Network MIMO large-system analysis and the impact of CSIT Estimation

We consider the downlink of a cellular network with multiple cells, multi-antenna base stations and single-antenna user terminals, including arbitrary inter-cell cooperation clusters, realistic distance-dependent pathloss and general “fairness” requirements. We focus on linear zero-forcing beamforming. Beyond Monte Carlo simulation, no efficient computation method to evaluate the ergodic throughput of such systems has been provided so far. We propose a method based on the combination of some large random matrix results with convex optimization. The proposed method is computationally much more efficient than Monte Carlo simulation and provides further insight in the joint operations of multiuser downlink beamforming, inter-cell cooperation and interference and opportunistic “fair” scheduling. Further, our analysis can be easily extended to consider achievable rates in the presence of non-ideal channel state information. In particular, we can study the system capacity of schemes based on explicit downlink training and channel state feedback.

[1]  Aaron D. Wyner,et al.  Shannon-theoretic approach to a Gaussian cellular multiple-access channel , 1994, IEEE Trans. Inf. Theory.

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

[3]  Giuseppe Caire,et al.  MIMO broadcast channel optimization under general linear constraints , 2009, 2009 IEEE International Symposium on Information Theory.

[4]  David Tse,et al.  Opportunistic beamforming using dumb antennas , 2002, IEEE Trans. Inf. Theory.

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

[6]  Giuseppe Caire,et al.  On the selection of semi-orthogonal users for zero-forcing beamforming , 2009, 2009 IEEE International Symposium on Information Theory.

[7]  Leandros Tassiulas,et al.  Resource Allocation and Cross-Layer Control in Wireless Networks , 2006, Found. Trends Netw..

[8]  Antonia Maria Tulino,et al.  Random Matrix Theory and Wireless Communications , 2004, Found. Trends Commun. Inf. Theory.

[9]  Jean C. Walrand,et al.  Fair end-to-end window-based congestion control , 2000, TNET.

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

[11]  Jean Walrand,et al.  Fair end-to-end window-based congestion control , 1998, TNET.

[12]  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.

[13]  Shlomo Shamai,et al.  Fading channels: How perfect need "Perfect side information" be? , 2002, IEEE Trans. Inf. Theory.