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PAPER

16 × 16 MIMO Testbed for MU-MIMO Downlink Transmission
Kentaro NISHIMORI†a) , Riichi KUDO†† , Naoki HONMA††† , Members, Yasushi TAKATORI†† , Senior Member,and Masato MIZOGUCHI†† , Member

SUMMARY
Multi-user multiple input multiple output (MU-MIMO)
systems have attracted much attention as a technology that enhances thetotal system capacity by generating a virtual MIMO channel between abase station and multiple terminal stations. Extensive evaluations are stillneeded because there are many more system parameters in MU-MIMO thanin single user (SU)-MIMO and the MU-MIMO performance in actual environments is still not well understood. This paper describes the featuresand effectiveness of a 16 × 16 MU-MIMO testbed in an actual indoor environment. Moreover, we propose a simple adaptive modulation scheme
for MU-MIMO-OFDM transmission that employs a bit interleaver in thefrequency and space domains. We evaluate the frequency efficiency by obtaining the bit error rate of this testbed in an actual indoor environment. We
show that 16×4×4-user MU-MIMO transmission using the proposed modulation scheme achieves the frequency utilization of 870 Mbps and 1 Gbps
(respective SNRs: 31 and 36 dB) with a 20-MHz bandwidth.
key words: multiuser MIMO, adaptive modulation scheme, OFDM, eigenvalue, bit interleaver

1.

Introduction

The growing popularity of mobile phone systems and wireless LANs has set the demand for achieving broadband wireless transmission within a limited frequency band. Multiple
input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems have been developed because they promise to increase the channel capacity compared to single input single output (SISO) systems [1]–[4].
Moreover, multi-user MIMO (MU-MIMO) systems haverecently attracted much attention as a technology that enhances the total system capacity by generating a large virtual
MIMO channel between a base station and multiple terminal
stations [5], [6].
Many previous studies concerning MU-MIMO systems
focused on the theoretical derivation of the channel capacity [7] and performance evaluation based on computer simulations [6]. Several downlink beamforming methods andscheduling algorithms were proposed for MU-MIMO systems [8]–[12].
In order to incorporate MU-MIMO technologies into
Manuscript received May 20, 2009.
Manuscript revised September 23, 2009.
†
The author is now with the Faculty of Engineering, Niigata
University, Niigata-shi, 950-2102 Japan.
††
The authors are with NTT Network Innovation Laboratories,
NTT Corporation, Yokosuka-shi, 239-0847 Japan.
†††
The author is now with the Faculty of Engineering, Iwate University, Morioka-shi, 020-8551 Japan.
a) E-mail: nishimori@m.ieice.org
DOI: 10.1587/transcom.E93.B.345

commercial systems, many system parameters must be determined. The optimal transmission control methods andantenna configurations are complex in comparison to thosefor single user (SU)-MIMO applications. The schedulingalgorithm, which determines the number of data streams peruser and the allowed number of simultaneously communicating users, largely depends on factors such as the propagation environment, the number of connected users, and thenumber of transmitter and receiver antennas. However, theperformance of MU-MIMO technologies considering actual
system parameters is not well understood.
In this paper, we describe a MU-MIMO testbed thatcan be used to evaluate a maximum of 16 × 16 MIMO transmissions. This testbed deals with OFDM signals according to the IEEE802.11a standard format [13], and it can beused to evaluate various system parameters such as the number of antennas, users, transmission control methods, andscheduling algorithms. In this paper, we first describe thehardware configuration and communication flow of the developed testbed.
In order to actualize the downlink beamforming
scheme, an adequate modulation scheme must be implemented according to the transmission quality for each data
stream. The optimal modulation scheme and coding rate
for each subcarrier are ideally determined when the OFDM
transmission with error correction is considered. However, the calculation complexity level increases when trying
to achieve the ideal adaptive modulation strategy. Hence,
we propose a simple adaptive modulation scheme for MUMIMO-OFDM transmission that employs a bit interleaver
in the frequency and space domains. The proposed scheme
enables efficient adaptive modulation by calculating the effective number of eigenvalues and preparing the relationshipbetween the average Signal to Noise Ratio (SNR) of the effective eigenvalues and the pairs comprising the modulation
scheme and coding rate.
Finally, we show the experimental results in an actualindoor environment in order to evaluate the frequency utilization for a downlink beamforming scheme that is implemented in the MU-MIMO testbed. We clarify that the MUMIMO transmission using the simple adaptive modulation
scheme achieves the frequency utilization of 43.5 bits/s/Hz(870 Mbps) and 50 bits/s/Hz (1 Gbps) when the SNRs are 31
and 36 dB, respectively.
The rest of this paper is organized as follows. Section 2 outlines the 16×16 MU-MIMO testbed. The measure-c 2010 The Institute of Electronics, Information and Communication Engineers
Copyright IEICE TRANS. COMMUN., VOL.E93–B, NO.2 FEBRUARY 2010

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ment conditions and proposed adaptive modulation scheme
are described in Sect. 3. Section 3 also presents experimental results on the frequency utilization using the MU-MIMO
testbed.
2.

Configuration of 16 × 16 MU-MIMO Testbed

2.1 Hardware Configuration
The configuration of the experimental testbed for the MUMIMO-OFDM transmission is shown in Fig. 1. The main
parameters are given in Table 1. The center frequency ofthis testbed is 4.85 GHz. To evaluate the effect of the number of antennas and users in the MU-MIMO channels, thetotal numbers of transmitters and receivers are 16, respectively. The 16 × 16 MU-MIMO-OFDM transmission can beevaluated using a bandwidth of 100 MHz at maximum. Although the MU-MIMO scenario in Fig. 1 represents the casein which there are four receiver antennas and four users, it
should be noted that various combinations for the number ofantennas and users for both transmitters and receivers can beevaluated in this testbed. For example, if the number of receiver antennas per user is set to two, then we can deal with

MU-MIMO transmission for eight users.
2.2 Communication Flow
The communication flow of this testbed is described in thissubsection. Table 2 represents the signal parameters that areused in this study. The frame format is shown in Fig. 2. At
the transmitter, we employ convolutional coding with theconstraint length of seven. These bits are interleaved forboth data steams and subcarriers, and divided into streams
corresponding to the modulation scheme. Next, these bits
are modulated and multiplied with the transmission weightvectors. Each stream uses the same modulation scheme over
all the sub-carriers. Inverse Fast Fourier transform (IFFT)
blocks transform each signal into a time-domain waveform,
and guard intervals (GIs) are added to mitigate the effect ofmultipath fading. The output signals of the IFFT are converted into analog signals and up-converted to RF signals.
At the receiver, the received RF signals are downconverted to baseband signals. Next, the baseband signals
are converted into digital signals using automatic gain control (AGC) and an A/D converter. Symbol timing detection and automatic frequency control (AFC) are performed
for each received preamble and the FFT is applied to theresidual signals after the GIs are removed. After the FFT
processing, the channel matrix is estimated by the known
preambles in Fig. 2, and the data symbols are decoded using the Zero Forcing (ZF) algorithm. The demodulated bits
are then serialized and de-interleaved. Finally, the Viterbi
decoder estimates the transmitted bits. The frame format
is based on the IEEE802.11a standard [13], which is used
for commercial wireless LANs, but is extended to enable
MU-MIMO-OFDM transmission. As shown in Fig. 2, each
Table 2Signal parameters in measurement.

Sampling rate
Number of FFT points
Number of subcarriers
Symbol length
Preamble length
Modulation scheme

Coding rate

Fig. 1

20 MHz (A/D), 40 MHz (D/A)
64
48 (Information)+4 (Pilot)
3.2 µs+0.8 µs (GI)
8.0 µs (short), 16.0 µs (long)
QPSK, 16QAM
64QAM, 256QAM
1024QAM
1/2, 2/3, 3/4, 5/6, 7/8

Block diagram of 16 × 16 MU-MIMO testbed.
Table 1

RF parameters.

Number of Tx antennas
Number of Rx antennas
Radio frequency
Bandwidth
Range of AD/DA
Transmit power
Sensitivity

16
164.85 GHz
100 MHz (Max)
12 bits
10 W (Max)−20 ∼−70 dBm

Fig. 2

Frame format of MU-MIMO testbed.

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long preamble is one half the length of the 802.11a standard
and transmitted in four OFDM symbol timings. In the data
sequence, 100 OFDM symbols are transmitted per packet.
The channel matrices at all sub-carriers are fed back to thetransmitter using a cable (TCP-IP). Hence, the channel state
information (CSI) can be assumed to be ideally fed backfrom the receiver to transmitter in this testbed.
2.3 Transmit Beamforming Method
In this paper, we use the block-diagonalization (BD) algorithm, which is well known as a practical linear beamforming method for MU-MIMO systems [9], [10]. We consider
the downlink transmission. The numbers of transmit and receive antennas are assumed to be NT and NR , respectively.
The received signal, yi ∈ CNT ×1 , for User i is written as
yi = HiNU

Wi xi + ni

(1)

i=1

where Hi ∈ CNR ×NT is the channel matrix for User i. Terms
xi and ni are the transmit signal and noise for User i, respectively. Term NU represents the number of users. To avoidinterference, singular value decomposition (SVD) Hi is performed asTHi = HT1 · · · HTi−1 HTi+1 · · · HTNU








= Ui (Di 0)(V̄i Ṽi )H ,(2)



where Hi is the total channel matrix except for Hi . Term
V̄i represents the signal space of all users except User i.
Term Ṽi represents the null space weight that does not interfere with User i. In the BD algorithm, the virtual transmission weights are calculated using the SVD for the null
space channel matrix, Hi Ṽi , as








Hi Ṽi = Ui Di Vi H

(3)



where Di ∈ CNR ×NR is the diagonal matrix and the diagonal elements of Di are the square root of the eigenvalues of





Hi Ṽi Ṽi H HiH , λi,1 , λi,2 , · · ·, λi,NR . The transmission weightmatrices for User i, Wi , are determined as Ṽi V̄i and thetransmission qualities of data streams correspond to the diagonal elements of Di .
3.

Performance Evaluation of MU-MIMO Testbed

3.1 Measurement Environment
In order to clarify the characteristics of the proposed MUMIMO testbed in terms of the proposed modulation scheme,
we conducted measurements in an indoor environment. The
measurement environment is shown in Fig. 3. The measurement space in the room is 17.6 × 12.1 × 3 [m]. Sleeve antennas are used for the transmitter and receiver antennas. Linear arrays are used for the transmitter and receiver arrays.
The element spacing for the transmitter and that for the receiver antennas are 1.0 and 0.5 wavelengths, respectively.

Fig. 3

Measurement environment.

The heights of the transmitter and receiver antennas are 1.7
and 0.7 m, respectively. The maximum transmission power
per antenna is −6 dBm.
The transmitter antennas are located at Point Tx andthe receiver antennas are located at nine points (Rx-A to I).
All users are assumed to be located at each receiving point
in order to employ the evaluation among the users whose
SNRs are almost the same. To clarify the dependency of
the transmission quality on the direction of the receiver array antenna, we evaluate three rotation angles (−45◦ , 0◦ ,and 45◦ ) at each receiving point. Two to eight user cases
are assumed in the MU-MIMO scenario. The bit error rate
(BER) and frequency utilization for two to eight receiverantennas are given when the MU-MIMO scheme is evaluated with transmit beamforming based on the BD algorithm
described in Sect. 2.3. We use the ZF algorithm, which iswell known as the SDM decoding method. We also evaluate the Eigen-mode transmission [14]–[15] when considering the SU-MIMO transmission [16].
In this measurement, we use the modulation signals
given in Table 2 and the frame format in Fig. 2. In thismeasurement, we use the modulation signals given in Table 2. The total number of bits ranges from 107 to 108
and the frequency utilization is obtained at the BER of
107 to 108 bits. The frequency utilization (2.7 bits/s/Hz or
54 Mbit/s per 20 MHz) is evaluated for the 64QAM modulation scheme with the coding rate of R = 3/4, according
to the IEEE802.11a standard. In this case, 4.5 bits per subcarrier per OFDM symbol are transmitted.
3.2 Simple Adaptive Modulation Scheme
In this subsection, simple adaptive modulation and coding
is proposed. Figure 4 shows the distribution of eigenvalues
that are obtained using the BD algorithm in Sect. 2.3. The
eigenvalue distribution is shown when an i.i.d. channel isassumed in the propagation environment. For this calculation, 10,000 trials are performed. Figure 4 shows the results
when the number of antennas (NR ) per user is 3 or 4, andwhen the number of transmitter antennas (NT ) and the number of users (NU ) are 16 and 4, respectively.
Figure 4 shows that the fourth eigenvalue is very small
compared to the other eigenvalues in the 16×4×4-user case,

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Fig. 6
Fig. 4

Fig. 5

Eigenvalue distribution when using BD algorithm.

Achievable bit rate comparison (3 and 4 beams).

while the third eigenvalue is not small in the 16 × 3 × 4-usercase. Moreover, since the range of the fourth eigenvalue exceeds 20 dB, it is very difficult to assign a single modulation
scheme for such a data stream.
Figure 5 shows a comparison of the achievable bit rate
based on the BD algorithm [10] using the channel matrix,
when three or four beams are assumed for each user. The
channel matrixes for 16 × 4 × 4-user and 16 × 3 × 4-user aremeasured at Rx-A to I in Fig. 3 and the achievable bit rateis calculated by using the channel matrix for each receiving
point. The achievable bit rate in Fig. 5 is averaged by the
results on all subcarriers and receiving points (Rx-A to I in
Fig. 3). Note that the achievable bit rate is different fromthe frequency utilization obtained by the actual data transmission because the achievable bit rate is determined using
the eigenvalues that are obtained in Eq. (3). Figure 5 showsthat a higher achievable bit rate with three beams is obtained
compared to that with four beams for all users. Hence, thefourth eigenvalue is not useful from the viewpoint of thechannel capacity.
Figure 6 represents the flow of the proposed scheme.
Based on the results in Figs. 4 and 5, we first discard theeigenvalues that are less than the threshold value in the proposed scheme (Step 1 in Fig. 6). The eigenvalues for all

Flow of adaptive modulation scheme.

Table 3 Example of relationship between averaged eigenvalue and combination of modulation schemes (R=5/6).
Mod.

Mod.

Mod.

λ̄

scheme 1

scheme 2

scheme 3[dB]

1024QAM

1024QAM

256QAM

33 < λ̄ < 371024QAM

256QAM

256QAM

31 < λ̄ < 371024QAM

256QAM

64QAM

30 < λ̄ < 371024QAM

256QAM

16QAM

27.5 < λ̄ < 34

256QAM

256QAM

64QAM

27.5 < λ̄ < 32

256QAM

64QAM

64QAM

26 < λ̄ < 32

256QAM

64QAM

16QAM

25.0 < λ̄ < 29

64QAM

64QAM

64QAM

25.0 < λ̄ < 29

64QAM

64QAM

16QAM

22.5 < λ̄ < 26.5

64QAM

16QAM

16QAM

20 < λ̄ < 26

64QAM

16QAM

QPSK

18 < λ̄ < 22.5

16QAM

16QAM

QPSK

16 < λ̄ < 21.5

16QAM

QPSK

QPSK

13 < λ̄ < 19

QPSK

QPSK

QPSK

12 < λ̄ < 16.5

subcarriers are checked. In this paper, we set this thresholdvalue to 0 dB, which means an internal noise power. On theother hand, employing an interleaver in both data streams
and subcarriers suppresses the degradation in the transmission quality for subcarriers with a low SNR, even if fixed
modulation schemes are employed for each subcarrier on
the data streams corresponding to the first to third eigenvalues.
As the next step, we calculate the average value of theeigenvalues that are selected in Step 1 (Step 2 in Fig. 6).
The eigenvalue is averaged by using those for all subcarriers.
Table 3 shows an example of the relationship between the
average eigenvalue and the combination of the modulation
schemes when coding rate R is 5/6. Table 3 is obtained by
the combination of modulation schemes that achieves errorfree transmission for a certain SNR. The number of trials
is 1000 and the i.i.d. channel is assumed in this calculation
when changing the SNR.
In OFDM systems, the optimal method is to select themodulation scheme and coding rate based on the eigenvalue for each subcarrier. However, the selection method isNISHIMORI et al.: 16 × 16 MIMO TESTBED FOR MU-MIMO DOWNLINK TRANSMISSION

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Fig. 7 Frequency utilization versus number of receiver antennas (NT =16, NU = 4).

very complex in such a scheme, because many combinations
must be considered for each eigenvalue. On the contrary, thekey point here is that we utilize the average eigenvalue using
those for all subcarriers, which represents the transmission
quality when considering the interleaver for the data streams
and subcarriers. Since the interleaver can mitigate the degradation in the transmission quality for the subcarriers with alow SNR, different modulation schemes and coding rates foreach subcarrier are not required and the average SNR or average eigenvalue approximately represents the transmission
quality. If we prepare a database such as Table 3 in advance,
appropriate modulation schemes for each data stream can beeasily determined. As shown in Step 3 in Fig. 6, the modulation scheme and coding rate are selected by using the reference table. Actually, we prepare the reference tables foreach coding rate and the number of beams in advance.
3.3 Effectiveness of MU-MIMO Downlink Transmission
First, we clarify the effectiveness of the proposed adaptive
modulation and coding scheme as shown in 3.2. Figure 7
shows the frequency utilization versus the number of receiver antennas (NR ) when the numbers of transmitter antennas (NT ) and users (NU ) are 16 and 4, respectively. We compare the results between the proposed modulation schemeand the conventional schemes. In conventional scheme (A),the modulation scheme and coding rate are determined according to each SNR for each subcarrier. Conventional
scheme (B) means that all possible combinations of modulation schemes where the BER becomes zero are checkedusing the channel matrix. Nmin equals min(NR , 3).
Figure 7 shows that the frequency utilization with theproposed adaptive modulation scheme becomes almost thesame as that for conventional scheme (B) when NR is lessthan four, although the calculation complexity of the proposed scheme is dramatically reduced compared to conventional scheme (B). Surprisingly, the frequency utilization
when NR is four is decreased compared to that when NR isFig. 8Frequency utilization versus SNR when NR is two.

Fig. 9Frequency utilization versus SNR when NR is four.

three, if the number of selected beams is fixed to four inconventional scheme (B). Hence, we found that the minimum eigenvalue significantly affects the degradation in the
MU-MIMO transmission when NT equals NR × NU . Moreover, even if the modulation and coding are assigned basedon each subcarrier (conventional scheme (A)), the frequency
utilization is not improved when NR equals four because thefourth eigenvalue is not useful as shown in Fig. 6. On theother hand, the frequency utilization of the proposed scheme
is almost the same compared to that for conventional scheme(B) when the number of selected beams is set to three.
Figures 8 and 9 indicate the frequency utilization versus the SNR when the number of receiver antennas is two
and four, respectively. In these figures, the proposed adaptive modulation scheme is employed. The frequency utilization is defined as the transmission bit rate at which the
BER is less than 10−7 . Figures 8 and 9 show that the frequency utilization can be improved due to the increase in thenumber of users and the SNR. For example, the frequency
utilization for 43.5/50 and 30/35 bits/s/Hz (SNR: 31/36 dB)
can be obtained when 16 × 4 × 4-user and 16 × 2 × 4 user
MU-MIMO transmission are employed, respectively. More-IEICE TRANS. COMMUN., VOL.E93–B, NO.2 FEBRUARY 2010

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Fig. 11

Example of signal constellations (NT = 16, NR = 4, NU = 4).

Fig. 12

Resource allocations for MIMO-TDMA and MU-MIMO.

Fig. 10 Frequency utilization comparison between MU-MIMO and
SU-MIMO.

over, Figs. 8 and 9 show that the frequency utilization withthe proposed adaptive modulation scheme becomes almost
the same as that for conventional scheme (B) when NR isless than four.
Next, we compare the difference in improvement for
the MU-MIMO transmission when the number of receiverantennas is changed. Figure 8 shows that the frequency
utilization of MU-MIMO transmission is 2.0 to 3.3 times
higher compared to the eigenvector beamforming method
in SU-MIMO, when the number of receiver antennas is 2.
On the other hand, the frequency utilization of MU-MIMO
transmission becomes 1.6 to 2.4 times higher compared to
SU-MIMO, when the number of receiver antennas is 4.
Thus, we found that the effect on MU-MIMO is obtainedwith a smaller number of antennas.
We compare the frequency utilization between MUMIMO and SU-MIMO in Fig. 10, when the number of receiver antennas (NR ) is changed. For the MU-MIMO scenario, we set the condition where NT equals NR × NU . The
average SNR is 31 dB. We employ the proposed modulation
scheme in this figure. Figure 10 shows that the frequency
utilization with the SU-MIMO transmission cannot be significantly improved when NT is greater than NR . On theother hand, a frequency utilization of over 40 bits/s/Hz canbe obtained using the MU-MIMO transmission (NT = 16)
using the proposed modulation scheme, even if the numberof receiver antennas is 2.
Figure 11 shows signal constellation examples of the
16 × 4 × 4-user transmission. As shown in Fig. 11, the
MU-MIMO transmission using our testbed enables even1024QAM transmission. In this example, frequency utilization of 48 bits/s/Hz was obtained by 16 × 4 × 4-User MUMIMO transmission, when the SNR is 35 dB.
Finally, we evaluate the transmission rate that can be

Fig. 13

Transmission rate that can be assigned to one user.

assigned to one user. Figure 12 shows the considered scenario for this evaluation. The results are shown in Fig. 13.
When considering SU-MIMO transmission, we assume thattime division multiple access is applied for each user in SUMIMO scheme. Moreover, we assume that the time resources allocated fairly to each user as shown in Fig. 12.
Hence, the frequency utilization for each user in the SUMIMO scheme in Fig. 13 becomes 1/NU of that for a one
user case when the number of users is NU . For comparison, the maximum frequency utilization for SISO and 2 × 2
MIMO using IEEE802.11a and n are shown in Fig. 13. In
the case of SISO in IEEE802.11a, the frequency utilization is 2.7 bit/s/Hz without multiple access when employing
64QAM and R = 3/4. For the 2 × 2 MIMO transmission[13], the transmission with 2 streams (64QAM and coding
rate, R = 3/4) is assumed and the frequency utilization is
5.4 bit/s/Hz.
Figure 13 shows that the frequency utilization with NU user transmission does not become 1/NU of that with one

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user transmission when applying MU-MIMO transmission.
More specifically, the transmission rate for one user can be
improved by using MU-MIMO transmission compared to
SU-MIMO transmission using the TDMA scheme. For example, the transmission rate per user in 16 × 2 MIMO isalmost the same as that for 2 × 2 MIMO with only one usertransmission, when 2 users are considered in the TDMA
scheme. On the other hand, the transmission with 16 × 2
MU-MIMO can guarantee the transmission rate obtained by
2 × 2 MIMO up to 6 to 7 users.
4.

Conclusion

This paper described a MU-MIMO testbed that deals with
16 × 16 MIMO-OFDM transmission. We also proposed a
simple adaptive modulation scheme that employs a bit interleaver for the data streams and subcarriers for MU-MIMOOFDM transmission. Using this testbed, we evaluated thefrequency utilization for downlink transmission in a MUMIMO channel in an actual indoor environment. We clarified that the 16 × 4 × 4-user MU-MIMO transmission using the proposed modulation scheme achieved a frequency
utilization of over 43.5/50 bits/s/Hz (0.87/1 Gbps) when the
SNR was 31 and 36 dB, respectively. The frequency utilization of the MU-MIMO transmission with four users is 2.4 to
3.3 times higher compared to the eigenvector beamforming
method in SU-MIMO when the number of transmitter antennas is fixed to 16 and the number of receiver antennas ischanged from 2 to 4. Moreover, we clarified the effectiveness of MU-MIMO transmission compared to TDMA based
MIMO when considering the transmission rate per user. We
found that the transmission with 16×2 MU-MIMO can guarantee the transmission rate obtained by 2 × 2 IEEE802.11nbased MIMO up to 6 to 7 users.
A user selection scheme is required in MU-MIMO systems, although we did not focus on it in this paper. An important future investigation topic is to evaluate user selection
for users with different SNRs in order to enhance the reliability of MU-MIMO transmission in actual environments.
Acknowlegement
The part of this work is supported by the Ministry of Internal
Affairs and Communications, Japan, under the grant, “Research and development of fundamental technologies for advanced radio frequency spectrum sharing in mobile communication systems.” The authors thank Mr. Masaaki Ida, andMr. Yoshinobu Makise for their help on the indoor measurement.
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Kentaro Nishimori
received the B.E., M.E.
and Dr.Eng. degrees in electrical and computerengineering form Nagoya Institute of Technology, Nagoya, Japan in 1994, 1996 and 2002, respectively. In 1996, he joined the NTT Wireless Systems Laboratories, Nippon Telegraphand Telephone Corporation (NTT), in Japan. He
was senior research engineer on NTT Network
Innovation Laboratories. He is now an associate
professor in Niigata University. He was a visiting researcher at the Center for Teleinfrastructure (CTIF), Aalborg University, Aalborg, Denmark in 2006. He is currently an editor for the Transactions on Communications for the IEICE
Communications Society and Assistant Secretary of Technical Committee
on Antennas and Propagation of IEICE. He received the Young Engineers
Award from the IEICE of Japan in 2001, Young Engineer Award from IEEE
AP-S Japan Chapter in 2001, Best Paper Award of Software Radio Society
in 2007 and Distinguished Service Award from the IEICE Communications Society in 2005 and 2008. His current research interest is Multi-user
MIMO systems and cognitive radio systems. He is a member of IEEE.

Riichi Kudo
received the B.S. and M.S.
degree in geophysics from Tohoku University,
Japan, in 2001 and 2003, respectively. In 2003,
he joined NTT Network Innovation Laboratories, Yokosuka, Japan. He has been engaged
in the MIMO communication systems and the
beamforming method. He received the Young
Engineer Award from the Institute of Electronics, Information and Communication Engineers(IEICE) Japan in 2006. His current researchinterest is digital signal processing for wirelessand optical communication systems. He is a member of IEEE.

Naoki Honma
received the B.E., M.E., andPh.D. degrees in electrical engineering from Tohoku University, Sendai, Japan in 1996, 1998,
and 2005, respectively. From 1998 to 2009, he
was with the NTT Radio Communication Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in Japan. He is now
an Associate Professor at Iwate University. His
research interest includes compact antennas for
MIMO communications, and propagation for
wireless communication systems. He received
the Young Engineers Award from the IEICE of Japan in 2003, the APMCBest Paper Award in 2003, and the best paper award from the IEICE Communication Society in 2006. He is a member of IEEE.

Yasushi Takatori
was born in Tokyo, Japan,
1971. He received his B.E. degree in electrical and communication engineering and his
M.E. degree in system information engineeringfrom Tohoku University, Sendai, Japan in 1993
and 1995, respectively. He received his Ph.D.
degree in wireless communication engineeringfrom Aalborg University, Denmark in 2005. In
1995, he joined the Wireless Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in Japan. He is currently a seniorresearch engineer in the Wireless Systems Innovation Laboratory of NTT
Network Innovation Laboratories. He was a visiting researcher at the Center for TeleInFrastructure (CTIF), Aalborg University, Aalborg, Denmark
from 2004 to 2005. He received the Young Engineers Award from the
IEICE of Japan in 2000, the Excellent Paper Award of WPMC in 2004 and
YRP Award in 2005. His current research interest is MIMO systems, spatial
signal processing techniques and ultra-high speed optical communication
systems. He is a member of the IEEE.

Masato Mizoguchi
was born in Tokyo,
Japan, in 1965. He received the B.E. and
M.E. degrees in electrical engineering from Tokyo University of Science, Japan in 1989 and1991, respectively. In 1991, he joined Nippon
Telegraph and Telephone Corporation (NTT)and was mainly engaged in research and development of personal communication systems
and high data rate wireless LANs including theIEEE 802.11a systems. He is currently a SeniorResearch Engineer, Supervisor in the Wireless
Systems Innovation Laboratory of NTT Network Innovation Laboratories,
where he is engaged in future wireless broadband and ubiquitous systems.
He received the Young Researcher’s Award in 1998, the Best Paper Awardin 2000 and the Achievement Award in 2006 from IEICE. He is a memberof IEEE.