Robust multi-user opportunistic beamforming for sparse networks

A scheme exploiting reduced feedback for the purpose of opportunistic multi-user beamforming is proposed. The scheme builds on recent promising advances realized in the area of multi-user downlink precoding and scheduling based on partial transmitter channel state information (CSIT) using random beamforming-based SDMA. Although random precoding followed by SDMA scheduling is optimal within the set of unitary precoders, it is only so in the asymptotic number of users K. For-practically relevant-sparse networks (i.e., with low to moderate number of users) random beam-forming SDMA yields severely degraded performance. In this work we present a scheme allowing to restore robustness with respect to cell sparsity. The core idea here is to preserve the low complexity low feedback advantage of random opportunistic beamforming in selecting a target group of users, while much more efficient beamforming schemes can be used to serve the group of users once it has been identified. We propose different designs, optimal and suboptimal, based upon variable levels of feedback requirement. We show substantial gain over opportunistic beamforming for a range of K.

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

[2]  R. Laroia,et al.  Opportunistic beamforming using dumb antennas , 2002, Proceedings IEEE International Symposium on Information Theory,.

[3]  A. Goldsmith,et al.  Sum power iterative water-filling for multi-antenna Gaussian broadcast channels , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

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

[5]  Babak Hassibi,et al.  Rate maximization in multi-antenna broadcast channels with linear preprocessing , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[6]  Babak Hassibi,et al.  Scaling laws of sum rate using time-sharing, DPC, and beamforming for MIMO broadcast channels , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[7]  Andrea J. Goldsmith,et al.  Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels , 2003, IEEE Trans. Inf. Theory.

[8]  Rick S. Blum,et al.  Multiuser diversity for a dirty paper approach , 2003, IEEE Communications Letters.

[9]  G. Caire,et al.  Antenna Diversity vs. Multiuser Diversity: Quantifying the Tradeoffs , 2004 .

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

[11]  Andrea J. Goldsmith,et al.  Sum power iterative water-filling for multi-antenna Gaussian broadcast channels , 2005, IEEE Transactions on Information Theory.

[12]  Giuseppe Caire,et al.  Transmit Diversity Versus Opportunistic Beamforming in Data Packet Mobile Downlink Transmission , 2007, IEEE Transactions on Communications.

[13]  Raymond Knopp,et al.  Information capacity and power control in single-cell multiuser communications , 1995, Proceedings IEEE International Conference on Communications ICC '95.

[14]  Babak Hassibi,et al.  On the capacity of MIMO broadcast channel with partial side information , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[15]  David Gesbert,et al.  Memory-based opportunistic multi-user beamforming , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[16]  H. Boche,et al.  A general duality theory for uplink and downlink beamforming , 2002, Proceedings IEEE 56th Vehicular Technology Conference.