On the selection of semi-orthogonal users for zero-forcing beamforming

We reconsider the role of user selection in multiuser MIMO broadcast channels (downlink), in the relevant regime where the number of users K is linear in the number of transmitter (base station) antennas M. User selection is known to achieve mutually quasi-orthogonal user channels and, at the same time, a multiuser diversity effect in terms of receiver SNR. These goals are achieved in the regime of fixed number of transmit antennas, and very large number of users. In contrast, we show that when K = O(M) these effects cannot be achieved, and the role of user selection is marginal. In terms of system design, our results suggest that only a small number K ≈ M of users should feedback their channel state information at each point in time. This greatly alleviates the burden of the channel state information feedback, while achieving essentially optimal performance.

[1]  Babak Hassibi,et al.  On the capacity of MIMO broadcast channels with partial side information , 2005, IEEE Transactions on Information Theory.

[2]  Nihar Jindal,et al.  MIMO broadcast channels with finite rate feedback , 2006, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[3]  Wei Yu,et al.  Sum-capacity computation for the Gaussian vector broadcast channel via dual decomposition , 2006, IEEE Transactions on Information Theory.

[4]  Shlomo Shamai,et al.  On the achievable throughput of a multiantenna Gaussian broadcast channel , 2003, IEEE Transactions on Information Theory.

[5]  Andrea J. Goldsmith,et al.  On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming , 2006, IEEE Journal on Selected Areas in Communications.

[6]  Ali Esmaili,et al.  Probability and Random Processes , 2005, Technometrics.

[7]  A. Edelman,et al.  Random matrix theory , 2005, Acta Numerica.

[8]  N.D. Sidiropoulos,et al.  On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm , 2005, IEEE Transactions on Signal Processing.

[9]  Sergio VerdÂ,et al.  Fading Channels: InformationTheoretic and Communications Aspects , 2000 .

[10]  Babak Hassibi,et al.  A Comparison of Time-Sharing, DPC, and Beamforming for MIMO Broadcast Channels With Many Users , 2007, IEEE Transactions on Communications.

[11]  Giuseppe Caire,et al.  Quantized vs. Analog Feedback for the MIMO Broadcast Channel: A Comparison between Zero-Forcing Based Achievable Rates , 2007, 2007 IEEE International Symposium on Information Theory.

[12]  Shlomo Shamai,et al.  Fading Channels: Information-Theoretic and Communication Aspects , 1998, IEEE Trans. Inf. Theory.

[13]  Ami Wiesel,et al.  Zero-Forcing Precoding and Generalized Inverses , 2008, IEEE Transactions on Signal Processing.

[14]  Michael D. Zoltowski,et al.  Multiple Antenna Broadcast Channels With Shape Feedback and Limited Feedback , 2007, IEEE Transactions on Signal Processing.

[15]  G. Grimmett,et al.  Probability and random processes , 2002 .

[16]  Shlomo Shamai,et al.  The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel , 2006, IEEE Transactions on Information Theory.

[17]  Alireza Bayesteh,et al.  Is it possible to achieve the optimum throughput and fairness simultaneously in a MIMO Broadcast Channel? , 2008, 2008 IEEE International Symposium on Information Theory.

[18]  Giuseppe Caire,et al.  How much training and feedback are needed in MIMO broadcast channels? , 2008, 2008 IEEE International Symposium on Information Theory.