Decentralized Coherent Coordinated Multi-Point Transmission for Weighted Sum Rate Maximization

Decentralized downlink beamformer design for coherent coordinated multi-point transmission is proposed with weighted sum rate maximization system performance objective. The weighted sum rate is maximized by successive convex approximation of the corresponding weighted mean-squared error minimization problem. Decentralized beam coordination is achieved by employing a best response design, where each base station designs its own precoders in parallel assuming fixed transmission from the adjacent cells. After each beamformer update, the fixed terms are updated according to the solutions of the cooperating transmitters. The proposed design incorporates a bi-directional beamformer signaling scheme to improve the convergence properties. This scheme exploits the time division duplexing frame structure and is shown to improve the training latency of the iterative transceiver design. Furthermore, the improved transceiver convergence rate enables periodic beamformer reinitialization, which greatly improves the achieved system performance in dense networks.

[1]  Antti Tölli,et al.  Decentralized sum MSE minimization for coordinated multi-point transmission , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Antti Tölli,et al.  Effective CSI Signaling and Decentralized Beam Coordination in TDD Multi-Cell MIMO Systems , 2013, IEEE Transactions on Signal Processing.

[3]  John M. Cioffi,et al.  Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design , 2008, IEEE Trans. Wirel. Commun..

[4]  Antti Tölli,et al.  Decentralized Minimum Power Multi-Cell Beamforming with Limited Backhaul Signaling , 2011, IEEE Transactions on Wireless Communications.

[5]  Zhi-Quan Luo,et al.  An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Wei Yu,et al.  Multi-Cell MIMO Cooperative Networks: A New Look at Interference , 2010, IEEE Journal on Selected Areas in Communications.

[7]  Emil Björnson,et al.  Cooperative Multicell Precoding: Rate Region Characterization and Distributed Strategies With Instantaneous and Statistical CSI , 2010, IEEE Transactions on Signal Processing.

[8]  Francisco Facchinei,et al.  Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems , 2013, IEEE Transactions on Signal Processing.

[9]  Zhi-Quan Luo,et al.  Dynamic Spectrum Management: Complexity and Duality , 2008, IEEE Journal of Selected Topics in Signal Processing.

[10]  Jeffrey G. Andrews,et al.  Adaptive Spatial Intercell Interference Cancellation in Multicell Wireless Networks , 2009, IEEE Journal on Selected Areas in Communications.

[11]  Kwang Bok Lee,et al.  Channel Feedback Optimization for Network MIMO Systems , 2012, IEEE Transactions on Vehicular Technology.

[12]  Chenyang Yang,et al.  Coordinated Multi-Point Transmission Strategies for TDD Systems with Non-Ideal Channel Reciprocity , 2013, IEEE Transactions on Communications.

[13]  Michael L. Honig,et al.  Bi-Directional Training for Adaptive Beamforming and Power Control in Interference Networks , 2014, IEEE Transactions on Signal Processing.

[14]  Zhisheng Niu,et al.  Distributed Adaptation of Quantized Feedback for Downlink Network MIMO Systems , 2011, IEEE Transactions on Wireless Communications.