2017/05/15 by Jarkko Kaleva, Antti Tölli, Kaleva, Jarkko +7
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Information Theory (cs.IT) #Power Line Communications and Noise
paper · pdf · doi:10.48550/arxiv.1705.05252
openalex publication_date 2017/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Downlink beamforming techniques with low signaling overhead are proposed for joint processing coordinated (JP) multi-point transmission. The objective is to maximize the weighted sum rate within joint transmission clusters. As the considered weighted sum rate maximization is a non-convex problem, successive convex approximation techniques, based on weighted mean-squared error minimization, are applied to devise algorithms with tractable computational complexity. Decentralized algorithms are proposed to enable JP even with limited backhaul connectivity. These algorithms rely provide a variety of alternatives for signaling overhead, computational complexity and convergence behavior. Time division duplexing is exploited to design transceiver training techniques for two scenarios: stream specific estimation and direct estimation. In the stream specific estimation, the base station and user equipment estimate all of the stream specific precoded pilots individually and construct the transmit/receive covariance matrices based on these pilot estimates. With the direct estimation, only the intended transmission is separately estimated and the covariance matrices constructed directly from the aggregate system-wide pilots. The impact of feedback/backhaul signaling quantization is considered, in order to further reduce the signaling overhead. Also, user admission is being considered for time-correlated channels. The enhanced transceiver convergence rate enables periodic beamformer reinitialization, which greatly improves the achieved system performance in dense networks.