Copyright 978-1-4673-5828-6/13/$31.00 ©2013 IEEE

Novel Adaptive Channel State Information Feedback for Multiuser MIMO in

Wireless Broadband Communications

Maneesha Sharma Faculty of Electrical Engineering and Computer Science

Queensland University of Technology

Brisbane, Australia

m1.sharma@qut.edu.au

Abstract—The availability of Channel State Information (CSI)

at the transmitter in Multi User –Multiple Input Multiple

Output (MU-MIMO) has been a popular research topic in

recent years. The CSI makes it possible to adapt transmissions

to current channel conditions, which is crucial for achieving

reliable communication with high data rates in multi antenna

systems. Basically there are two methods that allow the

transmitter to obtain CSI from the receiver. This paper

highlights both of these mechanisms and presents the basis of a

novel feedback mechanism that efficiently provides the

transmitter with minimum CSI to allow knowledge of a rapidly

changing channel at the transmitter end of a MU-MIMO

system. Preliminary simulation results show that channel

capacity in a 4 X 4 MIMO system increases by up to 20% with

the availability of CSI at the transmitter.

Keywords- Multiuser MIMO, Channel State Information,

Channel Reciprocity, Feedback Mechanisms

I. INTRODUCTION

Multiple Input Multiple Output (MIMO) technology constitutes a breakthrough in the design of wireless communication systems. The technology offers a number of benefits that help meet the challenges posed by both the impairments in the wireless channel as well as resource constraints. In addition to the time and frequency dimensions that are exploited in conventional single-antenna wireless systems, the leverages of MIMO are realized by exploiting the spatial dimension provided by the multiple antennas at the transmitter and the receiver.

The channel capacity of MIMO system increases linearly with the increase in the number of antenna pairs between transmitter and receiver. In addition, several works have shown that the capacity in MIMO can be further improved if the forward Channel State Information (CSI) is made available at the transmitter. Accurate CSI is much more crucial in MU-MIMO since having approximate CSI results in residual interference, incurs a significant loss in throughput, and does not achieve the full multiplexing gain

The Shannon’s capacity formula is the baseline for the derivation of MIMO channel capacity equations. If Q denotes the covariance matrix of the transmitted Gaussian signal of total radiated power P, then the Shannon’s capacity for a fading MIMO channel with additive white Gaussian noise (AWGN) is given by: C=log2|IN + HQH

†| b/s/Hz (1)

where |.|, (†), H and IN represent the determinant, transpose-conjugate , N x M channel matrix and N x N identity matrix, respectively. Furthermore, for the equal power uncorrelated sources (where CSI is not available), Q = (ρ/M) IN. Thus the capacity equation becomes

C=log2|IN + (ρ/M) HH†| b/s/Hz (2)

where ρ represents SNR and M represents total equal power for uncorrelated sources.

The remainder of this paper is organized as follows. In section II, the feedback mechanisms in MIMO and preliminary simulation results are presented. Section III describes the research problem and main aims of the research. Section IV discusses the methodology and research plan that will be implemented throughout this research. Finally, the conclusion and future work are presented in Section V.

II. FEEDBACK MECHANISMS IN MIMO

TECHNOLOGY

The design and implementation of MIMO still faces challenges, one of the most relevant is the availability of CSI at the transmitter. The transmitter can only acquire CSI indirectly, since the signal enters the channel only after leaving the transmitter. The receiver, however, can estimate the channel by inserting pilots in the transmitted signal.

The reciprocity principle in wireless communication assumes that the channel from an antenna A to another antenna B is identical to the transpose of the channel from B to A, as shown in Fig. 1. The reciprocity principle only holds true if both forward and reverse links occur at the same frequency, the same time and same antenna locations. The conditions that must be met are the time lag Δt between the forward and reverse transmissions must be much smaller than the channel coherence time TC i.e. Δt <TC .

Similarly, any frequency offset Δf must be much smaller than the channel coherence bandwidth BC i.e. ∆f < BC .

And the antenna location differences on the two links must be much smaller than the channel coherence distance DC.

The other method of obtaining CSI at the transmitter is using feedback channel from the receiver of the forward link; this is also shown in Fig. 1. The channel is measured at the receiver at B during the forward link (A to B) transmission, and the information is sent to the transmitter at A via a feedback channel.

Feedback is not limited by the reciprocity requirements. However, the time lag Δlag between the channel measurement at B and its use by the transmitter at A can be a source of error, unless it is much smaller than the channel coherence time i.e. Δlag ˂TC.

HA → B

HB → A

Figure 1. CSI using reciprocity and feedback mechanisms

Additionally the use of a feedback channel might cause a degradation of the overall system throughput. Especially in MIMO and multi-carrier (e.g. OFDM) systems the amount of necessary feedback to signalize the CSI dramatically increases with the number of antennas and subcarriers.

Let’s consider a MIMO system using Least-Square (LS) channel estimation. The CSI known at transmitter using feedback method can be given by

HT = H + εF (3) where H is the forward channel and εF is the NR×NT error matrix from feedback link.

Preliminary simulations were carried out in Matlab to check the performance of MIMO systems with and without the availability of CSI at the transmitter. It has been found that MIMO channel capacity increases with the availability of CSI at the transmitter as shown in Fig. 2. Therefore, the development of an efficient method of obtaining CSI at the transmitter is of great significance to multiuser MIMO systems.

0 5 10 15 200

20

40

60

80

100

SNR(dB)

Capacit

y(

bit

s/s/

Hz)

without CSIT 4x4

with CSIT 4x4

without CSIT 8x8

with CSIT 8x8

Figure 2. Capacity Vs SNR in MIMO

III. RESEARCH PROBLEM

This project aims to develop a novel adaptive feedback mechanism that will allow obtaining CSI efficiently at the

transmitter of a MU-MIMO OFDM system. It is very important to carefully consider how feedback is designed and used to support MU-MIMO. This research focuses on the following key design parameters and research questions in such a feedback process:

how often should users perform feedback considering a range of time-varying channel conditions

the most efficient method to minimize bandwidth use during feedback, and

the optimum number of users and OFDM subcarriers to consider when providing feedback

IV. METHODOLOGY AND RESEARCH PLAN

The research methodology can be illustrated in following three phases

Phase 1: The first phase consists of the detailed study of the MU-MIMO technologies, MU-MIMO channel capacity and the different feedback mechanisms that have been developed so far to obtain the channel state information at the transmitter.

Phase 2: The second phase consists of simulation of MU-MIMO system using reciprocity and feedback mechanisms. The two different methods developed so far will be simulated and compared using Matlab.

Phase 3: Design of novel channel state information feedback mechanism in MU-MIMO: The last phase consists of designing and implementing, a novel and highly efficient feedback mechanism that will be useful in obtaining the channel state information at the transmitter in MU-MIMO systems.

V. CONCLUSION AND FUTURE WORKS

Two significant outcomes are expected from this research project. Firstly, the behaviour of MU-MIMO systems under time-varying channels will be analyzed using the reciprocity principle and CSI feedback from the receiver. Secondly, a novel and highly efficient CSI feedback mechanism will be proposed considering a wide range of channel conditions. The proposed novel feedback mechanism is expected to provide an optimum use of bandwidth and energy in MU-MIMO that are subject to time-varying channels.

ACKNOWLEDGEMENT

I would like to express my deep gratitude to Dr. Karla Ziri- Castro whose exceptional supervision and support enables this work. Her generous investment of time and energy as well as perpetual enthusiasm deserves my utmost acknowledgement.

REFERENCES

[1] C Ezio Biglieri, Robert Calderbank, Anthony Constantinides, Andrea Goldsmith, Arogyaswami Paulraj and H. Vincent Poor “MIMO Wireless Communications”, Cambridge University Press

[2] C. Oestges and B. Clerckx, “MIMO wireless communications from real-world propagation to space-time code design”, London Academic Press,2007

Transceiver

B Transceiver

A

Feedback

Channel