7/23/2019 MIMO Method for Combination

http://slidepdf.com/reader/full/mimo-method-for-combination 1/2

Antenna Decision Method for Downlink Multiuser

MIMO Systems with Receive Antenna Allocation

Tomoki Murakami, Yasushi Takatori, and Masato

Mizoguchi

 NTT Access Network Service Systems Laboratories

 Nippon Telegraph and Telephone Corporation

Yokosuka, Japan

E-mail: murakami.tomoki@lab.ntt.co.jp

Fumiaki Maehara

Graduate School of Fundamental Science and Engineering

Waseda University

Shinjuku, Japan

 Abstract  — This paper proposes an antenna decision method for

downlink multiuser MIMO (MU-MIMO) with receive antenna

allocation per subcarrier. When receive antenna allocation is

used, each receiver requires the information of the antenna

allocation before receiving the data; for this an overhead signal is

used. The proposed method decreases the overhead by deciding

an antenna with comparison of received power at each antenna of

the receiver. Computer simulations show that the proposed

method offers highly accurate antenna decision and high channel

capacity with one OFDM symbol.

I.  I NTRODUCTION 

To satisfy the growing demand for wireless communication,it is necessary to enhance the channel capacity of wirelesssystems [1]. Downlink MU-MIMO, in which a transmitter withmultiple antennas simultaneously transmits to multiplereceivers each with a few antennas, is highly promising.Wireless system standards such as IEEE 802.11ac and 3GPPLTE advanced address and specify downlink MU-MIMO.

Downlink MU-MIMO mitigates inter-stream interference (ISI) by pre-coding [2]. Several papers assume that transmitterantenna number exceeds total number of receiver antenna. Inthis scenario, transmitter greatly mitigates ISI by the degree offreedom (DoF) of its antennas. However, given the rapid rise inthe numbers of wireless devices and antennas, conventionalmethod for receivers with total antennas over transmitterantennas is hard to be used because of insufficient of DoF.

Papers [3] proposed a coordinated Tx-Rx blockdiagonalization (BD) algorithm for receivers with moreantennas than transmitter antennas. In this algorithm, one datais transmitted to each user, an initial set of receiver weights isassumed, and the optimal transmitter and receiver weights arealternatively recomputed until the solution converges to the one

with minimum power. Downlink MU-MIMO with receiveantenna allocation also has been investigated for single carriersystems [4]. In order to apply these methods to OFDM systems,each receiver requires pre-information of receiver weight orallocated antenna before receiving data. Therefore, since such

 pre-information increase in proportion to receiver number orreceive antenna number, transmission efficiency is degraded.

In this paper, we propose an antenna decision method fordownlink MU-MIMO OFDM with receive antenna allocation

 per subcarrier. The proposed method decides an antenna by

comparison of received power at each antenna of the receiverusing just a few OFDM signals, so transmission efficiency isexpected to remain high. Moreover, the proposed methodimproves the accuracy of antenna decision by receive antenna

allocation at the transmitter while taking antenna decision errorinto consideration. The simulation results show that the proposal offers high accuracy in antenna decision and highchannel capacity due to its minimal overhead.

II.  A NTENNA DECISION METHOD 

We consider a downlink MU-MIMO OFDM system wheretransmitter with M  antennas communicates with N  receivers. Inthe following, two antennas are assumed at each receiver for

the simplicity of the explanation. Hn(k )=[h1,n(k )T h2,n(k )

T ]

T ∈ℂ

2× M  

denotes channel state information (CSI) of the k th(k ∈1… K )

subcarrier between transmitter and nth  receiver. h1,n(k ) andh2,n(k ) are CSI between transmitter and the first and secondantennas of nth  receiver, respectively. In the proposed method,

the transmitter determines receive antenna per subcarrier. CSIafter receive antenna allocation is defined as ĥn(k ) which ischosen from h1,n(k ) and h2,n(k ) in accordance with the selectedreceive antenna. Transmitter must have CSI before datatransmission to calculate pre-coding weight for downlink MU-MIMO. The received signals of the k th  subcarrier at the nth receiver, yn(k ) is expressed by transmit signal x(k ) as

( ) ( ) ( ) ( ) ( ) ( ) ( )   )(ˆˆ

,1

k nk  xk k k  xk k k  y nl 

 N 

nl l 

l nnnnn   ++=   ∑≠=

whwh , (1)

where wn(k )∈ℂ N ×1 is the pre-coding weight of the k 

th subcarrier

for the nth

  receiver and nn(k ) is the additive white Gaussian

noise vectors of the k th subcarrier with variance of σ  

2. Various

 pre-coding algorithms, e.g. channel inversion, are applicable atthe transmitter.

For the overhead reduction, the proposal decides allocatedantenna by comparing the received powers of each antenna atreceiver using one OFDM symbol as training signal. Eachreceiver decides allocated antenna with minimum received

 power calculated from training signals among all antennas

. Then, we assume that pre-coding weight for trainingsignal, w'(k ), which defines the right singular vectorcorresponding to minimum eigenvalue of Hn(k ) in order tomitigate receive power of allocated antenna. However, the

304978-1-4799-7815-1/15/$31.00 ©2015 IEEE AP-S 2015

7/23/2019 MIMO Method for Combination

http://slidepdf.com/reader/full/mimo-method-for-combination 2/2

 proposal perfectly cannot decide the allocated antenna becauseof frequency selective fading. For improving the accuracy ofantenna decision, this paper also proposes receive antennaallocation while taking antenna decision error intoconsideration. In the proposal, transmitter calculates signal tointerference and noise power ratio (SINR) at the each receivercalculated by

( )( ) ( )

( ) ( )∑≠=

′+

′=

 N 

nl l   F 

l n

 F nn

n

k k 

k k k SINR

,1

22

2

ˆ

ˆ

wh

wh

σ  

, (2) 

where ĥʹn(k ) is CSI of the k th subcarrier between transmitter andestimated antenna of nth receiver which transmitter estimates bycomparison between ||h1,n(k )w'(k )|| F 

2  and ||h2,n(k )w'(k )|| F 2. The

antenna combination of maximum SINR improves channelcapacity and minimizes antenna decision error.

III.  PERFORMANCE EVALUATION 

We assume that transmitter with four antennas transmits tofour receivers with two antennas. To comply with the IEEE802.11 standard, the bandwidth and subcarrier number are 20MHz and 52, respectively. Average signal to noise ratio (SNR)

 between transmitter and each receiver is 30 dB. CSI is anormalized flat fading channel as independent and identicallydistributed zero-mean complex Gaussian. As the antennaallocation method, this paper assumes the greedy algorithm,which allocates an antenna combination that maximizes thetotal SINR from among all combinations. Fig. 1 shows thecumulative distribution function (CDF) of the channel capacity

 per subcarrier by using the coordinated Tx-Rx BD algorithm[3] and MU-MIMO-OFDM with receive antenna allocation forthe potential analysis of channel capacity. In the coordinatedTx-Rx BD algorithm, pre-coding and receiver weights areupdated 10 times. Antenna allocation performed randomly or in

optimal manner. Moreover the result of single user MIMO(SU-MIMO) with time division multiplexing access (TDMA)is also plotted. From these results, the coordinated Tx-Rx BDalgorithm achieves higher channel capacity than the othermethods. This is because all receiver antennas are possible to

 be used. However, its demerit higher computational loads forreceiver weight multiplication and greater overhead fornotification of quantized receiver weight. On the other hand,the channel capacity of downlink MU-MIMO-OFDM withreceive antenna allocation per subcarrier is similar to that ofcoordinated Tx-Rx BD algorithm even though each receiveruses only one antenna. This is because diversity gains ineffectively obtained by allocating antenna per subcarrier.

Fig. 2 shows the CDF of the channel capacity per subcarrier

of downlink MU-MIMO-OFDM with receive antennaallocation by using the proposal. This simulation assumed threeantenna decision variants. The first and second methods do notuse and use the pre-coding of the proposal, respectively. Thelast method performs receive antenna allocation at thetransmitter while taking antenna decision error intoconsideration. We find that the channel capacity of the methodwithout pre-coding is greatly degraded because of the manyantenna decision errors created by frequency selective fading.By using the pre-coding of the proposed method, the channel

capacity improves by 120 % at 10 % CDF value. However,since this pre-coding cannot perfectly mitigate the received

 power, antenna decision error still occurs in response to thefrequency selective fading. Finally, the antenna allocation withthe proposed method achieves the channel capacity close tooptimal because of the very small decision error. In detail, the

 proposed method improves the channel capacity by 80% at

10% CDF value.

IV.  CONCLUSION 

This paper proposed an antenna decision method fordownlink MU-MIMO OFDM with receive antenna allocation

 per subcarrier. The proposal realizes antenna decision bycomparing received powers of each antenna at the receiver with

 just a few OFDM symbols. By using proposed method, antennadecision is possible by little overhead. Simulation resultsclarified that the proposal achieves to higher antenna decisionaccuracy and channel capacity than the conventional method.

R EFERENCES 

[1]  Riichi Kudo, et al., “An advanced Wi-Fi data service platform coupled

with a cellular network for future wireless access,” IEEE communicationmagazine, vol.52, issue:11, pp.46-53, Nov. 2014.

[2] 

Q.H. Spencer et al., " Zero-forcing methods for downlink spatialmultiplexing in multiuser MIMO channels",  IEEE Trans. on Signal

 Processing , vol.52, no.2, pp.461-471, Feb. 2004.

[3] 

M. Codreanu et al., "Joint design of Tx-Rx Beamforers in MIMOdownlink channel",  IEEE Trans. on Signal Processing , vol.55, no.9,

 pp.4639-4655, Sept. 2007.

[4]  M. Sadek et al., "Active antenna selection in multiuser MIMOcommunications",  IEEE Trans. on Signal Processing , vol.55, no.4,

 pp.1498-1510, April 2007.

0 2 4 6 8 10 120

20

40

60

80

100

SU-MIMO (TDMA)

Coordinated Tx-Rx BD algorithm

MU-MIMO with optimal antenna allocation

MU-MIMO with random antenna allocation

Channel capacity [bit/s/Hz]

   C   D   F   [   %

   ]

 

Fig. 1. CDF of channel capacity per subcarier

0 2 4 6 8 10 120

20

40

60

80

100

Channel capacity [bit/s/Hz]

   C   D   F   [   %

   ]

Optimal allocation

Random allocation

Proposal

with pre-coding

without pre-coding

 

Fig. 2. CDF of Channel capacity per subcarrier 

305