VOL. E101-B NO. 2
FEBRUARY 2018
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IEICE TRANS. COMMUN., VOL.E101–B, NO.2 FEBRUARY 2018

434

PAPER

Special Section on Recent Progress in Antennas and Propagation in Conjunction with Main Topics of ISAP2016

Inter-Terminal Interference Evaluation of Full Duplex MIMO Using
Measured Channel
Yuta KASHINO†a) , Masakuni TSUNEZAWA† , Student Members, Naoki HONMA† ,and Kentaro NISHIMORI†† , Members

SUMMARY In-band full-duplex (FD) Multiple-Input and MultipleOutput (MIMO) communication performs uplink and downlink transmission at the same time using the same frequency. In this system, the spectral
efficiency is theoretically double that of conventional duplex schemes, suchas Time Division Duplex (TDD) and Frequency Division Duplex (FDD).
However, this system suffers interference because the uplink and downlink
streams coexist within the same channel. Especially at the terminal side, it
is quite difficult for the terminal to eliminate the interference signals fromother terminals since it has no knowledge about the contents of the interference signals. This paper presents an inter-terminal interference suppression
method between the uplink and downlink signals assuming the multi-user
environment. This method uses eigen-beamforming at the transmitting
terminal to direct the null to the other terminal. Since this beamforming
technique reduces the degrees of freedom available, the interference suppression performance and transmitting data-rate have a trade-off relation.
This study investigates the system capacity characteristics in multi-user fullduplex MIMO communication using the propagation channel information
measured in an actual outdoor experiment and shows that the proposed
communication scheme offers higher system capacity than the conventional
scheme.
key words: MIMO, full duplex, interference reduction, inter-terminal interference

1.

Introduction

One of the important issues in wireless communication isincreasing the capacity within the constraints of the limited
spectral resources [1]. Hence, the future wireless system isrequired to achieve higher spectral efficiency than current
systems. To satisfy this requirement, in-band full duplex(FD) communication [2]–[4] is being studied. Current andconventional systems use Frequency Division Duplex (FDD)or Time Division Duplex (TDD) scheme. FDD requires two
separate frequency bands for uplink and downlink transmissions. TDD requires only a single frequency band and theuplink and downlink connections share the same channel but
in different periods. This may cause a reduction in communication speed because the transmitter and the receiver are
not operated at the same time.
To solve these problems, in-band FD multiple-input
and multiple-output (MIMO) communication is being studManuscript received February 15, 2017.
Manuscript revised June 15, 2017.
Manuscript publicized August 22, 2017.
† The authors are with the graduate school of Engineering, Iwate
University, Morioka-shi, 020-8551 Japan.
†† The author is with the Faculty of Engineering, Niigata University, Niigata-shi, 950-2181 Japan.
a) E-mail: t2316012@iwate-u.ac.jp
DOI: 10.1587/transcom.2017ISP0025

ied [5], [6]. The FD scheme is expected to double the frequency utilization efficiency compared with conventional
duplex schemes such as FDD and TDD, because the FD
scheme uses the same frequency channel to realize simultaneous uplink and downlink transmission. As pointed out
in the above references, self-interference suppression is a
key challenge in the FD scheme because the receiver is affected by the significantly strong signal transmitted from thestation’s own transmitting antenna. Study [5] uses various
kinds of techniques, both analog and digital, and interference
cancellation of more than 100 dB was realized. Also, theantenna arrangement can be exploited for self-interference
cancellation [7], and the authors have extended this idea to
MIMO systems [8]. However, none of these techniques are
suitable for mobile terminals because they require highly
complex or bulky hardware for achieving high interference
cancelation performance.
One solution to this problem is the unidirectional FD
scheme [9]. In this scheme, only the base station or the relay
station performs simultaneous transmission and reception.
Even though the terminal station does not perform simultaneous transmission and reception, the spectral efficiency
is doubled because the uplink and the downlink communication are realized at the same time without extending the
bandwidth. Accordingly, the RF front end used in the current
mobile handsets can be used because no simultaneous transmission and reception is performed at the terminal. However, there is a problem that interference occurs between theterminals because transmitting terminals will coexist withreceiving terminals. A solution must be found in the physical layer because it is very difficult to reconstruct the desired
signal because the interference distorts and adds noise to thesignal. Moreover, the knowledge of the contents of the interference signal from the other terminal is not available atthe receiving terminal, which makes it difficult to solve theproblem.
This paper focuses on the inter-terminal interference
and proposes an inter-terminal interference suppression
method to improve the system capacity including uplinkand downlink users. The proposed method uses eigenbeamforming at the transmitting side, as this renders it unnecessary for the receiving terminal to acquire any priori information. However, the transmission capacity is degradedbecause some array degrees of freedom are consumed bynull-beamforming. Since the necessary degrees of freedom
for null-beamforming depend on the environment between

Copyright © 2018 The Institute of Electronics, Information and Communication Engineers

KASHINO et al.: INTER-TERMINAL INTERFERENCE EVALUATION OF FULL DUPLEX MIMO USING MEASURED CHANNEL435

the transmitting and receiving terminals, this needs to beclarified in actual environments. Accordingly, we carriedout a channel measurement campaign, and the feasibility ofthe proposed FD scheme was confirmed.
In the remainder of this paper, 2 explains the principleof the proposed method, while 3 and 4 show experimental
conditions and results, respectively.
2.

Interference Suppression Method by Eigenbeamforming

2.1

Proposed System Model

Figure 1 shows the system model considered in this study.
Although there may be many users communicating in the
coverage area of the BS, just two users are considered inthis FD scenario. User selection and scheduling are nottaken into consideration for simplicity because we focus on
the fundamental performance of the FD system. Althoughestimating and sharing the time-varying channel information is an important problem, this study assumes that thechannel information is already known at all stations. These
problems are quite important and they will be resolved infuture work. This system model assumes the following situation: uplink user terminal 1 (UT1) and downlink user terminal 2 (UT2) communicate with the BS (Base Station) at
the same time. The BS employs separate transmitting andreceiving antennas to enable simultaneous communication
through uplink channel H 1 and downlink channel H 2 ; perfect self-interference elimination inside the BS is assumed.
This assumption is quite important because the focus of thispaper is inter-terminal interference. The signal transmitted
from UT1 reaches UT2 via the interference channel, H i .
Though this scheme alleviates the intense self-interference
inside the UTs, the transmitted signal for one UT interfereswith the other UT. To solve this problem, this study adopts
an eigen-beamforming technique at the transmitting UT, i.e.
UT1 directs a null to UT2. The capacity improvement due
to the proposed scheme is evaluated using a propagation
channel measured in an actual outdoor environment.
2.2

Eigen-Beamforming Method

First, we describe the eigen-beamforming method used forinterference suppression. Figure 2 shows the concept of
eigen-beamforming. The inter-terminal interference channel
matrix is defined as, hi,11..
H i = .
 hi, N 1
2

···
..
.
···

hi,1N 1..
.
hi, N 2 N 1



 ,


(1)

Fig. 2

Proposed system model.

Conceptual diagram of eigen-beamforming.

In the above equation, U i and V i are singular vector matrices, and Σi is a singular value matrix. · H represents
the complex conjugate transpose of the matrix. Also, themaximum number of eigenvalues, N, in H i is defined as,
N = min(N 1, N 2 ).

(2)

(3)

The singular vector matrix of (2) is expressed as,
Ui = [u i,1, · · · , u i, N 2 ]pp
Σi = diag( λ i,1, · · · , λ i, N )Vi = [v i,1, · · · , v i, N 1 ].

(4)
(5)
(6)

λ i,1, · · · , λ i, Ni is a list of eigenvalues arranged in descendingorder. Also, interference power Pi through H i is written as
Pi = ||H i ||F2

Pt1
N1

= (λ i,1 + · · · + λ i, N )where the size of the matrix is N 2 × N 1 when the numbersof antennas of the uplink and downlink terminals are N 1 andN 2 , respectively, see Fig. 2. Singular value decompositionof H i can be expressed as,
H i = Ui Σi Vi H .

Fig. 1

Pt1
.
N1

(7)

where || · ||F represents the Frobenius norm, and Pt1 is the total transmitting power at UT1. In this beamforming scheme,
some of the column vectors in the singular vector matrix, Vi ,are used for transmitting weight. When L degrees of freedom are used for interference suppression, the interference

IEICE TRANS. COMMUN., VOL.E101–B, NO.2 FEBRUARY 2018436

is minimized by choosing the L vectors corresponding to thelowest L eigenvalues. Hence, the transmitting weight vector
is expressed as,
V i (N −L) = [v i, L+1, · · · , v i, N 1 ].

(8)

The receiving vectors corresponding to this transmitting
weight are written as,
U i (N −L) = [u i, L+1, · · · , u i, N 2 ].

(9)Note that the receiving weight is not used in an actual system,
but is defined here just to simplify the following discussion.
Applying these weights, self-interference channel H i is converted into the equivalent inter-terminal interference channel
H i (N −L) expressed as,
H i (N −L) = U (N −L)H H i V (N −L)pp= diag( λ i, L+1, · · · , λ i, N ),Fig. 3

Antennas used.

(10)

where the number of streams in this case is N − L. When
this transmitting weight is used, the interference power givenby,
Pi (L−N ) = ||H i (N −L) ||F2

Pt1
N1

= (λ i, L+1 + · · · + λ i, N )

Pt1
.
N1

(11)

By using the transmitting weight mentioned above, the interference power at the receiver becomes smaller than (7). Using the lower-ranked eigenvectors as the transmitting weightmakes it possible to suppress the interference power. However, this reduces the maximum possible number of streams
at the transmitting terminal, which may degrade the transmission capacity. In this study, we consider the effect of the
reduced number of degrees of freedom on the transmission
capacity characteristics.

4.

Results3.

4.1

Eigenvalue Distribution

Experimental Conditions and Experimental Environment

Figure 3 shows the base station antennas and user terminal
antennas used in this measurement. In this measurement
campaign, the center frequency was set to 2.47125 GHz.
This experiment assumed that the two terminals simultaneously communicate with the base station. The base station
antenna consists of four subarrays, each of which has fourelement vertical patch antennas, where the signals at allelements are combined through the feedlines with identicalphase delays. In the FD scheme, the number of BS antennas
is four, and same BS antennas are used in measuring bothH 1 and H 2 . Each terminal has a four-element sleeve linear
array, where the inter-element spacing is set to 0.5 λ (λ:wavelength in a vacuum).
Figure 4 shows the measurement environment androute. In this photo, point A is 65 m away from point C,
which is just under the building mounting the BS, and point
B lies midway between them. UT2 was located at point Cor point D. All channels existing among the BS, UT1, andFig. 4

Measurment route.

UT2 (H 1 , H 2 , H i ) were measured while UT1 was moved
from point A to point C by way of point B. Also, the arrayorientations of the user terminals were set on either the x or
y direction as shown in Fig. 4.

Figure 5 shows the eigenvalue characteristics of two different channels versus UT1 location, which is measured by d(the distance from point C) ; UT2 remains fixed at point C.
Result (a) shows the eigenvalues of the channel between BS
and UT1, and (b) shows the eigenvalues of the channel between UT1 and UT2. These figures show that the average
difference between the first eigenvalue and the second eigenvalue is 16.8 dB and 12.6 dB for BS-UT1 and UT1-UT2,
respectively. Hence, the eigenvalues (other than the first one
in the interference channel between UT1 and UT2) remain
relatively higher than that between BS and UT1. The reason
for this is that the effect of multipath propagation is more
intense between two ground-level sites than that from a low
site to the BS, whose elevation is much higher than groundlevel.
4.2

Eigenvalue Criteria: Achievable Rate Definition

In this evaluation the number of antennas at all stations are

KASHINO et al.: INTER-TERMINAL INTERFERENCE EVALUATION OF FULL DUPLEX MIMO USING MEASURED CHANNEL

437
Table 1

Summary of the signal flow and capacity.
Period 1

UT1
UplinkDownlink
UplinkTDD
MU-MIMO
FD

UT2
Downlink
Downlink

Period 2
UT1
UT2
Downlink
Downlink Downlink
Downlink
UplinkC 2i = log2 I + H 2 H 2 H

Pt
Mσn2

· I + (H i V iDistance characteristics of eigenvalue distribution.

identically given as N1 = N2 = NB = M, and the transmitting power at all stations are also identical as Pt for simplicity. This assumption is fair because the transmission poweris limited by RF frontend performance, and all stations are
assumed to have the same RF frontends in this study.
The transmission capacity was calculated using the following equations,

C FD =Pt
C 1 = log2 I + H 1 H 1
Mσn2Pt
C 2 = log2 I + H 2 H 2 H
Mσn21
C TDD = (C 1 + C 2 )2HC MU−MIMO = log2 I + H M H M H
C 1BF =

(12)
(13)
(14)
Pt
Mσn2

log2 I + (H 1 V i (N −L) )(H 1 V i (N −L) ) H
C 2BF =
∗

(15)

Pt
Mσn2
∗

log2 I + (H 2T U i (N −L) )(H 2T U i (N −L) ) H

(16)

Pt
(17)
Mσn2

Pt
)
Mσn2

(N −L) H

) −1
(18)

!
Pt
C 1i = log2 I + H 1 H 1
Mσn2
) −1
(
∗
∗
Pt
· I + (H i U i (N −L) )(H i U i (N −L) ) H, (19)
Mσn2

where σn2 is the noise power, · ∗ represents conjugate operation, and C1 and C2 represent the raw uplink and downlink capacities without any interference and beamforming.
V i(N −L) and U i(N −L) represent the eigenvectors corresponding to (N − L + 1)th∼Nth eigenmodes. Although C1 and C2
represent UT1 uplink and UT2 downlink capacities, respectively, UT1 downlink and UT2 uplink capacities are identicalto C1 and C2 , respectively due to the channel capacity duality [10]. CTDD represents the TDD system capacity when
the burst period is equally shared by uplink and downlink.
C MU−MIMO is the downlink capacity with block diagonalization (BD) based multi-user (MU) MIMO [11], [12], where
BS uses a four element subarray and each UT uses two eigenmodes corresponding to the two highest eigenvalues. H M
represents the channel when BD-based weight is applied atboth transmitter and receiver sides. The weight determination method is detailed in Appendix. C FD is the system
capacity using the proposed FD scheme. The signal flow
and capacity are summarized in Table I.
Since the proposed FD scheme has two periods to realize the bidirectional communication between the BS and
UTs, the capacities with periods 1 and 2 are averaged as,T

Fig. 5)(H i V i

Eq. (14)
Eq. (15)
Eqs. (16)–(19)!

((N −L)

Capacity∗1(C 1BF (UL) + C2i (DL)2
+ C2BF (UL) + C1i (DL)).

(20)

C 1BF , C 2BF , C 1i , and C 2i are the partial capacities that areused for calculating the proposed FD system capacity [13],C FD . When UT1 and UT2 are the uplink and downlink users
at a certain moment, the uplink and downlink capacities
are expressed as C 1BF and C 2i , respectively, and the system
capacity at this moment is defined as the sum of them. L
indicates the number of freedom used for forming nulls at the
uplink terminal, where the capacity without beamforming
is calculated by setting L = 0. UT1 uses L degrees of
freedom for nulling, so C 1BF is reduced by the number ofnulls. Though UT2 is receiving the signal from the BS, theremaining interference from UT1 impacts the capacity of
UT2 as C 2i . At the next moment, UT1 and UT2 become theIEICE TRANS. COMMUN., VOL.E101–B, NO.2 FEBRUARY 2018

438

Fig. 6

CDF of channel capacity between A-B.

downlink and uplink users, and the signal flow direction isreversed. C 2BF and C 1i are the capacities when the uplinkand downlink users are switched, and the system capacity
is defined as the sum of them. Therefore, the total system
capacity of the proposed FD is defined as (20) because thisstudy assumes the uplink and downlink periods are assigned
equally.
4.3

Transmission Capacity Characteristics when UT2 isPlaced at Point C

Figures 6 and 7 show the Cumulative Distribution Function (CDF) of the transmission capacity of the various communication methods discussed in 3, where UT2 is placedat point C, UT1 moved between A and B, and then between B and C. In this arrangement, UT2 and BS lie on
the same line from UT1. This means the capacity of the
proposed FD is likely to be worst since the desired signals
from UT1 to BS are also suppressed by null-beamforming
to UT2. TDD, MU-MIMO, and FD represent the channel capacities of TDD, multi-user MIMO, and FD MIMO.
The terms w/o BF, w/ BF1, and w/ BF1&2 stand for thechannel capacities when the value of L in equations (16)–(19) is 0, 1 and 2, respectively. These results confirm thatin both cases the FD scheme outperforms TDD and MUMIMO schemes. Of particular interest in Fig. 6(a), FD with
beamforming (w/ BF1 and w/ BF1&2) yields better performance than FD without beamforming (w/o BF), and FD

Fig. 7

CDF of channel capacity between B-C.

(w/ BF1) improves the median value of the transmission capacity by 14.4 and 7.19 Bits/s/Hz compared to that of TDD
and MU-MIMO, respectively, when the terminal antennas
are co-linear on the x axis direction and UT1 is located between A and B. On the other hand, the advantage of the FD
scheme compared to TDD and MU-MIMO schemes is smallin Fig. 7, as UT1 lies between B and C, i.e. the distance
between the two UTs is quite short, i.e. the interference is
not sufficiently suppressed. Nevertheless, Fig. 7(b) shows
FD with beamforming (w/ BF1 and w/ BF1&2) yields better
performance than all other schemes including FD without
beamforming (w/o BF) when both UT arrays are face the yaxis direction. The curve, FD (w/ BF1), plotted in Fig. 7(b)shows an improvement in the median value of the transmission capacity by 5.79 and 4.19 Bits/s/Hz compared to TDD
and MU-MIMO, respectively. This is because the first eigenvalue in the interference channel becomes large compared tothe other eigenvalues, and this condition suits the beamforming technique. These results confirm that high performanceis attained when the two UTs operate in end-fire orientation.
4.4

Transmission Capacity Characteristics when UT2 isPlaced at Point D

Figures 8 and 9 plots the CDF of the transmission capacity ofthe various communication methods, where UT2 is placedat point D, and UT1 moves between A and B, and then between B and C. In this case, the directions of the users as

KASHINO et al.: INTER-TERMINAL INTERFERENCE EVALUATION OF FULL DUPLEX MIMO USING MEASURED CHANNEL

439

Fig. 8Fig. 9

CDF of channel capacity between A-B.

seen from the BS are different, so BD-based MU-MIMO isexpected to offer better performance than that demonstrated
in Figs. 6 and 7. The inter-user direction with respect tothe array orientation varies depending on UT1 location, andthis is the cause of the great performance variation in the
proposed scheme shown in Fig. 8. The performance variation can be understood from the spread of CDF in Fig. 8.
Nevertheless, from the results shown in Fig. 8, the curve,
FD (w/o BF), shown in Fig. 8(a) realizes higher channel capacity than the conventional method as in 4.3, and improves
the median value of the transmission capacity by 15.2 and8.36 Bits/s/Hz compared to that of TDD and MU-MIMO,
respectively. Similarly, when UT1 is located between B andC where the terminal antennas are co-linear in the x axis
direction, the curve, FD (w/ BF2), shown in Fig. 9(b) realizes higher channel capacity than the conventional methods,
and improves the median value of the transmission capacity
by 10.5 and 7.39 Bits/s/Hz compared to that of TDD and
MU-MIMO, respectively. Both antenna arrangements improve the capacity of the FD MIMO communication system
by up to 2.1 Bits/s/Hz even if just a single-null is used. The
measurements reveal that eigen-beamforming attains superior performance even when the inter-terminal distance issmall and the interference power is large.

5.

CDF of channel capacity between B-C.

Conclusion

This paper introduced an inter-terminal interference suppression method for a FD MIMO communication system,
where uplink and downlink users are simultaneously active.
For interference suppression, the transmitting user employs
the eigen-beamforming method. A field measurement campaign showed that the FD MIMO communication scheme
offers larger transmission capacity than the conventional
schemes regardless of the inter-user distance. The measurement results showed the proposed scheme improved the
median value of the transmission capacity from 1.30 to 1.53
times over that of the conventional TDD and MU-MIMO
schemes in the scenario considered.
Acknowledgments
This research and development work was supported by the
MIC/SCOPE (155002002).
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Appendix:

The Weight Determination by BD Method

The weight is determined by BD method for MU-MIMO
scheme, but the total number of the antennas at the UT
side is larger than that at the BS. This means the normal
BD method cannot be simply applied to this case. First,
we consider downlink channel H 2 in Fig. 1. The numberof antennas at all stations are identically given as M = 4.
Therefore, singular value decomposition of H 2 defined by(2)–(6) is written as,
H2 = [u 2,1, · · · , u 2,4 ]Σ2 [v 2,1, · · · , v 2,4 ] H .

(A· 1)

Also, we select the highest two eigen-modes out of fourmodes, and the eigenvectors, u 2,1 and u 2,2 , corresponding to
the selected eigen-modes are used as the receiving weights.
Therefore, the downlink channel with mode selection is defined as,
H2d = [u 2,1, u 2,2 ]H H 2 .

(A· 2)

We perform same processing for H 1T and calculate H 1d .
By using this mode selection technique, the total number ofthe equivalent antennas at UT side is four, which is exactly
same to the number of the antennas at BS. Therefore, the BD
method can be applicable for this scenario.

Yuta Kashino
received the B.E. in electrical and electronic engineering from Iwate University, Morioka, Japan in 2016. He is currently
in the master program in Iwate University. His
research interest is interference reduction techniques for MIMO full-duplex communication.

Masakuni Tsunezawa
received the B.E. inelectrical and electronic engineering from Iwate
University, Morioka, Japan in 2015. He is currently in the master program in Iwate University.
His research interest is interference reduction
techniques for MIMO full-duplex communication.

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. In 1998, he joined the
NTT Radio Communication Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in Japan. He is now working for
Iwate University. He received the Young Engineers Award from the IEICE of Japan in 2003,
the APMC Best Paper Award in 2003, the Best
Paper Award of IEICE Communication Society
in 2006, and 2014 Asia-Pacific Microwave Conference Prize in 2014, respectively. His current research interest is MIMO system and its applications. He is a member of IEEE.

Kentaro Nishimori
received the B.E., M.E.
and Ph.D. degrees in electrical and computer engineering form Nagoya Institute of Technology,
Nagoya, Japan in 1994, 1996 and 2003, respectively. In 1996, he joined the NTT Wireless Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in Japan. He wassenior research engineer on NTT Network Innovation Laboratories. He is now associate professor in Niigata University. He was a visiting
researcher at the Center for Teleinfrastructure(CTIF), Aalborg University, Aalborg, Denmark from Feb. 2006 to Jan.
2007. He was an Associate Editor for the Transactions on Communications
for the IEICE Communications Society from May 2007 to May 2010 andAssistant Secretary of Technical Committee on Antennas and Propagationof IEICE from June 2008 to May 2010. He is now Secretary of TechnicalCommittee 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 SoftwareRadio Society in 2007. He received IEICE Best Paper Award in 2010. His
main interests are spatial signal processing including MIMO systems andinterference management techniques. He is a member of IEEE and IEICE.