IEICE TRANS. COMMUN., VOL.E98–B, NO.5 MAY 2015

861

PAPER

Interference Suppression Method between Primary Broadcasting
and Secondary Systems Using Load Modulation
Takuma ITO†a) , Student Member, Naoki HONMA† , Member, Keisuke TERASAKI† , Student Member,
Kentaro NISHIMORI†† , Senior Member, and Yoshitaka TSUNEKAWA† , Member

SUMMARY
Controlling interference from the secondary system (SS)to the receiver of the primary system (PS) is an important issue when the
SS uses the same frequency band as the television broadcast system. The
reason includes that the SS is unaware of the interference imposed on the
primary receiver (PS-Rx), which does not have a transmitter. In this paper,
we propose an interference control method between PS-Rx and SS, wherea load modulation scheme is introduced to the PS-Rx. In this method, thesignal from the PS transmitting station is scattered by switching its load
impedance. The SS observes the scattered channel and calculates the interference suppression weights for transmitting, and controls interference bytransmit beamforming. A simulation shows that the Signal-to-Interference
Ratio (SIR) with interference control is improved by up to 41.5 dB compared to that without interference control at short distances; the results
confirm that the proposed method is eﬀective in controlling interference
between PS-Rx and SS. Furthermore, we evaluate the Signal-to-Noise Ratio (SNR) and channel capacity at SS.
key words: load modulation, frequency sharing, interference control,channel capacity

1.

Introduction

The demand for frequency resources have been increasing
because various wireless communication systems have beendeveloped and released. However, the frequency band cannot be assigned arbitrary since the frequency resources arelimited. Therefore, frequency sharing technologies among
the multiple systems are required. However, co-channel interference between existing primary system (PS) and newly
installed secondary system (SS) becomes one of important
issues to be resolved when the same frequency band is usedamong them.
The frequency sharing technique using beam-forming(BF) at the SS base station (SS-BS) has been proposed [1].
The SS-BS suppresses the interference to the PS terminal bydirecting null directivity toward PS terminal. However, thistechnique cannot be applied to the situation where PS terminals do not have transmitters, such as TV receiver, since SSBS cannot know the interference channel between SS-BSand PS receiver (PS-Rx). Further, cognitive radio technologies that improve the utilization of the frequency resources
Manuscript received September 8, 2014.
Manuscript revised December 18, 2014.
†
The authors are with the Graduate School of Engineering,
Iwate University, Morioka-shi, 020-8551 Japan.
††
The author is with the Graduate School of Science and Technology, Niigata University, Niigata-shi, 950-2181 Japan.
a) E-mail: t2313001@iwate-u.ac.jp
DOI: 10.1587/transcom.E98.B.861

by observing the environment of other radio systems havebeen proposed [2]–[4]. Particularly, in [4], it is shown thatthe influence of interference from PS transmitting station(PS-Tx) to SS user equipment is evaluated when the SS isoverlaid on the PS, i.e. TV system, and an algorithm for canceling the interference signal at SS user equipment is proposed. However, they have been only evaluated the performance of SS, and the eﬀect of the interference suppression
from SS-BS to TV receiver has not been investigated.
The target of this study is confirming an intersystem interference suppression method to realize frequency sharing
between SS and PS, where PS terminals do not emit a radio wave, such as TV receiver. In this paper, we introduce
a load modulation technique to PS-Rx in order to let SS-BS
get the interference channel between PS-Rx and SS-BS. A
load modulation is a technique that the terminal scatters andmodulates the signal by switching the load impedance connected to the antenna [5]. This will enable PS-Rx to send theinformation to SS-BS without a transmitter by scattering thesignal from PS-Tx. At the SS-BS, estimates the scatteredchannel and calculates the interference suppression weights
for transmitting, and controls interference by BF [6].
Note that the term, ‘load modulation’, is used to explain varying the load impedance at the PS-Rx antenna, but
the aim of this scheme is not for signal transmission to PS
but for channel estimation at other system. Actually, the TV
broadcast signal is already modulated signal, and varying
impedance yields doubly modulated signal. Nevertheless,this scheme is physically identical to the existing load modulation technique, and the term, ‘load modulation’, is usedin the following discussion.
2.

Interference Control Method Using Load Modulation

Figure 1(a) shows the mechanism of load modulation. In
this study, we use the bi-static structure, where the transmitting and receiving antennas at the reader are placed separately. The tag modulates and reflects the incident signal byswitching the load impedance, Z, connected to the antenna
at the terminal side [5]. The tag can send data without atransmitter using a carrier wave from the signal of readerTx side. At the reader-Rx, the modulated signal is decodedby observing its amplitude and phase, and can get information of the tag side. Also, the direct wave from reader-Tx
reaches the reader-Rx. In this study, the load modulation

c 2015 The Institute of Electronics, Information and Communication Engineers
Copyright IEICE TRANS. COMMUN., VOL.E98–B, NO.5 MAY 2015

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Fig. 2Equivalent circuit model of proposed system.

port, and bR is the signal to R port, where [·]T is the matrix
transposition. The relationship between bT , bS and bR canbe expressed as [7]–[9],
⎛ ⎞ ⎛
⎞⎛ ⎞
⎜⎜⎜bT ⎟⎟⎟ ⎜⎜⎜ S T T HT S HT R ⎟⎟⎟ ⎜⎜⎜aT ⎟⎟⎟⎜⎜⎜⎜ bS ⎟⎟⎟⎟ = ⎜⎜⎜⎜ HS T SSS HS R ⎟⎟⎟⎟ ⎜⎜⎜⎜ aS ⎟⎟⎟⎟
(1)
⎝ ⎠ ⎝
⎠⎝ ⎠
bR
HRT HRS S RR aR(2)aR = ΓbR ,Fig. 1

Proposed system model.

scheme is utilized to estimate the channel, which is used
for suppressing the interference from SS-BS to PS-Rx. Figure 1(b) shows load modulation at the PS-Rx side and thestructure of interference suppression. This model assumes
SS is overlaid on existing PS. SS has a base station (SS-BS)and user equipment (SS-UE), and each of them has multiple
antennas. In PS, a receiver (PS-Rx) receives the signal fromtransmitting station (PS-Tx), and the number of the PS-Rx
is set to one for simplicity since the transmitting power at
SS-BS is assumed to be suﬃciently small and the numberof the PS-Rx can be one or less in the range where the signal reaches from SS. In this study, we assume that PS is a
television broadcast system. The number of antennas of PSTx, PS-Rx and SS-BS are MT T , MRR , and MSB , respectively,
and MT T = MRR = 1. Z is connected to PS-Rx antenna, and
PS-Rx is able to reflect the broadcasting waves by switching Z. This allows SS-BS to receive the interference channel
information.
Here, in this model, T, R, and S represent transmitting(PS-Tx antenna), terminal (PS-Rx antenna), and receiving(SS-BS antennas) ports, respectively. Figure 2 shows theantenna system model that includes not only PS-Tx and SSBS antennas, but also antenna at the PS-Rx. A PS-Rx antenna is terminated by a tunable reactance, Z. The term SS
is the S -parameter matrix, and includes the S -parameter andchannel matrix of both PS-Tx and SS-BS antennas. aT is thetransmitted signal from T port, aS = [aS 1 , . . . , aS MS B ]T is thesignals from S port, aR is the signal from R port, bT is thesignal to T port, bS = [bS 1 , . . . , bS MS B ]T is the signals to S

where S T T and SSS are the scattering matrices for the transmitting and SS-BS antennas, respectively. The term S RR(∈ C MRR ×MRR ) is the scattering coeﬃcient of the PS-Rx antenna, and HRS (∈ C MRR ×MSB ) represents the channel between
SS-BS and PS-Rx antennas. Terms HS T (∈ C MSB ×MT T ) andHRT (∈ C MRR ×MT T ) denote the channels from PS-Tx antenna
to SS-BS antennas and PS-Rx antenna, respectively. Since
the matrix is symmetric, HT S = HTS T (∈ C MT T ×MSB ) and
HS R = HTRS (∈ C MSB ×MRR ). From the relationship shownin (1) and (2), the observed signal at S port is described as,bS = {HS T + HS R Γ(1 − S RR Γ)−1 HRT }aT= HS T aT ,(3)

where HS T (∈ C MSB ×MT T ) is the observed propagation channel at SS-BS which consists of both a direct path and path
via PS-Rx. Γ denotes the reflection coeﬃcient of Z, and isexpressed as,
Γ=Z − Z0.
Z + Z0(4)First, the channel, HS R , with ideal load (Z0 ) is calculated in
the ring model, which is explained in Sect. 3, and then, theinteraction between S RR and load impedance and its eﬀectof the load on the channel is calculated by (3). Z0 is the reference impedance and the value is 50 Ω, and the state of Z
is open (∞) or load (50 Ω). Here, PS-Rx switches Z during
PS-Rx does not receive signals, i.e. TV is not used. But thisscheme is applicable when we have suﬃciently stable environment, where the channel varies little. Nevertheless, it
was recently shown that the channel in the indoor environment is fairly stable. In [10], the Doppler characteristics for
wireless LAN in an indoor environment were experimentally evaluated, and it showed the temporal channel varianceis much smaller than it has been believed as in the IEEE

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863

802.11n standardization. Therefore, this paper assumes thechannel is suﬃciently stable for this reason and simplicity ofthe discussion. Otherwise, PS-Rx can receive and reflect the
PS-Tx signal simultaneously if a part of the received poweris used for the backscattering. This can be realized by inserting variable impedance element to the feed circuit, forexample, the varactor diode is attached on the transmission
line between antenna and receiver at PS-Rx [11]. However,the latter scheme is not dealt with in this paper because thisneeds further investigation and discussions. In the case ofopen, SS-BS observes the sum of the reflected wave from
PS-Rx and direct wave from PS-Tx because PS-Rx reflects
all broadcasting waves, and HS T can be transformed into,HS T (open) = HS T + HS R (1 − S RR )−1 HRT .

(5)

On the other hand, when Z is switched to load, only the
direct wave from PS-Tx is observed at SS-BS because PSRx does not reflect the broadcasting wave, and (3) is transformed to,HS T (load) = HS T .

Table 1

Simulation condition.

Antenna element
nowriteNumber of antennas
nowriteSS-BS element spacing
Frequency
PS-Tx power
Noise power at SS-BS
nowriteHeightnowritePath-loss between PS-Tx and
PS-Rx (SS-BS)
Path-loss between PS-Rx and
SS-BS
Channel model

Dipole antenna
PS-Tx: 1
PS-Rx: 1
SS-BS: 20.5 λ
700 MHz
60 dBm−100 dBm
PS-Tx: 100 m
PS-Rx: 10 m
SS-BS: 10 m
Okumura-Hata modelFree space propagation loss
Ring model(6)

By (5) and (6), SS-BS can estimate HS R , which is the channel from PS-Tx by way of PS-Rx. It can be expressed as,
HS R = HS R (1 − S RR )−1 HRT .

(7)

By singular value decomposition (SVD) of the transposed
matrix of HS R , we get,H
HTS R = U RS [SRS , 0][V̄ RS , Ṽ RS ] ,Channel model and antenna arrangement.

(8)

where Ṽ RS is the null-space weight vectors from SS-BS. i.e.,
SS-BS can suppress interference by using Ṽ RS as the transmitting weight. However, the channel received by SS-BS
will contain noise, so we need to consider the eﬀect of noise
on BF accuracy. Although the signal from PS-Tx can be
modulated, pilot signals for synchronization can be used,
for example [12].
Note that both HS R and HS R can be used for the weightcalculation, but HS R is used instead of HS R because information of HS R is not available in an actual operation. Also,
the proposed method is applicable to a case that PS-Tx and
PS-Rx have multiple antennas. However, MRR < MSB since
the null directivity of SS-BS antenna is produced [13].
3.

Fig. 3

Simulation Setup

Table 1 shows the simulation conditions. λ represents wavelength, and the amplitudes of transmitted signal and noise
at SS-BS are in a Gaussian distribution. All antennas are
dipoles. PS-Tx and PS-Rx have one antenna each while SSBS has two; SS-BS antenna element spacing is 0.5 λ. In thisstudy, the frequency lies in the 700 MHz band and the transmitted power of PS-Tx is set to 60 dBm since we assume atelevision broadcast system. The noise power in this paper
is set to −100 dBm since we have considered that the simulation condition should be suﬃciently feasible [14]. As thepath-loss model, we use Okumura-Hata model for HRT and

HS T , where the height of PS-Tx is 100 m and that of PSRx and SS-BS are 10 m. A free space propagation model isadopted for HS R for simplicity.
Figure 3 shows the channel model and the arrangementof antennas and scatterers. The channels are calculated bya geometry-based ring model [15], [16] and the path-loss istaken into account as described above. After that, the influence of the channel due to load modulation can be calculated by (3). The separation distance of PS-Tx and PS-Rx
is denoted by d1 [km] and that of PS-Rx and SS-BS is denoted by d2 [m]. Also, we consider the distance of between
PS-Tx and SS-BS is approximately equal to d1 . In the ringmodel, 10 scatterers are distributed on the horizontal planearound each antenna, and ring size is assumed to be suﬃciently large compared to antenna size, i.e. all paths can berecognized as plane waves. For each trial, the channel isgenerated randomly by arranging the scatterers in random
directions [17], [18], i.e. 1000 channels with diﬀerent path
distributions are used for the evaluation. The intensity of thereceived signals at PS-Rx and SS-BS vary depending on thechannel strength and noise for each trial since the strength ofthe channel generated by this model and noise are not unified in this simulation. Nevertheless, the power expectationof the channel is statistically determined corresponding to
the path-loss. Also, the noise power expectation is always
constant because it does not depend on the channel.
When we consider the practical use, PS-Rx and SS-BSIEICE TRANS. COMMUN., VOL.E98–B, NO.5 MAY 2015864

antennas can be placed at indoor or outdoor. If SS-BS isplaced at outdoor, the transmitted signal from SS-BS may
interfere many PS-Rxs because some TV set antennas arein line-of-sight. To suppress the interference, SS-BS is required to have many antennas because it must have suﬃcientdegree of freedom to suppress the interference to multiple
PS-Rxs. In this case, the other technique, such as beamtilting, transmission power control at SS-BS, and so on,
should be jointly used to mitigate the interference with therealistic number of the antennas. When SS-BS is located in
an indoor environment, the building penetration loss needs
to be considered. Although the reception power at PS-Rx
is lowered by the penetration loss, the loss is about 8 dB in
the UHF band [19]–[21]. On the other hand, the penetration loss can be exploited for confining SS-BS signals into
the limited area. This means the SS-BS can be aware of thesmallest number of the PS-Rxs, which may be the nearest
TV set.
4.

Results

4.1 The Estimation Error at SS
In Sect. 2, we indicated the need to estimate HS R from PSTx by way of PS-Rx in order to control interference by BF.
However, channel error due to noise is inevitable when receiving signals at SS-BS. The estimation error is expressedas [9], [22],J=||Herror − Htrue ||2F||Htrue ||2F,

X = [x1 , x2 , · · · , xL ]. From Fig. 4, it can be seen that the
50% estimation error at L = 100 is a 22.0 dB improvementcompared to that at L = 1. That is, the estimation error is
reduced by increasing L, and L is set to 100 in the following
discussions.
Figure 5 shows the CDF of the channel estimation error
with various intersystem distances. It can be seen that the
50% estimation error is −8.3 dB at d1 = 3 km, d2 = 50 m
whereas J = −38.1 dB at d1 = 1 km, d2 = 10 m. That is, theestimation error is raised since the path-loss increases withd1 and d2 . Thus, the estimation error falls as d1 and d2 aredecreased.
4.2 SIR at PS-Rx
This section discusses SIR at PS-Rx with and without interference control. Here, the desired signal power and theinterference power are calculated by using HRT and HTS R ,respectively. The transmitted power of SS-BS is 10 dBm.
Figure 6 shows the relation between the channel estimation error and SIR with interference control. In thisfigure, it is found that the lower estimation error is, thehigher SIR becomes. Here, according to [23], in a televi-(9)

where Herror is the channel containing error and Htrue is thetrue channel to be estimated, i.e., HS R .
Figure 4 shows the CDF (Cumulative Distribution
Function) of the estimation error with various numbers of
training sets, L. Here, the intersystem distances are d1 =1 km and d2 = 10 m. Training is the process of estimating the propagation channel at the receiving side (at SSBS) when PS-Tx transmits a known signal, and L represents the number of transmission symbols that are used
for training. In this study, PS-Tx transmits the symbols,

Fig. 5 CDF of the channel estimation error with various intersystem distances, d1 and d2 .

Fig. 4L.

Fig. 6 The relation between the channel estimation error and SIR withinterference control.

CDF of the estimation error with various numbers of training sets,

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Fig. 8Fig. 7

Interference signal at PS-Rx with interference control versus d2 .

10% SIR at PS-Rx versus d1 .

sion broadcast system, the required SIR, which is defined
as SIRREQ , must be above 23 dB at PS-Rx. In order to reveal the applicable range of proposed method, the required
estimation error, JREQ is calculated by median value when
SIRREQ = 23 dB. From Figs. 5 and 6, it can be seen that
JREQ = −19.5 dB, and the applicable range of proposed
method is determined as: d1 = 3 km or less when d2 = 10 m.
Or d2 = 50 m or less when d1 = 1 km.
Next, the distance characteristics of SIR are discussed
in the view of the applicable ranges. Figure 7 shows the SIR
at PS-Rx versus d1 . Also, SIR in following discussion isevaluated using the 10% value in CDF because we need toconfirm that the eﬀect of interference reduction by proposed
method is suﬃcient even if the interference from SS-BS is ina worst case such as LOS environment. It is found that SIR
with interference control is independent of d2 from Fig. 7.
The reason is as follows:(1) The path-loss becomes small (large) as d2 becomescloser (more distant).
(2) The interference to PS-Rx becomes large (small), but thecomponent of HS R which is necessary for interference suppression, also becomes large (small).
(3) Figure 8 shows the interference signal at PS-Rx with interference control versus d2 . It is found that the interference
signal with interference control is independent of d2 since
the interference to PS-Rx and the level of interference suppression by proposed method cancel each other.
(4) Figure 9 shows 10% SIR at PS-Rx with interference control versus d2 . SIR can be calculated from the ratio of thedesired signal and the interference signal. From Fig. 8, it
was found that the interference to PS-Rx with interference
control is independent of d2 , and the desired signal is naturally no change for d2 . Therefore, it can also be seen that
SIR with interference control becomes independent of d2 .
In addition, the reason that the SIR at PS-Rx is independent of d2 can be understood in detail by the following
discussion. The interference signal with interference control
from SS-BS to PS-Rx can be expressed as,
yiRS = HTS R Ṽ RS sS

Fig. 9

10% SIR at PS-Rx with interference control versus d2 .

√=−2nT Ṽ RS sS,√(1 − S RR )−1 PT ART(10)where Ṽ RS and sS represent the null-space weight vectors,
which is obtained from (8) and transmitted signal at SS-BS,
respectively. n is the noise at SS-BS. ART denotes path-loss
between PS-Tx and PS-Rx, i.e. ART = (PR : received power
at PS-Rx)/(PT : transmitted power at PS-Tx). The derivationof (10) is described in appendix. From (10), it is clarified
that the interference signal from SS-BS to PS-Rx is independent of the path-loss between PS-Rx and SS-BS, i.e. d2
does not aﬀect SIR.
Furthermore, in Fig. 7, it can be seen that the eﬀect ofinterference reduction by proposed method is remarkable
when both d1 and d2 are suﬃciently short, e.g., SIR withinterference control is 26.5 dB, which is 41.5 dB higher thanthat without interference control at d1 = 1 km and d2 = 5 m.
Therefore, the proposed method is eﬀective in controllinginterference between PS-Rx and SS-BS when intersystem
distances are short.
4.3 Transmission Characteristics at SS
This section focuses on the transmission characteristics at
SS when the base station of SS controls transmitted powerso as to achieve the desired SIR at PS-Rx. Figure 10 showsa transmission system model at SS. SS-BS is the base sta-

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Fig. 11
Fig. 10

Average SNR at SS versus d3 .

Transmission system model at SS.

tion, SS-UE is the user equipment, and both systems have
two antennas each (MSB = MUE = 2). The operation frequency of SS is 700 MHz. The propagation channel H IS T is
the interference channel from PS-Tx to SS-UE. In this paper, the channel estimation error of H IS T is inevitable due tothe noise. Nevertheless, SS-UE can estimate ideal H IS T bytraining for a long time. This will enable SS-UE to suppress
H IS T , and the interference suppression process at SS-UE is
described in the following. By the SVD of H IS T , we get,
H IS T = [Ū IS T , Ũ IS T ][SIS T , 0]T V H
IS T ,(11)

where Ũ IS T is the null-space weight vectors from PS-Tx.
The equivalent channel between PS-Tx and SS-UE becomes,
H

HIS T = Ũ IS T H IS T ,(12)when the receiver weight, Ũ IS T , is used. In this study, we assume the ideal environment, i.e., SS-UE completely cancels
the interference, H IS T . Also, HSS represents the self-linkchannel at SS, and the equivalent is expressed as,
H

HSS = Ũ IS T HSS Ṽ RS .

(13)

H IS T and HSS are also calculated by the ring model and thepath-loss is taken into account. By using (13), the channel
capacity at SS can be approximately calculated by [24],  HPSB
C = log2  HSS HSS+ ISS ,
(14)
2


(MSB − NNull )σnwhere PSB is the transmitted power of SS-BS and Nnull is thedegree of freedom which SS-BS uses in order to suppress
the interference. ISS is an identity matrix and σ2n is the powerof Gaussian noise.
Figure 11 shows average SNR at SS versus the distance between SS-BS and SS-UE, d3 . In this figure, the solidand dashed lines represent the SNR with PS and proposed
scheme and the without PS, where the transmitting power of
SS-BS, PSB is given identically for both scenarios. Here, theSNR with PS is calculated by assuming SS-BS suppresses
interference against PS-Rx and SS-UE cancels the interference from PS-Tx, i.e. SS-BS and SS-UE have only one remaining degree of freedom for their communication link.

Fig. 12

Average channel capacity at SS versus d3 .

On the other hand, the SNR without PS in calculated by assuming SS-BS and SS-UE do not suppress the interference
because they have neither the PS-Rx nor PS-Tx, i.e. both of
the SS-BS and SS-UE can use the full degree of freedom
for their communication link. Therefore, the communication links of SS with PS and without PS are SISO (SingleInput Single-Output) and MIMO (Multiple-Input MultipleOutput), respectively. The intersystem distances are set tod1 = 1 km and d2 = 5 m, and PSB = 10 dBm. When we focus on the solid lines at d3 = 5 m, it can be seen that the SNRvalues are 11.7 and 14.0 dB at SS-BS with PS and withoutPS, respectively. Note that no interference control is performed for the scenario without PS. It is found that the SNR
without PS is a little higher than that with PS since there
is no decrease in degree of freedom of SS-BS antennas bynulling.
Figure 12 shows average capacity at SS versus d3 . In
this figure, the solid and dashed lines represent the capacitywith and without PS, respectively. The simulation conditions are given identically to Fig. 11. When we focus on thesolid lines at d3 = 5 m, it can be seen that the capacities withand without PS are 4.0 and 7.3 bits/s/Hz in SS, respectively.
The reason that capacity with PS is 3.3 bits/s/Hz lower thanthat without PS is in common with consideration of Fig. 11.
It is also found that the capacity is decreased due to the existence of PS, however, it is possible to ensure the enoughcapacity in all distances. These results indicate that the pro-

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pose method allows SS to suppress the interference to PS-Rx
and communicate suﬃciently.
5.

Conclusion

In this paper, the interference control method using load
modulation has been presented for coexistence of PS-Rx
with SS-BS. In this method, a load modulation technique
is used to let us establish the interference channel between
PS-Rx and SS-BS. Also, SS-BS estimates the interference
channel by observing the modulated signals at PS-Rx side,and uses null-space vector as transmitting weights that are
obtained from the channel. A simulation of SIR with interference control showed a significant SIR improvement regardless of d2 . In particular, the interference reduction offered by the proposed method is remarkable if the intersystem distances are short, e.g., SIR with interference control
is 26.5 dB, and this SIR is 41.5 dB higher than that without interference control at d1 = 1 km and d2 = 5 m. Thus,
it is possible to reduce the interference to PS-Rx by using
proposed method. Furthermore, this paper has evaluated average channel capacity at SS. It is found that the averagecapacity with interference is reduced due to the degrees of
freedom of SS-BS antennas control compared to absence of
PS-Tx and PS-Rx (without interference control). However,it can also be seen that capacity with interference control is
4.0 bits/s/Hz when d3 = 5 m. Therefore, in nulling to PSRx, it is possible to obtain the data rate at the SS if it is a
short-range environment.
These results show that the interference control using
proposed method is eﬀective especially in close-range configurations, and is suited to small cells that share a frequency.
Acknowledgements
This research was partially supported by JSPS KAKENHI(25709030).
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IEICE TRANS. COMMUN., VOL.E98–B, NO.5 MAY 2015868

and (A· 8), HTS R is expressed as,
Appendix:

The Derivation of (10)This appendix shows the derivation of (10). From (5) inthis paper, the observed propagation channel at SS-BS is
expressed as,
HS T = HS T + HS R (1 − S RR )−1 HRT ,(A· 1)

where the state of load impedance, Z at PS-Rx is switched toopen. This channel is added to noise, so the received signal
at SS-BS is expressed as,
ySB = {HS T + HS R (1 − S RR )−1 HRT }sT + n,

(A· 2)

where sT is the transmitted signal of PS-Tx, and its power
represents PT . n is the noise per SS-BS antenna and has
dispersion, σ2n . It is expressed as,
n = [n1 , n2 , · · · , n MSB ]T ,(A· 3)

where ni (i = 1, 2, · · · , MSB ) is in a Gaussian distribution
and MSB denotes the number of SS-BS. Here, it is possible
to consider sT as known signal when SS-BS√can decode thesT . For sake of simplicity, we define sT as PT , and (A· 2)
is transformed into,n(A· 4)HS T = HS T + HS R (1−S RR )−1 ART + √ ,PTwhere ART is path-loss between PS-Tx and PS-Rx, i.e.
ART = (PR : received power √at PS-Rx)/(PT : transmitted
power at PS-Tx), and HRT = ART . As shown by (6), theterm HS T can be eliminated from (A· 4) since HS T is directly observed by switching the termination to load (50 Ω)state at PS-Rx. Therefore, HS R estimated by SS-BS is expressed as,n
HS R = HS R (1 − S RR )−1 ART + √ ,PT(A· 5)HTS R =

HT
SR

√

2nT
−
.
√
√
(1−S RR )−1 ART (1−S RR )−1 PT ART(A· 9)

Furthermore, from (8) in this paper, SS-BS can suppress interference by using Ṽ RS as the transmitting weight,
which represents null-space eigenvector and is calculated
from singular-value-decomposition of HT
S R . The interference signal is calculated by,
yiRS = HTS R Ṽ RS sS√ T
HT
2n Ṽ RS sS
S R Ṽ RS sS
=
−
√
√
−1
(1−S RR )ART (1−S RR )−1 PT ART√ T
2n Ṽ RS sS=−.
√(1 − S RR )−1 PT ART(A· 10)(A· 11)sS is the transmitted signal of SS-BS, and defined as,sS = [sS ,1 , sS ,2 , · · · , sS ,MSB ]T ,(A· 12)where sS ,i (i = 1, 2, · · · , MSB ) is in a Gaussian distribution.
HT Ṽ s
From (A· 10) and (A· 11), the term (1−SS R )−1RS√AS is elimiRR

RT

nated due to orthogonality between HTS R and Ṽ RS . As aresult, (10) in this paper is derived.

Takuma Itoreceived the B.E. in electricaland electronic engineering from Iwate University, Morioka, Japan in 2013. He is currently inthe master program in Iwate University. His research interest is interference control using load
modulation.

where n is expressed as,n = nopen − nload(A· 6)

since (A· 5) is obtained by subtracting two diﬀerent channels(i.e. HS T (open) and HS T (load) ) that contain diﬀerent noises.
Also, the power expectation of (A· 6) is calculated by,
E |nopen − nload |2= E |nopen |2 − nopen n∗load − nload n∗open + |nload |2= E |nopen |2 + |nload |2= 2σ2n ,(A· 7)

then, n can be expressed as,
n =

2n.

(A· 8)The interference from SS-BS to PS-Rx is discussed
in this following. In this study, the transposed matrix of
HS R , HTS R represents the interference channel. By (A· 5)

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, and theBest Paper Award of IEICE Communication Society in 2006, respectively. His current research interest is planar antennas
for high-speed wireless communication systems. He is a member of IEEE.

ITO et al.: INTERFERENCE SUPPRESSION METHOD BETWEEN PRIMARY BROADCASTING AND SECONDARY SYSTEMS USING LOAD MODULATION

869

Keisuke Terasaki
received the B.E. inelectrical and electronic engineering from Iwate
University, Morioka, Japan in 2012. He is currently in the master program in Iwate University.
His research interest is feasible load modulation
techniques for multiple antenna systems.

Kentaro Nishimori
received the B.E., M.E.
and Ph.D. degrees in electrical and computer engineering from 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 was senior 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
and Assistant Secretary of Technical Committee on Antennas and Propagation of IEICE from June 2008 to May 2010. He received the Young Engineer Award from IEEE AP-S Japan Chapter in 2001, Best Paper Awardof Software Radio Society in 2007 and Distinguished Service Award fromthe IEICE Communications Society in 2005, 2008 and 2010. His main interests are spatial signal processing including MIMO systems and interference management techniques in heterogeneous networks. He is a memberof IEEE. He received IEICE Best Paper Award in 2010.

Yoshitaka Tsunekawa
received the B.E.
degree from Iwate University, Morioka, Japan,
in 1980 and the M.E. and the Doctor of Engineering degrees from Tohoku University, Sendai, Japan, in 1983 and 1993, respectively. Since
1983, he has been with the Faculty of Engineering, Iwate University, where he is now a professor at the Department of Electrical Engineeringand Computer Science. His current research interests include digital signal processing and digital control. He is a member of the Instituteof Electronics, Information and Communication Engineers of Japan, andIEEE.