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February 2012, 19(1): 1117www.sciencedirect.com/science/journal/10058885 http://jcupt.xsw.bupt.cn

The Journal of China

Universities of Posts and

Telecommunications

Interference-aware uplink transmission schemesfor multicell MIMO system

LI Liang, QIU Ling (), WEI Guo

Wireless Information Network Laboratory, University of Science and Technology of China, Hefei 230027, China

Abstract

In this paper we consider interference-aware uplink transmission schemes for multicell multiple-input

multiple-output (MIMO) system. Unlike conventional transmission schemes without considering the interference probably

caused to other cell, we jointly optimize the transceiver beamforming vectors to maximize the desired signals whileremoving the intercell interference. Specifically, for a two-cell system where each transmitter is equipped with two

antennas, we derive the closed-form expression for the transmit scheme called coordinated beamforming (CBF)

via generalized-eigen analysis. Moreover, when asymmetric interference is considered, we give a balancing

beamforming (BBF) scheme where the interfering transmitter is to strike a compromise between maximizing the desired

signal and minimizing the generated interference. Simulation results show that both schemes perform better than

conventional schemes under different scenarios.

Keywords interference-aware, MIMO system, coordinated beamforming, interference alignment

1 Introduction

The growing demands on mobile networks to support

data applications at higher throughputs and spectral

efficiency have driven the need to develop low frequency

reuse factor deployment, which has now been actively

considered for many beyond-3G cellular systems such as

IEEE 802.16 m, 3GPP long term evolution (LTE) [1], and

the newly emerging cognitive radio (CR) wireless

network [2]. Due to transmit power limitations in mobile

stations, the constraint on the uplink link budget will

necessitate the need for smaller cell sizes than are typically

deployed for present cellular systems. All these factorsresult in an interference limited system, in which the

performance of conventional transmission schemes

degrades seriously. Hence interference mitigation at the

receiver, transmitter, or both is of utmost importance [34].

There have been abundant works focusing on the

transmission schemes such as cooperative transmission or

Received date: 21-06-2011

Corresponding author: QIU Ling, E-mail: [email protected]

DOI: 10.1016/S1005-8885(11)60221-5

coordinated beamforming in downlink which are shown to

be instrumental in optimizing the rates in an interference

channel [56], whereas in the uplink the research mainly

suggests different kinds of receiver schemes. The

ambitious approach, multicell processing also referred as

network MIMO, towards lifting the limits imposed by

intercell interference on the uplink spectral efficiency has

been widely discussed [79], in which cooperative

decoding is performed amongst multiple base stations.

However, multicell processing requires not only the

knowledge of channel-state information (CSI) but also the

symbol constellation of each interferer as well. Moreover

the latency introduced by exchange of information makes

the real time processing of interference cancellation from

adjacent base stations unfeasible [4]. In fact, all these

works have neglected the capability of interference

mitigation at the mobile stations when multiple antennas

are equipped, which is the concern of this paper.

Since the conventional singular value decomposition (SVD)

based uplink transmission scheme [10] does not consider

the interference towards/from other cell, some works [2,11]

have been done to develop new uplink transmission

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12 The Journal of China Universities of Posts and Telecommunications 2012

schemes to handle the intercell interference. In Ref. [2],

the projected-channel SVD (P-SVD) is proposed to ensure

that the transmission is along the null-space of the

generated interfering channel. However, this scheme puts

the constraint that the number of transmit antennas must bemore than the total number of receive antennas in adjacent

cells. In Ref. [11], the signal-to-interference and noise ratio

(SGINR)-based transmission scheme is proposed to

maximize the ratio of signal to generated interference plus

noise. However it gives poor performance when the

interference is comparable to the desired signal since it

doesn't actually remove the interference.

In this paper, we focus on developing interference-aware

uplink transmission schemes that exploit multiple transmit

antennas at the mobile station to mitigate the intercell

interference for multicell MIMO systems. For simplicity,we consider two-cell scenario only, which works

reasonably for linear cell layout where the dominant

interference from adjacent cells is only concerned. Based

on the constraint of zero-interference after MMSE

equalization, we jointly optimize the transceiver

beamforming vectors and derive the closed-form

expression for the transmit beamforming called CBF via

generalized-eigen analysis. As we will see, the scheme in

fact works similarly as the interference alignment [9], and

full degrees of freedom (DoF) is achieved when both

nodes are equipped with two antennas [12]. Howeverwhen the asymmetric interference is considered, nulling

interference at both transmitters is unnecessary. Thus we

further give a BBF scheme where only the interfering

transmitter is responsible for interference mitigation,

which is to strike a compromise between maximizing the

desired signal and minimizing the generated interference.

Then the meaningful different user rate profiles can be

obtained.

The rest of this paper is organized as follows. Sect. 2

describes the system model and formulates an optimization

problem. In Sect. 3, we drive the CBF and BBF schemeunder different scenarios. Simulation results are presented

in Sect. 4, and conclusions are drawn in Sect. 5.

We define here some notations used throughout this

paper. We use boldface capital letters and boldface small

letters to denote matrices and vectors, respectively, T( )

and H( ) to denote transpose and conjugate transpose,

respectively, det( ) to denote determinant of a matrix,

( )(max)v A (resp. ( )(min)v A ) to denote the eigenvector

corresponding to the largest (resp. smallest) eigenvalue of

A , 1( ) to denote matrix inversion, to denote

Euclidean norm of a vector, to denote perpendicular,

NI to denote the N N identity matrix.

2 System model

We consider an uplink MIMO system comprised of two

cells. Time division duplex (TDD) system is assumed to

explore the channel reciprocity. The mobile station (MS)

and base station (BS) are equipped with tN transmit

antennas and rN receive antennas, respectively. It is

assumed that a single MS is selected by a user scheduler at

the given time and frequency in each cell, as has been

adopted in wideband system, thus intracell interference is

not considered. The ith transmitter (the MS in the ith cell)

communicates with the ith receiver (the BS in the ith cell)

only, resulting in an interference channel, by transmitting

with si

N streams using a t si

N N precoding matrixi

F .

The received signal at ith receiver can be expressed as

, , ,i i i i i i i j i j j j i = + +y H F x H F x n (1)

where ,i iH denotes the r tN N direct-link channel

matrix for ith MS, while ,i jH denotes the r tN N

cross-link channel matrix from thejth ( , 1i j= or 2, i j )

receiver and the ith transmitter,i

x denotes s 1i

N

symbol vector transmitted from the ith transmitter. Each

element of ,j iH and ix is assumed to be independent

and identically distributed (i.i.d.) circularly symmetric

complex Gaussian random variables with zero mean and

unit variance.i

n denotes an r 1N additive white

Gaussian noise (AWGN) vector with unit variance per

entry.i

denotes the signal-to-noise ratio (SNR) of the

ith cell, ,i j denotes the interference-to-noise ratio (INR)

at the ith receiver caused by the jth transmitter. Both the

SNR and INR include the transmitted power and

independent long-term fading comprised of the path loss

and log-normal shadow fading implicitly. Then theachievable rate of the ith cell is given by

r

1 H

, ,lg det[ ( ) ]i N i i i i i i i iC = +I R H F H F (2)

where 1i

R denotes the covariance matrix of the noise

plus interference signal at the ith receiver expressed as

r

H

, , ,( )i N i j i j j i j j= +R I H F H F (3)

The optimization problem for finding precoding

matrices that maximize the total achievable rate can be

formulated as

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Issue 1 LI Liang, et al. / Interference-aware uplink transmission schemes for multicell MIMO system 13

1 2

opt opt

1 2 1 2,

H

( , ) arg max( )

s.t. tr( ) =1 for alli i

C C

i

= +

F F

F F

F F

(4)

Apparently this is a non-convex problem, it is

impossible to find a closed-form solution. In the nextsection, we will give our proposed interference-aware

transmission schemes.

For comparison purpose, we first give the conventional

transmission schemes [10], in which the interference is not

considered. Conventionally, when only direct CSI is

available at the transmitter, the SVD-based transmission is

favorable to egoistically maximize its own desired signal

gain. Perform SVD to ,i iH asH

,i i =H U V , then the

precoding matrixi

F is given by

1

1/ 2

; single stream

; multiple streamsi

=

V

F VP (5)

where 1 V denotes the singular vector corresponding to

the maximum singular value, and P is the power

allocation matrix which can be determined by the standard

water-filling [10] over the SNR of each stream.

3 Interference-aware transmission schemes

Since we focus on the interference-limited system,

where cell-edge users are of concern, the most appropriate

strategy is to take single-stream transmission, thus t 1N

beamforming vectori

F is to be designed. Moreover, to

get a closed-form expression as we will see later, we

assume that each transmitter is equipped with two antennas

which is also a typical configuration for the upcoming

fourth generation (4G) wireless system such as LTE [1]. In

this section, we assume that every channel matrix ,i jH is

available at the BSs, if possible where deployment has

been established to facilitate the communication between

BSs[4], to coordinately design the transceiver

beamforming vectors. We first consider the symmetric

interference scenario, where both MSs are cell-edge located,and derive a closed-form expression for the transmit

beamforming called CBF via generalized-eigen analysis.

Then the asymmetric interference is considered. We give a

BBF scheme where the interfering transmitter is to strike a

compromise between maximizing the desired signal and

minimizing the generated interference.

3.1 CBF

We assume that each BS performs conventional single

user detection, against the cooperative decodingby using

linear SINR-optimal MMSE receiver to detect the desired

signal. Then the receive beamforming for the ith receiver

is expressed as1

,i i i i i i

=w R H F (6)where the

iR is expressed as Eq. (3). The estimate of

ix ,

which is denoted by i

x , can be obtained using Eqs. (1) and

(6) as

(H H H 1, ,i i i i i i i i i i i i ix = = +w y F H R H F x

), , i j i j j j i +H F x n (7)The design goal is to remove the other-cell interference

term after MMSE equalization, such that the estimate i

x

is devoid of interference. Namely, H H 1, , 0i i i i i j j =F H R H F

is expected. Note that the jointly optimized transceiverbeamforming design is different from the null-space

projection [2] which can be applied only when the number

of transmit antennas is larger than the total number of

receive antennas in adjacent cells.

Before introducing our results in Theorem 1, we review

the followingLemma [13] on matrix inversion:

Lemma 1 (Woodbury formula):1 1 1 1 1 1 1( ) ( ) + = +A UBV A A U B VA U VA (8)

where , U , B and V all denote matrices of the

correct size. LetrN

= I , , ,i j i j j=U H F ,H=V U ,

1=B , then we have

r

H

, , ,1

H

, , ,

( )

1 ( )

i j i j j i j j

i N

i j i j j i j j

= +

H F H FR I

H F H F (9)

Apply Eq. (9) into the zero-interference constraint, then

we haveH H

, ,H H 1

, , H

, , ,1 ( )

i i i i j j

i i i i i j j

i j i j j i j j

= =+

F H H FF H R H F

H F H F

H H

, , 0 0i i i i j j =F H H F (10)

Then the solution of optimized transmit beamforming

under the constraint of zero-interference after MMSEequalization is given by the following Theorem 1.

Theorem 1 For two-cell system where the antenna

configuration is t r2, 2,N N= the transmit beam-

forming vector for transmitter iunder the Eq. (10) is given

by the set of generalized eigenvectors of H, ,i j i iH H and

H

, ,j j j iH H .

Proof From the zero-interference constraint Eq. (10),

we have

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H H H

, , , ,

H H H

, , , ,

0

0

i i i i j j j i j i i i

j j j j i i j j j j i i

=

=

F H H F F H H F

F H H F F H H F (11)

which means that thej

F lies in the null-space of

H

, ,i j i i iH H F andH

, ,j j j i iH H F respectively. Since jF istwo-dimensional non-zero vector, the null-space of

jF is

one-dimensional. As each channel is random and comes

from a continuous distribution, so H, ,i j i iH H will have full

rank with probability one, H, ,i j i i iH H F andH

, ,j j j i iH H F

must be non-zero vector and co-linear, which means thatH H

, , , ,i j i i i j j j i i=H H F H H F (12)

for some constant . By solving Eq. (12) according to the

typical generalized eigen-problem [13],i

F is given by

the generalized eigenvectors of H, ,i j i iH H andH

, ,j j j iH H ,

andj

F can be easily calculated from any one of the

constraint in Eq. (11).

Note that the CBF derived from the generalized eigen

analysis has similar form as the one in Ref. [5], which was

originally for single cell MIMO broadcast channel where

only one base station served two mobile stations.

Moreover, when the zero-interference constraint Eq. (11) is

satisfied, the MMSE receive beamforming in Eq. (6)

degenerates to the maximum ratio combining

,i i i i i=w H F , thus the receive process is simplified due

to the omitted estimation of the covariance of interferenceand noise, which is valuable especially in wideband

systems where the covariance has to be estimated for every

carrier.

As we will see later, our proposed CBF works

efficiently for high asymmetric interference scenario since

it is originally proposed to remove the interference.

However, when the interference is not such destructive, for

example, when the interference-dependent coordinated

scheduling has been adopted, the conventional

transmission scheme maximizing the desired signal will be

naturally preferable. Next we will consider a morepractical scenario that in two pair transceivers only one of

transmitters generates non-negligible interference to its

non-intended receiver.

3.2 BBF

As an effective means to handle the other-cell

interference, the coordinated scheduling (CS) has been

widely accepted [4] where the same frequency-time

resource block (RB) is kept from allocating to neighboring

cell-edge MSs that belong to neighboring cells respectively.

However, the CS does not completely avoid interference

among adjacent cells because the same RB used by a

cell-edge MS may be used by in-cell MSs from theadjacent cells, and thus the adjacent cell is interfered. This

can be referred as non-asymmetric interference scenario,

which is to be considered in this subsection.

Without loss of generality, we assume the MS1 is the

in-cell one, while the MS2 is at the cell edge with

non-negligible interference generated to cell1. That is,

1,2 is comparable to 1 , denoted as 1,2 1 , while

2,1 2 . For in-cell MS1, since it does not generate

noticeable interference to the other cell, the optimal choice

is to use eigenvector corresponding to the maximum

eigenvalue of H1,1 1,1( )H H to maximize its desired signal

power. That is

( )(max) H1 1,1 1,1( )v=F H H (13)

As this paper is addressed to design interference-aware

transmission scheme, so the transmit beamforming vector

of MS2, 2F , is expected to be derived according to

Eq. (10), that isH H H

1 1,1 1,2 2 2 1,2 1,1 10= F H H F F H H F (14)

Interestingly, when conventional multi-stream transmission

is adopted for in-cell MS1 to enhance its achievable rate,

the Eqs. (13)(14) still holds for the case where the

interference is suppressed along the dominant signal

direction.

However, the use of Eq. (14) causes performance

degradation of MS2. Then for the purpose of enhancing

the total sum rate through interference suppression while

guaranteeing the performance of MS2, we propose a

balancing beamforming scheme for MS2 to strike a


minimizing the generated interference. We denote the

optimal beamforming vector for MS2 to maximize its

desired signal gain as ( )des (max) H2 2,2 2,2( )v=F H H , denote

the basis for the left null-space of des2F as nullB , whereH des

null 2 0=B F (15)

Then both conditions of Eqs. (14) and (15) are expected

to be satisfied, that isH H

2 nullnull( ) null( ) F A B (16)

where H1,2 1,1 1= H H F denotes the orthogonal vector.

Then we accordingly construct the compound matrix

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( )H

=C A B , since apparently we have null( )=C H H

nullnull( ) null( )A B . So the balancing beamforming

vector 2F is derived as

( )min H

2 v=

F C C (17)Thus, from Eq. (17) we can get a balancing solution for

MS2.

Although the total achievable rate may not be improved

significantly compared to the conventional scheme,

however, the result shows meaningful when different rate

profiles are preferred. For example, for cellular systems the

fairness between MSs can be improved. Moreover, we can

also refer to cognitive network (CN) [2], since the problem

formulation and solution in this subsection fall under the

principle of CN, where MS1 is the so-called primary

user (PU) who is the legitimate user, while MS2 is thesecondary user (SU) or CR who transmits simultaneously

with the PU over the same RB provided that its

transmission will not cause the PUs performance to

degrade to an unacceptable level.

3.3 Feasibility analysis

Generally, we can conclude that the interference-aware

transmission scheme can be implemented as the following

procedure. From the perspective of reciprocity, it is

generally tenable that when a MS receives stronginterference from one neighboring BS it must interfere

with that BS at the same time. So in the first step, when a

cell-edge MS detects a strong interference from

neighboring cell, it reports the corresponding CSI and

interfering cell ID to its home BS. In the second step, the

BSs exchange CSI between each other and the coordinated

beamforming vectors for a second round. At last, the BSs

inform its home MS the transmit beamforming vector

through control channel [10] or elaborated downlink

pilots [14]. Since our proposed schemes are interference-

aware, only when the interference is strong enough thatthese steps will be executed.

The proposed schemes can be practically reasonable for

the following reasons: First, since in cellular systems,

mobile stations periodically monitor pilot channels of

neighboring base stations to assist with handoff [3], these

pilot signals could potentially be used to gather the

required CSI. Second, communication between base

stations already occurs in order to coordinate handoffs and

network-level operations. As for the third reason,

especially, when the downlink cooperative transmission is

adopted, which has been widely discussed [6], the

available CSI and already widely existed communication

mechanism could be shared for the uplink directly without

additional signaling for the first two steps. From thediscussion above, we can see that the proposed schemes do

not need much additional complexity of process, and can

alleviate the burden of the receiver effectively. Since the

schemes are essentially BS-centric, no additional processes

are needed for transmitter compared with conventional

schemes when the transmitter is informed of the

beamforming vector through the control channel [10].

4 Numerical results and discussion

In this section, we investigate the performance of the

proposed schemes in comparison with several already

existed schemes, namely, the conventional SVD-based

scheme [1011] with single or multiple streams (C-SVD-S,

C-SVD-M, respectively) according to Eq. (6), the

SGINR-based transmission scheme (Max-SGINR) [11].

The upper bound of two links with free-interference is also

given, which is expressed as

r

2

upper upper

1

H

upper , ,lg det( ( )

i

i

i

N i i i i i i i

C C

C

=

=

= +

I H F H F

(18)

where the iF is given by the same mean as C-SVD-S.

For fair comparison, both Max-SGINR and upper bound

take single stream transmission as our proposed schemes.

Unless otherwise stated, the long-term power control [4] is

assumed to perfectly compensate for the long-term fading

so that a given target SNR is satisfied at the BSs.

We first consider a symmetric system in Figs. 1 and 2,

where 1 2 1,2 2 1= , = . Fig. 1 shows the average

achievable rate per cell vs. INR with t r 2N N= = , and

SNR=20 dB. The P-SVD scheme is not plotted in this

MIMO configuration due to the lack of transmit antennas.

Clearly, the conventional schemes will perform better in

low INR region. However, since we are focusing on

interference-limited scenario, the high INR region is of

concern. As we can see in Fig. 1, the proposed CBF is

non-sensible to interference, thus shows its superiority in

high INR region due to its ability of maximizing the

desired signal while removing intercell interference. As for

C-SVD-S, in fact the interference-aware is implicated

since the receiver is left one more degree of freedom to

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suppress the interference and thus perform better than

C-SVD-M and Max-SGINR. Even though our proposed

CBF still perform better with about 6.6%, 14%, 121% gain

respectively when the INR equals to SNR.

Fig. 1 The average achievable rate vs. INR for t r= 2N N =

and SNR is 20 dB

Fig. 2 The average achievable rate vs. SNR fort r= 2N N =

and INR equals to SNR

In Fig. 2, we study the achievable rate vs. SNR with

t r 2N N= = and INR equals to SNR, which can be

practically scenario when two MSs are all located at the

boundary of the two cells. As we can see, the proposed

CBF gives better performance in the most regions of SNR,

and as the SNR increases, the proposed CBF shows the

same asymptotic performance as the upper bound while

the C-SVD-M becomes interference-limited very quicklydue to the lacked freedom for interference suppression. In

fact, our proposed CBF works the similar way as the

interference alignment [9] due to the zero-interference

constraint, and full DoF is achieved when both nodes are

equipped two antennas, as has been proved in Ref. [13].

Then in Figs. 35, we consider the asymmetric

interference scenario where only one link generates

non-negligible to the other link. First in Fig. 3, we briefly

compare the performance of different schemes under the

asymmetric interference. When the weak interference is

comparable with noise, the BBF gives best performance

due to the idea of balance, while when the interference gets

strong, CBF performs best as the way previously

discussed.

Fig. 3 The average achievable rate vs. Asy-INR for

t r= 2N N = and 1 2 1,2= 20 dB = = , 2,1Asy-INR=

Then we focus on the discussion of the property of the

proposed BBF with 2,1 0 = in Figs. 45. This can be

referred to the coordinated cellular system where only the

cell-edge users interfere with the neighboring cell or the

cognitive network where the SU carefully selects a PU so

as not to be interfered by it, while it altruistically designs

beamforming vector to assure that the PUs performance is

not affected. In Fig. 4, the achievable rates of MS1 and

MS2 under different transmission schemes are

characterized. For the conventional C-SVD-M unaware of

interference, the performance of MS1 quickly becomes

interference-limited, while MS2 alone experiences the

interference-free channel. When the BBF scheme is

applied to minimize the interference from MS2 to MS1,

the performance of MS1 gets a significant increase while

the MS2 suffers mild performance degradation. Roughly,

the proposed BBF scheme provides 147% and 13.7%

improvement at SNR equal 20 dB for the performance of

MS1 and average rate respectively. This is an inspiring

result for the cellular systems where the fairness between

MSs is guaranteed and average performance is well

enhanced or for the cognitive network where the PU is

well protected while the SU still get a fairish performance.

We further investigate the performance in the scenario

where the receiver has no ability of interference

suppression, e.g., r 1N = , in Fig. 5. In the figure, the

proposed BBF scheme provides 246% and 44.9%

improvement at SNR equal 20 dB for the performance of

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MS1 and average rate respectively.

Fig. 4 The average achievable rate vs. SNR for

t r= 2N N = and 1 2 1,2 2,1= , 0 = =

Note that although both proposed algorithms show their

superiority, when interference is low enough, conventionalscheme is good enough and coordination can be omitted.

Thus our results can be used as guideline to conduct the

adaptive strategy selection, as has been discussed in

Ref. [15].

Fig. 5 The average achievable rate vs. SNR for t =2,N

r 1N = and 1 2 1,2 2,1= , 0 = =

5 Conclusions

In this paper, we have developed two effective uplink

transmission schemes to handle the intercell interferencefor a two-cell MIMO systems. For a two-cell system where

the transmitter is equipped with two antennas, we derive

the CBF via generalized-eigen analysis under the

constraint of removing the interference after receiver

equalization. We also consider the asymmetric interference

scenario, where a BBF scheme is given to strike a


minimizing the generated interference. Simulation results

confirmed that both schemes outperformed the

conventional schemes under different scenarios. Although

we considered uplink scenario in this paper, similar results

can be directly applied for the downlink counterpart due to

the reciprocity, as in Fig. 5 for example. More general

cases like more than two cells scenario are left for thefuture work.

Acknowledgements

This work was supported by Chinese Important National Science

and Technology Specific Project (2010ZX03002-003-01).

References

1. 3GPP TR25.814. Physical layer aspects for evolved universal terrestrialradio access (UTRA). 2006

2. Zhang R, Liang Y C. Exploiting multi-antennas for opportunistic spectrumsharing in cognitive ratio networks. IEEE Journal of Selected Topics in

Signal Processing, 2008, 2(1): 881023. Andrews J G, Choi W, Heath R W. Overcoming interference in spatial

multiplexing MIMO cellular networks. IEEE Wireless Communications,

2007, 14(6): 951044. Boudreau G, Panicker J, Guo N, et al. Interference coordination and

cancellation for 4G networks. IEEE Commun Magazine, 2009, 47(4):

74815. Chae C B, Mazzarese D, Jindal N, et al. Coordinated beamforming with

limited feedback in the MIMO broadcast channel. IEEE Journal on Selected

Areas in Communications, 2008, 26(8): 150515156. Zhang R, Hanzo L. Joint and distributed linear precoding for centralized and

decentralized multicell processing. Proceedings of the 72nd Vehicular

Technology Conference (VTC-Fall10), Sep 69, 2010, Ottawa, Canada.Piscataway, NJ, USA: IEEE, 2010: 5p

7. Venkatesan S. Coordinating base stations for greater uplink spectralefficiency in a cellular network. Proceeding of the 18th IEEE InternationalSymposium on Personal, Indoor and Mobile Radio Communications

(PIMRC07), Sep 37, 2007, Athens, Greece. Piscataway, NJ, USA: IEEE2007: 5p

8. Chatzinotas S, Imran M, Hoshyar R. On the multicell processing capacity ofthe cellular MIMO uplink channel in correlated rayleigh fading environment.

IEEE Wireless Communications, 2009, 8(7): 370437159. Chatzinotas S, Ottersten B. Interference alignment for clustered multicell

joint decoding. Proceedings of the Wireless Communications and

Networking Conference (WCNC11), Mar 2831, 2011, Cancun, Mexico.Piscataway, NJ, USA: IEEE, 2011: 19661971

10. Berardinelli G, Srensen T B, Mogensen P, et al. SVD-based vs. release 8codebooks for single user MIMO LTE-A uplink. Proceedings of the 71st

Vehicular Technology Conference (VTC-Spring10), May 1619, 2010,Taipei, China. Piscataway, NJ, USA: IEEE, 2010: 5p

11. Lee B O, Je H W. A novel uplink MIMO transmission scheme in a multicellenvironment. IEEE Wireless Communications, 2009, 8(10): 49814987

12. Gou T, Jafar S A. Degrees of freedom of the K user M N MIMOinterference channel. IEEE Transactions on Information Theory, 2010,

56(12): 6040605713. Horn R A, Johnson C R. Matrix analysis. Cambridge, UK: Cambridge

University Press, 198514. Hara Y, Oshima K. Remote control of transmit beamforming in

TDD/MIMO system. Proceedings of the 17th IEEE InternationalSymposium on Personal, Indoor and Mobile Radio Communications

(PIMRC06), Sep 1114, 2006, Helsinki, Finland. Piscataway, NJ, USA:IEEE, 2006: 5p

15. Zhang J, Andrews J G. Adaptive spatial intercell interference cancellation inmulticell wireless networks. IEEE Journal on Selected Areas in

Communications, 2010, 28(9): 14551468

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