Error Control on Mobile Station Sides in Collaborative Multiple-Input Multiple-Output Systems

Abstract

Collaboration between mobile stations (MSs) may lead to a new form of wireless communication. Collaboration on MS sides considerably improves signal detection performance, especially for multiple-input multiple-output (MIMO) transmission systems. In this study, a novel detector collaboration system is proposed to reduce the traffic volume on inter-MS collaboration links. Multiple MSs in the immediate vicinity are used to collaboratively decode the MIMO signals received from a base station. Furthermore, we consider a distributed detection system, in which multiple detection MSs decode the MIMO signals independently. Multiple decision results are exploited to improve the error performance. Residual interference coefficients were used to finalize the decision results. The error ratio performance and the traffic volume over collaboration wireless links were compared with those of two combining schemes through computer simulations and field experiments. The results revealed that the proposed error control scheme on mobile station sides offers a better tradeoff between the error performance and the traffic volume on the collaboration wireless links.

Full text

Received January 11, 2022, accepted February 19, 2022, date of publication March 3, 2022, date of current version March 11, 2022.
Digital Object Identifier 10.1109/ACCESS.2022.3156604
Error Control on Mobile Station Sides
in Collaborative Multiple-Input
Multiple-Output Systems
HOKUTO TAROMARU 1, (Student Member, IEEE), HIDEKAZU MURATA 1, (Member, IEEE),
TOSHIRO NAKAHIRA2, DAISUKE MURAYAMA2, (Member, IEEE), AND TAKATSUNE MORIYAMA2
1Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
2NTT Access Network Service Systems Laboratories, NTT Corporation, Yokosuka 239-0847, Japan
Corresponding author: Hidekazu Murata (murata@i.kyoto-u.ac.jp)
ABSTRACT Collaboration between mobile stations (MSs) may lead to a new form of wireless commu-
nication. Collaboration on MS sides considerably improves signal detection performance, especially for
multiple-input multiple-output (MIMO) transmission systems. In this study, a novel detector collaboration
system is proposed to reduce the traffic volume on inter-MS collaboration links. Multiple MSs in the
immediate vicinity are used to collaboratively decode the MIMO signals received from a base station.
Furthermore, we consider a distributed detection system, in which multiple detection MSs decode the MIMO
signals independently. Multiple decision results are exploited to improve the error performance. Residual
interference coefficients were used to finalize the decision results. The error ratio performance and the traffic
volume over collaboration wireless links were compared with those of two combining schemes through
computer simulations and field experiments. The results revealed that the proposed error control scheme
on mobile station sides offers a better tradeoff between the error performance and the traffic volume on the
collaboration wireless links.
INDEX TERMS Cooperative communications, collaborative communications, HARQ, distributed MIMO.
I. INTRODUCTION
Collaboration on a mobile station (MS) sides can con-
siderably improve signal detection performance [1]–[4].
Especially, in multiple-input multiple-output (MIMO) trans-
mission systems, the increased number of antennas on the
MS side can be used to enhance the channel capacity [5]
and detection performance [6], [7]. In collaborative MIMO
detection systems, collaborative MS groups that consist of
MSs in the immediate vicinity are formed to collabora-
tively decode the MIMO signals transmitted from a base
station (BS).
We focus on physical-layer collaboration to improve trans-
mission performance from the BS to the collaborative MS
group. An MS in the collaborative MS group forwards the
signal waveform received from the BS to an MS in charge
of detection (detection MS). In this scenario, the number
of receive antennas on the MS side is considerably higher
The associate editor coordinating the review of this manuscript and
approving it for publication was Mauro Fadda .
than that of a single MS. Therefore, the channel capacity and
detection performance [7], [8] can be enhanced.
Wireless links connecting MSs for collaboration are cru-
cial for this collaborative detection system. Furthermore, the
wireless links should be high speed and low latency to provide
a physical-layer collaboration of MSs in the group. Thus,
a higher-frequency band transmission technology should be
preferred (e.g. 5G NR). Increasing the number of MSs in the
collaborative MS group offers a superior detection perfor-
mance [9]. However, more collaboration traffic is incurred
in the collaborative wireless links because of the increased
number of forwarded waveforms.
The authors in [10] focused on the broadcast of wireless
media and proposed distributed detection schemes to improve
detection performance with the same number of forwarded
waveforms. In these schemes, a neighboring MS (second-
detection MS) overhears the forwarded waveforms and then
independently decodes the MIMO signals transmitted from
the BS. If a unique set of signals is used in each detection
MS, the decision results differ. Therefore, MIMO detection
performance can be improved by appropriately utilizing the
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H. Taromaru et al.: Error Control on MS Sides in Collaborative MIMO Systems
results of multiple detection MSs. Notably, overhearing does
not require additional traffic. The traffic volume of the deci-
sion result exchange is generally lower than that of waveform
forwarding.
In the distributed detection scheme [10], detection MSs
independently decode the MIMO signal streams from the
BS. Each detection MS uses the waveforms forwarded by
the helper MSs as well as its own received signal from the
BS. Thus, distinct sets of MIMO received signals are used
in each detection MS, delivering different decision results.
This difference can be exploited to improve detection per-
formance. In [10], every detected bit sequence (DBS) was
gathered through collaboration links to determine the most
reliable DBS for each stream. This scheme always imposes
additional traffic on the collaboration wireless links to gather
the decision results from other detection MSs.
We improve our prior approach [10] and propose an
error-control scheme with reduced collaboration traffic on
the MS side. In the proposed scheme, multiple detection
MSs are used to enhance detection performance, similar to
the approach in [10], while keeping the collaboration traf-
fic volume as small as possible by using on-demand DBS
exchange based on reliability information [11]. For simplic-
ity, we assume that one of the detection MSs is a target
information sink. The proposed scheme operates as follows:
First, a target MS checks the reliabilities of its own DBSs.
Provided its own DBS of a stream is of low reliability, the
target MS requests a DBS of the stream along with its reli-
ability information from another detection MS. This process
is repeated until the DBSs of all detection MSs are examined
or the reliability satisfies the criterion. In this study, the
error performance is investigated through extensive computer
simulations and experimental evaluations.
The major contributions of this paper are as follows:
1) An on-demand reliability-based MS-side error-control
scheme that utilizes multiple detection MSs is
described.
2) The frame error ratio (FER) performance of the
proposed scheme is compared with those of the
majority combining (MC) scheme and log-likelihood
ratio (LLR) combining schemes.
3) The FER performance comparison is performed assum-
ing correlated fading channels.
4) The performance advantage of the proposed scheme is
demonstrated using the recorded signals of the mea-
surement campaign.
This study expanded the results of [11]. The points 2), 3),
and 4) present new results. The remainder of this paper is
organized as follows. The system model of the collaborative
MIMO systems is described in Section II. Section III intro-
duces an iterative equalization and detection scheme in the
frequency domain. The proposed reliability-based MS-side
error-control scheme is described in Section IV. Section V
presents the computer simulation results. The experimental
FIGURE 1. Distributed detection system with on-demand DBS requests.
Helper MSs forward the received signals from the BS to the detection
MSs. The target MS requests another decision result from other detection
MSs, if necessary.
setup and experimental results are discussed in Section VI.
Finally, the conclusions are presented in Section VII.
II. SYSTEM MODEL
A system model of an MS-side error-control scheme in a
distributed MIMO detection system is displayed in Fig. 1.
In this system, Mindependent MIMO signal streams are
transmitted from the BS to a collaborative MS group (H∪D).
The MSs in this group are equipped with a single antenna
each. In the collaborative MS group, helper MSs (H,h= |H|)
forward the signals to detection MSs (D,d= |D|) through
collaboration wireless links after receiving the signals from
the BS. A high-speed low-latency wireless communication
technique operating in high-frequency bands is suited for the
proposed technique.
In the collaborative MS group, the detection MSs receive
the forwarded signals as well as the signal transmitted from
the BS. Using these signals, each detection MS independently
decoded the MIMO signal streams from the BS. The decision
results of each detection MS may differ from those of the
other detection MSs because each detection MS received sig-
nal for detection. For simplicity, we assumed that the Msignal
streams are transmitted to a single-detection MS (target MS).
Thus, the target MS is an information sink, and ddecision
results are available in the collaborative MS group. To take
advantage of the multiple decision results, the reliability of
the decision results is employed as a result selection metric
in [10]. We proposed an error-control method on the MS side
to reduce the traffic volume over collaboration wireless links.
In the proposed technique, the target MS requests results
from another detection MS when the target MS finds that:
the reliability of the current results is not satisfactory.
III. DETECTION SCHEME
A. ITERATIVE MIMO DETECTION IN THE FREQUENCY
DOMAIN
The frequency-domain (FD) iterative MIMO detection
scheme is illustrated in Fig. 2. This detection scheme
iteratively decodes single-carrier MIMO signals using FD
soft cancellation followed by a minimum mean-squared
error (MMSE) filter [12], [13]. In this detector, the L=
h+1 received signals available at the detection MSs, namely
the own received signals and the hsignals forwarded from the
helper MSs, are used. The iterative detection scheme includes
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FIGURE 2. Frequency-domain iterative MIMO detection scheme.
three processes: i) Interference (other streams) cancellation
using soft replicas (after the first iteration), ii) MMSE-based
MIMO signal separation and equalization, and iii) soft decod-
ing with belief propagation (BP).
The received kth (1 ≤k≤K) symbol is denoted
by yl(k) (1 ≤l≤L). Next, the received signal vector
y(k)=[y1(k),y2(k),...,yL(k)]T∈CL×1is expressed by
the following,
y(k)=Hx(k)+n(k),(1)
where the superscript (.)Tdenotes the transpose operation,
H∈CL×Mis a channel matrix in which the channel vari-
ations are negligible over the K-symbol period, and x(k)∈
CM×1and n(k)∈CL×1are the transmitted symbol vector
and additive white Gaussian noise vector, respectively. The
FD MIMO received signal vector y(f)∈CL×1is equalized
in the MMSE filters wm(f)∈CL×1as follows:
˜xm(f)=wH
m(f)y(f),(2)
wm(f)= X
i
hi(f)hH
i(f)+σ2IL!−1
hm(f),(3)
where superscript (.)Hdenotes the Hermitian transpose and
hi(f)∈CL×1represents the ith column of the FD channel
matrix H(f). σ2is the noise variance, and ILis the L×L
identity matrix.
The MMSE filter output signals ˜
x(k)∈CM×1in the time
domain (TD) are fed into the BP decoder. In the BP decoder,
LLRs L(cm,k,i) are calculated iteratively (inner iteration),
where cm,k,idenotes the ith bit of the kth symbol in the mth
signal stream.
Provided that quadrature phase-shift keying (QPSK)
modulation is used, soft-decision symbols ˆ
x(k)=
[ˆx1(k),ˆx2(k),...,ˆxM(k)]T∈CM×1are generated in an FD
soft replica generator as follows:
ˆxm(k)=1
√2tanh L(cm,k,1)/2
+1
√2tanh L(cm,k,2)/2√−1.(4)
The TD soft-decision symbols ˆ
x(k) are converted into FD
signals ˆ
x(f)=[ˆx1(f),ˆx2(f),...,ˆxM(f)]T∈CM×1. Subse-
quently, the soft-decision replicas in FD ˆ
ym(f)∈CL×1are
given by the following expression:
ˆ
ym(f)=hm(f)ˆxm(f).(5)
The soft cancellation and MMSE MIMO signal separation
and equalization can be expressed as follows:
˜xm(f)
=wH
m(f)
y(f)−X
i6=mˆ
yi(f)
,(6)
wm(f)
=
hm(f)hH
m(f)+X
i6=m
βihi(f)hH
i(f)+σ2IL

−1
hm(f),
(7)
where wm(f)∈CL×1is an MMSE filter with residual inter-
ference coefficients βm(0 ≤βm≤1) as presented below [12],
[14]:
βm=


0,Parity-check satisfied
1−1
KPkˆxm(k)
2,otherwise. (8)
Thus, βmis set to 0 by satisfying all the parity-check equa-
tions of the mth stream based on hard decisions on a posterior
LLRs. The coefficient βmindicates the average residual sym-
bol interference after cancellation [14].
The aforementioned processes, namely (4)–(8) and soft
decoding, are repeated as the outer iteration.
B. EARLY STOPPING
Early stopping (ES) can reduce the computational complexity
of iterative signal processing. In [15], it was revealed that
the ES can improve the error performance in experimental
performance evaluation. In this study, the residual interfer-
ence coefficients given by (8) are exploited as a stopping
metric. The outer iteration ended on satisfying the following
inequality.
M
X
m=1
βm≤ε. (9)
The εis an iteration control threshold. Therefore, εcan be
adjusted to reduce the number of outer iterations, resulting in
reduced computational complexity.
IV. ERROR-CONTROL SCHEME ON THE MS SIDE
A. RESIDUAL INTERFERENCE COEFFICIENTS BASED
SELECTION SCHEME
In the proposed MS-side error-control scheme, the decision
results of multiple detection MSs are used to improve detec-
tion performance. The residual interference coefficient (RIC)
βmis used as reliability information. Because this coefficient
is inherent in FD iterative equalization, no increase in com-
putational complexity is observed. The most reliable DBS
is determined as the final bit sequence based on βm. The
error control on the MS side is performed on each stream
independently.
The target MS checks its own residual interference coef-
ficient, βm. If βmis larger than the threshold value β0, the
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target MS sends a request to other detection MSs. The request
packet contains βm∀mto avoid transferring a less reliable
DBS. Specifically, the reliability metric of the mth stream in
detection MSiis represented by βi,m. When the detection MSi
receives the request packet, the detection MSitransfers its
own DBS to the target MS if βi,m< βh+1,mholds, where
MS(h+1) is the target MS. The detailed process of this
MS-side error-control scheme is described in Algorithm 1.
Algorithm 1 MS-Side Error-Control Scheme Based on
Residual Interference Coefficients
Input: M: number of streams, βi,m:βmat the detection MSi,Si,m:
DBS of the mth stream at detection MSi,
Output: Sm: DBS of the mth stream
Initialization:Sm=Sh+1,m∀m
1: for m=1 to Mdo
2: i=h+2
3: while βh+1,m> β0do
4: request Si,mand βi,mfrom the detection MSi
5: if βi,m< βh+1,mthen
6: detection MSitransfers Si,mand βi,mto the target MS.
7: Sm=Si,m
8: βh+1,m=βi,m
9: end if
10: i=i+1
11: end while
12: end for
13: return Sm∀m
V. COMPUTER SIMULATIONS
A. SYSTEM DESCRIPTION
The error ratio performance with the collaboration traf-
fic volume was investigated through computer simulations.
The BS employed four transmit antennas and transmitted
M=4 independent signal streams. The transmission
packet is composed of a 39-symbol long orthogonal training
sequence (TS) including a seven-symbol cyclic extension,
four-symbol cyclic prefix (CP), and QPSK-modulated data
sequence (DATA) of 192 symbols (low-density parity-check
code, rate 1/2). The TSs are assigned to the signal streams in
a deterministic but arbitrary manner.
In the collaborative group, we have six (h+d=6) MSs,
where several cases of hand dcombinations are examined.
The frame structure is displayed in Fig. 3. The Mtransmitted
packets from the BS are received by the six MSs. The h
helper MSs forwarded the received signals to the ddetection
MSs. The collaboration wireless links are supposed to be
error free for simplicity because the transmission distance of
the collaboration wireless links are assumed to be short. The
threshold β0is assumed to be 0, while the channel matrices
are estimated using the least square method. The numbers of
outer and inner iterations were three and twelve, respectively.
B. SPATIAL CORRELATION
The effect of spatial correlation in the channel from the BS to
the MSs on the error performance was studied. The transmit
and receive correlation matrices T=E[HHH] and R=
E[HHH] in the Kronecker model were formed according to
FIGURE 3. Frame structure of the MS-side error-control scheme. The
helper MSs forward the packets received from the BS to the detection
MSs. The detection MSs, except for the target MS, transmit their DBS(s) to
the target MS upon request.
the exponential model of [16], where E[·] is the expectation
operator. The channel matrix with spatial correlation can be
obtained as follows:
H=1
√κR1/2GT1/2H
,(10)
where Gis a pseudo-random L×Mmatrix whose elements
follow an independently and identically distributed (i.i.d.)
complex Gaussian process with zero mean and unit variance.
The R1/2and T1/2are positive-semidefine matrices. κis the
trace of Rand T. The spatial correlation matrices are given
by exponential correlation matrices,
[T]i,j
N=[R]i,j
M=ρ|i−j|,(11)
where [·]i,jdenotes the element in the ith row and the jth
column of the matrix, and the parameter ρis a real number
ranging from 0 to 1.
C. TWO COMBINING SCHEMES FOR PERFORMANCE
COMPARISON
The performance of the error-control scheme on the MS side
with residual interference coefficients was compared with
those of two combining schemes, namely an MC scheme [17]
and an LLR combining scheme [18]. In the MC scheme, the
target MS receives DBSs from other detection MSs, followed
by a bit-by-bit majority combining using three DBSs. In the
LLR combining scheme, the target MS receives LLR values
of all bits from other detection MSs, and then hard decisions
are made on the sum of the LLR values.
D. TRAFFIC OVER COLLABORATION LINKS
The traffic volume over the collaboration wireless links is
measured in units of DBS (192 bits). The number of trans-
ferred DBS (TDBS) is incremented by one when a detection
MS transfers a DBS of a single stream to the target MS. The
traffic volume of waveform forwarding is measured in TDBS
by assuming that the I and Q components of the received sig-
nal are quantized in eight bits each. Thus, forwarding a single
received packet incurs 16 TDBS. Furthermore, a simple n-
bit quantization is assumed to transfer the LLR values of a
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FIGURE 4. Average FER performance versus average SNR ¯
γof MC, LLR,
RIC over the frequency-flat fading channel. The number of helper MSs his
three. No quantization is applied for the LLR scheme.
packet. Therefore, transferring LLR values of a single stream
corresponds to nTDBS.
E. COMPUTER SIMULATION RESULTS
The system that consists of ddetection MSs and hhelper MSs
is denoted as dDethH. The normalized maximum Doppler
frequency fDTswas 6.4×10−5throughout the computer
simulations. Figs. 4and 5display the average FER per-
formance of the proposed scheme in comparison with two
combining schemes LLR and MC. The collaborative group
included three detection MSs and three helper MSs (3Det3H,
d=3,h=3).
The average FER performance as a function of the aver-
age signal-to-noise ratio (SNR) ¯γover frequency-flat i.i.d.
Rayleigh fading channels is displayed in Fig. 4. The average
FER performance of a single-detection MS scheme (1Det3H)
is also displayed in this figure for comparison purposes.
MS-side error control is not performed in 1Det3H because
a single DBS is available for each stream. The average FER
performance of the proposed RIC scheme was considerably
improved compared with that of 1Det3H. This result revealed
that the RIC scheme can select a superior DBS using the
residual interference coefficients. The RIC scheme achieved
a better average FER performance than the MC and LLR
schemes.
The average FER performance over the frequency-selective
channel is displayed in Fig. 5. As a channel model, a symbol-
spaced four-tap i.i.d. Rayleigh fading with a flat power-delay
profile is considered. The average FER performance of all
schemes considerably improved because of the frequency
diversity effects from FD MMSE equalization. The RIC
scheme achieves the best FER performance in all schemes.
These results reveal that the proposed RIC scheme outper-
formed the combining schemes in both frequency-flat and
frequency-selective fading channels.
The average FER performance of the MS-side error-
control schemes that included two detection MSs and
four helper MSs (2Det4H) was studied. Fig. 6illustrates
the average FER performance over frequency-flat i.i.d.
FIGURE 5. Average FER performance versus average SNR ¯
γof MC, LLR,
RIC over frequency-selective fading channel. The number of helper MSs h
is three. No quantization is applied for the LLR scheme.
FIGURE 6. Average FER performance versus average SNR ¯
γof MC, LLR,
RIC over frequency-flat fading channel. The number of helper MSs his
four. No quantization is applied for the LLR scheme.
Rayleigh fading channels. Compared with Fig. 4, the average
FER performances of 1Det4H and the LLR scheme were
improved because the five received signals were used by
FD MMSE equalization. The proposed RIC scheme outper-
formed 1Det4H scheme.
Identifying the performance degradation of a multiple-
detector system in a spatially correlated channel is a critical
consideration. The average FER performance over spatially
correlated frequency-flat Rayleigh fading channels is pre-
sented in Fig. 7. The average SNR ¯γis 13 dB. This figure
reveals the average FER performance of the RIC scheme,
two combining schemes, and 1Det in both the h=3 and
h=4 cases. The performance of the MC scheme is presented
for the h=3 case. As expected, spatial correlation exhibited
a negative effect on the average FER performance in both
cases. The average FER performance of the RIC scheme was
superior to that of 1Det in both cases, even over spatially
correlated channels.
Various combinations of dand hare displayed in
Figs. 8and 9. In Figs. 8and 9, frequency-flat channels with
¯γ=12 dB, and frequency-selective channels with ¯γ=8 dB,
respectively, were assumed. The vertical axis represents the
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FIGURE 7. Average FER performance versus spatial correlation over
frequency-flat fading channel with spatial correlation. The average SNR ¯
γ
was 13dB. No quantization is applied for the LLR scheme.
FIGURE 8. Average FER performance versus average TDBS over
frequency-flat i.i.d. Rayleigh fading channels. The average SNR ¯
γwas
12 dB.
average FER, and the horizontal axis represents the average
TDBS. As observed in both figures, the proposed RIC scheme
achieved superior or comparable FER performance to the
LLR scheme with the same combination of dand h. The
average TDBS of the RIC scheme is always less than that of
the LLR scheme. The RIC scheme was superior to the 1Det
scheme in terms of the average FER performance of the RIC
and 1Det schemes for the same h, whereas the TDBSs were
almost the same. The average FER performance and average
TDBS of the MC scheme were between those of the 1Det
and LLR schemes. Both figures included the performance of
the 1Det scheme with five helpers (1Det5H) as a benchmark.
1Det5H performs the best in terms of the average FER per-
formance at the expense of the increased traffic volume.
VI. FIELD EXPERIMENTS
A. EXPERIMENTAL SETUP
As displayed in Fig. 10, four packets are transmitted simul-
taneously from the BS to six MSs every 50 ms. The packet
included a 15-symbol synchronization word for timing detec-
tion, an orthogonal TS, a 15-symbol control block (CTRL),
4-symbol CP, and a 192-symbol QPSK-modulated data
FIGURE 9. Average FER performance versus average TDBS over
frequency-selective i.i.d. Rayleigh fading channels. The average SNR ¯
γ
was 8 dB.
FIGURE 10. Frame structure of measurement campaign.
TABLE 1. Major parameters of the field measurements.
block. The TS and DATA were the same as those used in
computer simulation. The data bits were successively drawn
from a pseudo-random sequence generator. The symbol rate
was 312.5 kilo symbols per second (ksps) because of the
bandwidth limitation of the radio license. The frequency and
timing synchronizations were managed by 10 MHz signals
and 1-pulse-per-second signals of global-positioning-system-
based oscillators. The number of outer iterations was con-
trolled by the ES, in which the iteration control threshold ε
was 0.01. The noise variance was adjusted to provide the best
performance. Table 1reveals the major parameters of the field
measurements.
The carrier frequency and transmission power were
427.2 MHz and 1W per antenna, respectively. As displayed
in Fig. 11, four BS antennas were located 25.5 m above the
ground and placed in a 3.8 m ×2.5 m rectangle located on the
rooftop of the building on the campus of Kyoto University.
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FIGURE 11. BS antennas are arranged in a rectangle on the top of a
building.
FIGURE 12. MS antennas mounted on the vehicle. The antennas are
arranged in a uniform circular array.
FIGURE 13. Location of the base station and route of the vehicle
mounted with the antennas.
The BS antennas were 5.8 dBi omnidirectional in the hori-
zontal plane. Six MS receive antennas were installed on the
roof of the vehicle (2.1 m height), as displayed in Fig. 12.
The receiving antennas were a λ/4 omnidirectional monopole
antennas.
To record the received signals, the vehicle moved twice (i.e.
trial 1 and 2) along the Imadegawa-dori and Shirakawa-dori
streets in Kyoto city, as displayed in Fig. 13.
B. EXPERIMENTAL RESULTS
Fig. 14 illustrates the FER performance averaged over the
trials 1 and 2. The horizontal axis is the average TDBS.
FIGURE 14. Average FER performance versus the average TDBS of four
schemes with h=2,3,4,5. The results are averaged over two trials.
The number of helper MSs ranged from two to five because
six MSs were considered in this measurement. The helper
MSs and the detection MSs were selected in the numerical
order; for example, MS1 and MS2 are helpers for the two-
helper case, and MS1 through MS5 are helpers for the five-
helper case. The detection MSs are the rest of the MSs.
As displayed in this figure, the RIC scheme achieved
almost the best FER performance among all schemes with
the same number of helper MSs h. Furthermore, comparing
the RIC scheme and the 1Det scheme with the same number
of hrevealed that the average TDBS of the RIC scheme
was marginally larger than that of the 1Det scheme. This
increase in the average TDBS becomes negligible in the
low FER region. In contrast to the previous results of the
computer simulation, the 1Det5H scheme performance was
almost the same as the RIC scheme of 2Det4H. The cause of
this phenomenon requires further investigation.
VII. CONCLUSION
An MS-side error-control scheme that incorporates reliability
metrics on a per-stream basis was proposed in this study.
To demonstrate the effectiveness of the proposed scheme, the
FER performance determined by computer simulations and
field experiments were compared with those of two combin-
ing schemes. The proposed scheme can effectively improve
FER performance by using residual interference coefficients
to select better bit sequences. The proposed scheme that
considers spatial correlation in fading channels exhibited a
similar relationship between FER performance and spatial
correlation. Furthermore, the proposed scheme suppressed
the collaboration traffic volume by using multiple detection
MSs, and the differences in the DBSs of multiple detection
MSs are exploited with residual interference coefficients.
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HOKUTO TAROMARU (Student Member, IEEE)
received the B.E. degree from Ritsumeikan Uni-
versity, Shiga, Japan, in 2021. He is currently pur-
suing the M.E. degree with the Graduate School of
Informatics, Kyoto University, Kyoto, Japan.
HIDEKAZU MURATA (Member, IEEE) received
the B.E., M.E., and Ph.D. degrees in electronic
engineering from Kyoto University, Kyoto, Japan,
in 1991, 1993, and 2000, respectively.
In 1993, he joined the Faculty of Engineering,
Kyoto University. From 2002 to 2006, he was an
Associate Professor with the Tokyo Institute of
Technology. He has been with Kyoto University,
since October 2006, where he is currently an Asso-
ciate Professor with the Department of Commu-
nications and Computer Engineering, Graduate School of Informatics. His
research interests include signal processing and hardware implementation,
particularly for application to cooperative wireless networks.
Prof. Murata received the Young Researcher’s Award from IEICE of
Japan, in 1997; the Ericsson Young Scientist Award, in 2000; the Young
Scientists’ Prize of the Commendation for Science and Technology by the
Minister of Education, Culture, Sports, Science and Technology, in 2006;
the Best Paper Award of IEICE, in 2011 and 2013; and the IEEE ICC Best
Paper Award, in 2014. He is a member of IEICE and ITE.
TOSHIRO NAKAHIRA received the bachelor’s
degree in maritime safety from the Japan Coast
Guard Academy, in 2009, and the M.I. degree
in informatics from Kyoto University, in 2012.
In 2012, he joined the NTT Network Innovation
Laboratories. He is currently working with the
NTT Access Network Service Systems Labora-
tories. His research interests include natural area
design and dynamic control techniques using mul-
tiple wireless access. He is a member of IEICE.
He received the Best Research Award of the Fourth Basic Course Work-
shop of the Institute of Electronics, Information and Communication Engi-
neers (IEICE) Communication Quality, in 2017, and the Young Engineer
Award from IEICE, in 2019.
DAISUKE MURAYAMA (Member, IEEE)
received the B.E., M.E., and Ph.D. degrees
in electrical engineering from Keio University,
Kanagawa, Japan, in 2005, 2007, and 2015,
respectively. Since 2007, he has been with the NTT
Access Network Service Systems Laboratories,
NTT Corporation, Kanagawa, where he works on
wireless and optical access networks and systems.
His current research interests include 5G NR and
other unlicensed wireless access networks, passive
optical networks, system control, and measurement engineering. He is a
member of IEICE.
TAKATSUNE MORIYAMA received the B.E. and
M.E. degrees from the Muroran Institute of Tech-
nology, Japan, in 1991 and 1993, respectively.
He joined NTT, in 1993. Since 1999, he has been
working with the NTT Communications, where he
led network service development and operations
for corporate customers. He has been in his current
position, since July 2019.
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