 − AUCC
2 U UCC
 K
Pk  + UCC+ AUCC
2
K3
 k =1




UC

K=

UCC

K=

Conclusion

The UCC approach utilizing partial MU-MMSE with interference suppression
capability enhances CF-mMIMO systems. It should be noted that the communication
area size of the CF-mMIMO system is relatively small due to the limited computing
power of the CPU. It is well-known that the cellular system is the most practical
approach to accommodate a large number of users over a wide communication area andthat by reducing the cell size, the spectrum utilization efficiency can be improved.
However, a straightforward significant reduction in cell size may not be realistic because
the control data traffic related to hand-off, etc. increases significantly. The following
cellular architecture combined with UCC-based CF-mMIMO could be a realistic solution[8]. Instead of reducing the cell size further than a macro-cell with a radius of several

39

100 meters, UCC-based CF-mMIMO is employed to form user clusters as user-centricvirtual small cells within each CPU-controlled macro-cell. The size of the macro-cell canbe determined by considering the computing power of each CPU.
Acknowledgements
A part of this work was conducted under “R&D for further advancement of the 5th
generation mobile communication system” (JPJ000254) commissioned by the Ministryof Internal Affairs and Communications in Japan.
REFERENCE[1] I. Kanno, K. Yamazaki, Y. Kishi, and S. Konishi, “A Survey on Research Activities for
Deploying Cell Free Massive MIMO towards Beyond 5G,” IEICE Trans. Commun.,Vol. E105-B, No.10, pp. 1107-1116, Oct. 2022.
[2] E. Björnson and L. Sanguinetti, “Scalable Cell-Free Massive MIMO Systems,” IEEE
Trans. Commun., Vol. 68, Issue 7, pp. 4247 – 4261, July 2020.
[3] R. Takahashi, H. Matsuo, S. Xia, Q. Chen, and F. Adachi, “Uplink Postcoding in User
cluster-Centric Cell-Free massive MIMO,” IEICE Trans. Commun., Vol. E106-B, No.
9, pp. 784-757, Sep. 2023.
[4] Y. Huang, W. Lei, C. Lu, and M. Berg, “Fronthaul Functional Split of IRC-Based
Beamforming for Massive MIMO Systems,” Proc. IEEE VTC2019-Fall, Honolulu,
Hawaii, USA, 22–25 Sep. 2019.
[5] P. Bradley, K. Bennett, and A. Demiriz, “Constrained K-means clustering”, Microsoft
Research Technical Report, May 2000.
[6] S. Xia, C. Ge, Q. Chen, and F. Adachi, “Cellular Structuring and Clustering forDistributed Antenna Systems,” Proc. WPMC2021, Okayama, Japan, 14-16 Dec. 2021.
[7] E. Björnson, J. Hoydis, and L. Sanguinetti, “Massive MIMO Networks: Spectral,
Energy, and Hardware Efficiency,” Foundations and Trends in Signal Processing: Vol.
11, No. 3-4, pp. 154-655, 2017.
[8] F. Adachi, R. Takahashi, H. Matsuo, S. Xia, C. Ge, and Q. Chen, “On Design Conceptof Cellular Distributed MU-MIMO for Ultra-dense RAN,” Proc. IEEE APCC2022, Jeju
Island, Korea, 19-21 Oct. 2022.

40

7
Low-Complexity User-Centric TRP Clustering Method in Downlink
Cell-Free MIMO with Regularized ZF-Based Beamforming
Kenichi Higuchi, Tokyo University of Science
Hiroki Kato, Tokyo University of Science
Takanori Hara, Tokyo University of Science
Satoshi Suyama, NTT DOCOMO, INC.
Satoshi Nagata, NTT DOCOMO, INC.
Abstract—This article investigates a computationally-efficient method for user
equipment (UE)-centric transmission reception point (TRP) clustering in downlink cellfree multi-input multi-output (MIMO) using regularized zero-forcing (RZF)-based
beamforming (BF). The proposed method selects a set of TRPs used for data transmission
for each UE (user-centric TRP cluster) based only on the average path gain information
between UEs and TRPs. Thus, no complex calculations for a downlink BF matrix areneeded for TRP clustering. Since the proposed method takes into account the BF gain
and spatial interference levels for the RZF-based BF, the achievable system-level
throughput can be enhanced compared to that for conventional path loss-based TRP
clustering. This is achieved by utilizing the average path gain information of the target
and neighboring UEs. Computer simulation results assuming a realistic partial channel
state information scenario show the effectiveness of the proposed method compared toconventional methods.
7.1

Introduction

In downlink cell-free or distributed multi-input multi-output (MIMO) [1, 2], theselection of transmission reception points (TRPs) for data transmission to each user
equipment (UE), which is referred to as user-centric TRP clustering hereafter,
significantly impacts the achievable system throughput. This paper investigates acomputationally-efficient user-centric TRP clustering method.
User-centric TRP clustering can be categorized into two approaches. The first
approach utilizes only the average path gain (or path loss) in the TRP clustering.
References [1] and [3] investigated clustering methods that determine a set of TRPs inorder of the highest average path gain between the target UE and each TRP. Thesemethods achieve TRP clustering with low computational complexity since there is noneed for complex calculations for a beam forming (BF) matrix. However, since thereceived signal and interference power levels after BF are not sufficiently consideredduring the TRP clustering, the achievable system-level throughput performance is not
significant.
The second approach utilizes the instantaneous channel state information (CSI) in
TRP clustering [4, 5]. Reference [4] investigated a TRP clustering method thatmaximizes the instantaneous channel gain of the target UE. This method is simple but
the received signal and interference power levels after BF were not sufficiently
considered. Reference [5] investigated a TRP clustering method based on the received
signal power after BF and interference to other UEs, which is referred to as the signalto-leakage-and-noise ratio (SLNR)-based method hereafter. Since the SLNR-based

41

method considers the BF gain during the TRP clustering, its achievable throughput
performance is high. However, this method suffers from a significantly high level ofcomputational complexity due to the complex BF matrix computations, such as channel
matrix inversion. In addition, in a realistic scenario where the time interval for TRP
clustering is longer than the coherence time of the wireless channel, the advantage of
employing TRP clustering based on instantaneous CSI may be restricted due to theinstantaneous fading variation during the TRP clustering interval.
We propose a low-complexity user-centric TRP clustering method for regularized zeroforcing (RZF)-based BF [3, 6]. The proposed method selects the TRP cluster for each UE
based only on the average path gain information without the need for complex
calculations for the downlink BF matrix. Since the proposed method takes into accountthe BF gain and spatial interference levels for the RZF-based BF, the achievable systemlevel throughput can be enhanced compared to that for conventional path loss-based TRP
clustering. This is achieved by utilizing the average path gain information of the target
UE and its neighboring UEs. Computer simulation results assuming a realistic partial
CSI scenario show the effectiveness of the proposed method compared to conventional
methods.
7.2

Proposed TRP Clustering Method

We consider downlink cell-free MIMO, where a set of TRPs, L, and a set of users, K,
are distributed over the system coverage of interest. Each TRP and UE are equippedwith a single antenna. All TRPs are assumed to be connected to a central processing unit
(CPU), which allows joint data transmission using an arbitrary set of TRPs for each UE.
For simplicity, we assume flat instantaneous fading in this paper. Let hk,l be theinstantaneous CSI between UE k ∈ K and TRP l ∈ L, where hk,l ~ CN(0, gk,l) and gk,ldenotes the average path gain determined by the distance-dependent loss and random
shadowing effect. The set of candidate TRPs for UE k clustering is denoted as Lk ⊆ L. We
assume that |Lk| is fixed to Lcand for all UEs and Lk comprises TRPs with the Lcand highest
average path gain between it and UE k. In this paper, a part of instantaneous CSI hk,l isassumed to be estimated and known at TRPs based on the uplink reference signal in atime division duplex (TDD) system. Let Sk ⊆ Lk be the set of TRPs that know the
instantaneous CSI of UE k. Based on [3] and [5], we define Sk as the set of TRPs whose
average path gain to UE k is higher than gk,max−ΔCSI in decibels (dB), namely
Sk = l | g k ,l  g k ,max − Δ CSI , l  L , where gk,max is the maximum average path gain between
UE k and all TRPs, and ΔCSI is a parameter that determines the range of instantaneous
CSI acquisition.
We assume RZF-based BF [3, 6] in this paper. Let Tk ⊆ Lk be the TRP cluster for UE
k. The |Tk |-dimensional channel vector of UE k, hk(Tk), is represented ash k (T k ) = [hk ,[T k ]1

hk ,[T k ]2

hk ,[T k ] ]T ,|T k |(1)

where [Tk]l represents the l-th element of Tk. Due to the partial CSI condition, hk(Tk) may
contain unknown elements. We use the channel matrix muting method in [5] todetermine the BF vector with partial CSI. The muting operation generates muted

42channel vector h k (T k ) by setting the l-th element of hk(Tk), hk ,[T ] , to zero if it is notknown at the TRP.
Let Uk(Tk) be the set of UEs other than UE k for which the instantaneous CSI to TRP
set Tk is at least partially known. Thus, Uk (T k ) = { j | S j Sk  , j  k  K } . The muted
channel vector between set of TRPs Tk and UE j ∈ Uk(Tk) is denoted by h j (T k ) . Let
H k (T k ) be the muted channel matrix with hTk (T k ) and {hTj (T k )} aligned in the row
direction. According to the principle of RZF [6], the BF vector to UE k can be obtained
from the following matrix.
k l

−1


N 
H −k (T k ) = H kH (T k )  H k (T k )H kH (T k ) +  0 I  ,
pk 


(2)

where pk and N0 are the transmission signal power allocated to UE k and the noise power,
respectively. Term  N 0 / pk I is the normalization term and ξ is a nonnegative parameterthat adjusts the magnitude of the normalization term. We note that ξ includes the effectTof path loss. When h k (T k ) is placed in the first row of H k (T k ) , the BF vector to UE kcorresponds to the first column vector of H −k (T k ) after its norm is normalized to one.
When ξ is set to zero, RZF is equivalent to pure zero-forcing (ZF), which minimizes theinterference power. When ξ is set sufficiently large, RZF is equivalent to maximum ratio
combining (MRC), which maximizes the received signal power. The performance of RZF
is between pure ZF and MRC. By selecting an appropriate ξ value for a given channel,
RZF can achieve performance close to that for the minimum mean squared error(MMSE)-based BF.
We propose a computationally-efficient TRP clustering metric suitable for RZF. The
proposed metric comprises a weighted sum of two metrics suitable for pure ZF and MRC.
These metrics are defined as the ratio of the achievable BF gain of UE k to the spatial
interference levels (leakage power) imparted to other UEs in the neighborhood when
applying pure ZF and MRC-based BF.
First, we derive a metric for pure ZF, which corresponds to RZF with ξ of zero. Pure
ZF based on the Moore-Penrose generalized inverse of the channel matrix can berecognized as a concatenation of ZF and MRC of the effective channels after ZF. Thus,
effective channel fk after BF of UE k assuming the candidate TRP cluster, T, isrepresented asfk =

|T | −|Uk (T )|i =1| hTk (T )z ik |2 =

|T | −|Uk (T )|
i =1



lT

zki ,l hk ,l

2

,

(3)

where hk ,l is the l-th element of h k (T ) , z k is the i-th normalized ZF vector that isiorthogonal to all channel vectors of other UE i ∈ Uk(T), and zk ,l is the l-th element ofz ik . The received signal power of UE k becomesi| f k |2 pk =  i =1

|T |−|Uk (T )|2z i h pk .
lT k ,l k ,l(4)

The exact computation of (4) requires the calculation of the BF matrix. The proposed
method applies the expectation operation to the received signal power to avoid complex

43

icalculation of the BF matrix ( z k ) during the metric calculation for TRP clustering, which
is given by
E[| f k |2 pk ] =  i =1

|T | −|Uk (T )|lT

E  zki ,l


2





2
hk ,l  pk = (| T | − | Uk (T ) | ) | T |  lT g k ,l pk .
(5)The sum of the estimated interference power to other UE j ∈ Uk(Lk) and the expected
noise power are calculated as1
g j ,l  g k ,l  g k ,l  pk +  N 0 .
| Uk (Lk ) | jUk ( Lk ) lT \S j
lT



(6)

The second term in (6) is the product of noise power N0 and positive coefficient parameter
α. It reflects the noise power and the interference from other TRP clusters. The proposed
TRP clustering metric, kProp.ZF (T ) , of UE k for candidate TRP cluster T assuming pureZF-based BF is obtained askProp.ZF (T ) =(| T | − | U (T ) |) | T |  g p
k

lT

k ,l

k

1


g k ,l  pk +  N 0
  g j ,l  g k ,l l| Uk (Lk ) | jUk ( Lk ) lT \S j
T


.

(7)

Based on the same idea as in the derivation of kProp.ZF (T ) , the metric for MRC-based
BF, kProp.MRC (T ) , which corresponds to RZF with sufficiently large ξ, is obtained. The
proposed metric, kProp.MRC (T ) , of UE k for candidate TRP cluster T assuming MRC-based
BF is represented askProp.MRC (T ) =

g p

lT

k ,l

k

1


g j ,l  g k ,l  g k ,l  pk +  N 0
| Uk (Lk ) | jUk ( Lk ) lT
lT



.

(8)

Finally, by taking the weighted sum of two metrics, kProp.ZF (T ) and kProp.MRC (T ) ,proposed metric kProp. (T ) of UE k for candidate TRP cluster T for RZF-based BF isobtained as(9)kProp. (T ) = kProp.ZF (T ) + (1 −  )kProp.MRC (T ) ,where β is a weight coefficient that ranges from zero to one. UE k selects its clustered
TRPs that maximize kProp. (T ) .
7.3

Numerical Results

The user throughput for the proposed method is evaluated by computer simulationand compared to that for the conventional methods. TRPs and UEs are randomly placedin a wraparound 5 × 5-square kilometer (km2) system coverage area according to the
Poisson point process. The density of TRPs, L, of 25 and 50 per km2 are tested. The UE
density is set to 10 per km2. The system bandwidth is 100 MHz, and the transmission
signal power per UE is set to 30 dBm. The distance-dependent path loss with the decay
factor of 3.76, random log-normal shadowing with a standard deviation of 8 dB, andinstantaneous Rayleigh fading are simulated as the propagation channel model. The

44

noise power density at the UE is set to −165 dBm/Hz. Term ΔCSI is set to 10 dB in thepartial CSI model. In addition to the proposed method with parameter β, the path lossbased method [1, 3] and SLNR-based method [5] are evaluated as conventional methods
for comparison. Parameter α included in the metrics of the proposed and SLNR-based
methods is set to 104. Term Lcand is set to 15 for all TRP clustering methods. Throughput
is calculated based on Shannon’s capacity formula. We tested two scenarios in terms ofthe time interval of the TRP clustering. The first scenario is referred to as fast TRP
clustering, which has an interval within a coherent interval of the channel fading. The
second scenario is referred to as slow TRP clustering, which has an interval that is much
longer than the channel coherence time interval.
Figs. 7-1(a) and 7-1(b) show the cumulative probability of the user throughput whenpure ZF (RZF with ξ of zero) is applied to the fast and slow TRP clustering scenarios,
respectively. In the path loss-based method, the maximum number of TRPs in a cluster,denoted as U, is set to 5 and 7 for L of 25 and 50, respectively. Fig. 7-1(a) shows that thepath loss-based method is significantly degraded at a low cumulative probability
compared to the other methods. This is mainly due to the fact that the path loss-based
method does not consider the degrees of freedom of the MIMO channel and the received
signal power after BF. The proposed method achieves throughput close to that of the
SLNR-based method while avoiding complex calculation of the BF matrix for TRP
clustering. This is because the proposed method performs TRP clustering based on theestimated power gain at BF considering the degrees of freedom of the MIMO channel
and the interference to other UEs in the neighborhood. Fig. 7-1(b) shows that the
proposed method outperforms the two conventional methods in the slow TRP clustering
scenario. In the SLNR-based method, which is based on the instantaneous channel
condition, the optimal TRP clustering changes with instantaneous channel fading. Hence,
the SLNR-based method tends to be degraded by the instantaneous fading variation
after TRP clustering in the slow TRP clustering scenario. Since the proposed method isbased on the average channel conditions, the proposed method is particularly effective
in a realistic slow TRP clustering scenario.
Figs. 7-2(a) and 7-2(b) show the average and 5%-outage user throughput as a functionof ξ, respectively. The slow TRP clustering scenario is assumed. The proposed method
and conventional path loss-based method are compared. For each TRP clustering method,
the average number of TRPs used per UE, NTRP, is fixed at four by adjusting the
maximum number of TRPs in the cluster. In the proposed method, β of 0.5, 1.0, and 0.0
are tested. Fig. 7-2(a) shows that the proposed method outperforms the path loss-based
method regardless of ξ and L. The β value achieving the highest throughput varies
depending on ξ. For instance, at ξ of 103 where the effect of RZF is greatest under theassumed channel conditions, the proposed method with β = 0.5 achieves the highest
performance. Fig. 7-2(b) shows that the proposed method with β = 0.5 is most effective
also in terms of the 5%-outage user throughput at ξ of 103.

45

1.0

1.0

L = 250.8

L = 50

Cumulative probability

Cumulative probability

0.8

L = 25
L = 500.6

0.6

Fast TRP clusteringξ = 0 (Pure ZF)

Slow TRP clusteringξ = 0 (Pure ZF)

0.4

0.4

0.2

0.0
0

Proposed method (β = 1.0)
SLNR-based method
U=5Path loss-based
methodU=7500
1000
User throughput (Mb/s)

Proposed method (β = 1.0)
SLNR-based method
U=5Path loss-based
method
U=7
500
1000
1500
User throughput (Mb/s)

0.2

0.0
0

1500

(a) Fast TRP clustering.
(b) Slow TRP clustering.
Fig. 7-1 User throughput distribution at ZF-based BF.

140

500

120

5%-outage user throughput (Mb/s)

Average user throughput (Mb/s)

L = 50450

L = 50

100

400
350

β = 1.0
β = 0.5
β = 0.0
Path loss-based method (U = 4)

Proposedmethod

300
250
200

L = 25

Slow TRP clustering
NTRP = 4806040

β = 1.0
β = 0.5
β = 0.0
Path loss-based method (U = 4)

Proposed method20
0

0 10-5 10-3 10-1 101 103 105 107 109
ξ

L = 25

Slow TRP clustering
NTRP = 4

0 10

-5

10

-3

10

-1

10

1

ξ

10

3

10

5

10

7

10

9

(a) Average user throughput.
(b) 5%-outage user throughput.
Fig. 7-2 User throughput distribution as a function of ξ.

7.4

Conclusion

We investigated downlink cell-free MIMO in a partial CSI scenario and proposed a
low-complexity user-centric TRP clustering method with RZF-based BF. The proposed
method selects the TRP cluster for each UE based only on the average path gain
information between UEs and TRPs. The proposed method does not require complex
calculation of the BF matrix. The proposed method considers the BF gain and spatial46

interference levels, which is different from the conventional path loss-based method.
Computer simulation results assuming a realistic partial CSI scenario show theeffectiveness of the proposed method in terms of the achievable system-level throughput
compared to the conventional path loss-based and SLNR-based methods.
REFERENCE
[1]
[2]

[3]
[4]

[5]

[6]

[7]

E. Björson and L. Sanguinetti, “Scalable cell-free massive MIMO systems,” IEEE
Trans. Commun., vol. 68, no. 7, pp. 4247-4261, Jul. 2020.
I. Kanno, K. Yamazaki, Y. Kishi, and S. Konishi, “A survey on research activities for
deploying cell free massive MIMO towards beyond 5G,” IEICE Trans. Commun., vol.
E105-B, no. 10, pp. 1107-1116, Oct. 2022.
M. Mojahedian and A. Lozano, “Subset regularized zero-forcing precoders for cellfree C-RANs,” in Proc. IEEE EUSIPCO2021, Ireland, Aug. 2021.
S. Buzzi, C. D’Andrea, A. Zappone, and C. D’Elia, “User-centric 5G cellular
networks: Resource allocation and comparison with the cell-free massive MIMO
approach,” IEEE Trans. Commun., vol. 19, no. 2, pp. 1250-1264, Feb. 2020.
Y. Oshima, A. Benjebbour, and K. Higuchi, “A novel adaptive interference admission
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and regularization,” IEEE Trans. Commun., vol. 53, no. 1, pp. 195-202, Jan. 2005.
H. Q. Ngo, et al., “Cell-free massive MIMO versus small cells,” IEEE Trans. WirelessCommun., vol. 16, no. 3, pp. 1834-1850, Mar. 2017.

47

8

Toward Practical Cell-Free MIMO Network
Koji Ishibashi, The University of Electro-Communications

Abstract— As a promising enabler for beyond 5G and 6G, cell-free multiple-input
multiple-output (MIMO) networks have been actively discussed. This article introduces
practical bottlenecks of cell-free MIMO networks and briefly overviews related work.
8.1

Introduction

Cell-free MIMO is a promising technique for beyond 5G and 6G, offering uniform
quality of communications to user equipment (UE) through full or partial cooperation
among access points (APs) controlled by a central processing unit (CPU). To clarify theconcept of cell-free networks, Fig. 8-1 illustrates four different network architectures: (a)traditional cellular network including small-cell networks, (b) cellular network with
distributed antennas, so-called distributed antenna system (DAS), (c) cell-free networkwith a single CPU, and (d) cell-free network with multiple CPUs. In traditional cellular
networks, UEs near cell-edges would suffer from interference from neighboring cells
besides the weak received power of the desired signals due to the path-loss. Also, if thenumber of antennas co-located at each base station (BS) increases, the spatial correlation
would limit the resulting capacity.

Fig. 8-1 Illustration of (a) traditional cellular network, (b) cellular network with
distributed antennas, (c) cell-free network with a single CPU, and (d) cell-free networkwith multiple CPUs (blue dashed curves indicate fronthaul links and red backhaul).
Meanwhile, if the distributed antennas are employed, this capacity limitation can be
resolved while the interference limited regime at the cell edge still holds. To overcome48this cell edge problem, the system has to treat the interference not stochastically but
deterministically. Hence, cell-free network where all the antennas are jointly controlled
by a single CPU emerged [1]. Note that, mathematically, cell-free network is equivalent
to a distributed antenna system without the other cells or small-cell configuration,
namely highly dense BS placement with a single antenna, with full cooperation among
BSs [2]. As obvious, this configuration does not have the cell-boundary, so thatinterference among different streams can be completely eliminated through spatial
precoding or combining, resulting in the uniform service quality over the network.
However, this centralized processing architecture leads to the remarkably high (oralmost impossibly high) load at the CPU. Therefore, exchanged signal reduction basedon clustering has been actively discussed [3]. Considering latency and cost for fronthaullinks between APs and CPU, the assumption of the single CPU is still not viable.
Recently, cell-free network with multiple CPUs has been studied [4]. Note that, thisarchitecture is mathematically and conceptually identical to a cellular network with
distributed antennas with full cooperation among BSs, dubbed virtual MIMO [5] or
network MIMO [6]. Although cell-free MIMO networks (or their variants) have been
discussed for an extended period, due to its demanding implementation cost such asphase/time synchronization among APs and fronthaul/backhaul deployment between
APs and the CPU, the integration of these technologies into standardization or
introduction to the market has not much progressed. This article summarizes
fundamental practical difficulties of cell-free MIMO networks and explores related
efforts aimed at addressing them.
8.2

Fronthaul and Backhaul Limitations

Every AP is connected to a CPU via a dedicated optical fiber cable called fronthaul.
Since the transmission rate of this fronthaul has to transmit complex signals at the speedof the (Nyquist) symbol period especially in case of uplink transmission. Considering thespecifications of the current standard interface called Common Public Radio Interface(CPRI), this requirement is definitely challenging, and the resulting cost to deploy thecell-free network is the main bottleneck for practical implementation. A judicious option
to alleviate this requirement is to use low-resolution analog-to-digital converters (ADCs)[7][8]. In [7], the uplink signal detection in cell-free networks subject to limited fronthaullink capacity and highly correlated channel condition has been discussed. In [8], joint
channel and data estimation (JCDE) based on Bayesian inference was proposed for the
uplink transmission of cell-free networks with limited fronthaul link capacity. Anotherapproach is a radio stripe connecting APs in daisy chain; sequentially connected APs
share the same fronthaul and power supply [9], leading to low-cost implementation ofcell-free network. Using local processing at each AP, the radio stripe can reduce the total

49

amount of data transmitted over fronthaul. This architecture however can be deployed
to specific environments such as stadiums and train stations, and larger delay isunavoidable through the daisy chain network than a star topology as illustrated in Fig
8-1(c).
Cell-free MIMO network with multiple CPUs offers increased deployment flexibility,
so that it can be considered as the most promising and practical architecture to cover alarge service area. However, this architecture necessitates a high-speed backhaul, asCPUs must exchange received signals, channel state information (CSI), and signals of
various upper layers. This information exchange poses challenges, even when employing
user-centric clustering to restrict the volume of such information. In [10], the joint designof beamforming and user-centric clustering has been investigated under the limitationof backhaul capacity, revealing the actual gain achieved through multiple CPU
cooperation.
8.3

Network-Assisted Full Duplex

Most existing contributions assume time division duplex (TDD) with the assumptionof channel duality between uplink and downlink. At the beginning of coherent
transmission block, channel estimation is performed via uplink pilot transmission. Then,the block is divided into uplink and downlink as illustrated on the left of Fig. 8-2.
However, the overhead associated with separating uplink and downlink in TDD imposes
inevitable delay, leading to inferior overall system performance and difficulty to meet all
the users' demands. Therefore, the optimal duplex design for cell-free networks is
actively discussed.
One possible solution to overcome this problem is to implement in-band full-duplex
technology at each AP. However, this technology requires remarkably complex signal
processing, making it less preferable due to a large number of required APs in the
network. Without changing the hardware of APs, network-assisted full-duplex (NAFD)
has been proposed [11][12][13][14][15], where each AP is assigned to either uplink or
downlink to enable full-duplex transmission through the whole network as illustrated
on the right of Fig. 8-2. In [12][13], optimal AP configuration and the corresponding
beamforming have been derived using convex optimization techniques, and it is shownthat optimized NAFD significantly outperforms conventional TDD in terms of the sum
spectral efficiency and user fairness. Since these papers assumed the perfect CSI in
optimization, the AP configuration using large scale fading has been further investigated[14][15]. In [14], practical user-centric clustering and downlink power control have been
proposed, where APs in a cluster for each UE must have the same transmission direction,
namely either uplink or downlink, and clusters with the same transmission direction canshare APs. NAFD-based cell-free network with this practical clustering remarkably

50

improves the sum spectral efficiency when the fronthaul capacity is limited.

Fig. 8-2 Cell-free networks based on (left) time division duplex (TDD) and (right)
network assisted full-duplex (NAFD).
8.4

Other Necessary Technologies

In this section, remaining other problems are overviewed. To initiate communications,
it is necessary to transmit a request without a grant from a BS in current cellular
networks. In [16], grant-free access for cell-free networks has been studied. Usingbilinear framework, this approach exhibits the superior performance comparable togrant-based transmission, enabling the low latency initialization.
Moreover, considering diverse use cases in beyond 5G and 6G, UEs may havecompletely different communications capability and requirements, for example thenumber of antennas and the number of data streams. In [17], cell-free network withheterogeneous assumption, in which UEs have arbitrary number of antennas and data
streams, has been studied. The proposed tensor-decomposition based beamforming
outperforms popular conventional beamforming with lower or similar complexity.
Finally, to realize the coherent transmission over APs, all the APs must synchronize
in time and phase, which is not viable, especially when the size of the network becomeslarge. Recently, mixed coherent and non-coherent transmission has been proposed [18]where only APs controlled by the same CPU are synchronized. Although this approach
mitigates the synchronization requirement, the resulting system is mathematicallysame as a DAS with flexible cells. Considering a non-negligible leap to deploy distributed
antennas, a repeater-assisted MIMO system has been proposed as an interim technology[19], enabling significant throughput performance comparable to cell-free MIMO
networks.

51

8.5

Conclusion

This article introduces the practical difficulties of cell-free MIMO networks andprovides a brief overview of papers that address these challenges. While some
remarkable contributions have successfully addressed fundamental issues, there still
exists a significant gap between theory and practice. This underscores the need for
additional research and experiments to bridge this divide.
Acknowledgements
The author extends sincere thanks to his collaborators including current and pastmembers of his laboratory for their dedication. Moreover, the author would like toexpress his sincere gratitude to Prof. Takumi Takahashi and Dr. Hiroki Iimori for theirvaluable comments.
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53