Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Information Theory | Base station,Mathematical optimization,Backhaul (telecommunications),Communication channel,User equipment,Rate of convergence,Quantization (signal processing),Mathematics,Computational complexity theory,Covariance |
DocType | Volume | Citations |
Journal | abs/1705.05252 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jarkko Kaleva | 1 | 0 | 0.34 |
| Antti Tölli | 2 | 432 | 59.89 |
| Markku J. Juntti | 3 | 1065 | 127.57 |
| Randall Berry | 4 | 0 | 0.68 |
| Michael L. Honig | 5 | 2971 | 411.29 |