Multicell MISO Downlink Weighted Sum-Rate Maximization: A Distributed Approach

• Published in: IEEE transactions on signal processing Vol. 61; no. 3; pp. 556 - 570
• Main Authors: Weeraddana, P. C., Codreanu, M., Latva-aho, M., Ephremides, A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.2013; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Approximation methods; Detection, estimation, filtering, equalization, prediction; Distributed optimization; Exact sciences and technology; geometric programming; Information, signal and communications theory; Integrated circuits; Interference; Optimization; primal decomposition; Programming; second-order cone programming; Signal and communications theory; Signal processing algorithms; Signal, noise; subgradient method; successive convex approximations; Telecommunications and information theory; Transmitting antennas; wireless networks; Performance evaluation; Second order; Mobile phone; Base station; Decomposition method; Feedback regulation; Downlink; Iterative method; subgradient method; Implementation; Optimization; Mean square error; Distributed processing; Successive approximation; Distributed optimization; primal decomposition; Covariance; Antenna array; Optical fiber; second-order cone programming; Optical links; Radio link; geometric programming; wireless networks; Signalling; Algorithm; Multiple system; successive convex approximations; Signal processing; Numerical simulation
• ISSN: 1053-587X; 1941-0476; 1941-0476
• DOI: 10.1109/TSP.2012.2225060

We develop an easy to implement distributed method for weighted sum-rate maximization (WSRMax) problem in a multicell multiple antenna downlink system. Unlike the recently proposed minimum weighted mean-squared error based algorithms, where at each iteration all mobile terminals needs to estimate the covariance matrices of their received signals, compute and feedback over the air certain parameters to the base stations (BS), our algorithm operates without any user terminal assistance. It requires only BS to BS signalling via reliable backhaul links (e.g., fiber, microwave links) and all required computation is performed at the BSs. The algorithm is based on primal decomposition and subgradient methods, where the original nonconvex problem is split into a master problem and a number of subproblems (one for each BS). A novel sequential convex approximation strategy is proposed to address the nonconvex master problem. In the case of subproblems, we adopt an existing iterative approach based on second-order cone programming and geometric programming. The subproblems are coordinated to find a (possibly suboptimal) solution to the master problem. Subproblems can be solved by BSs in a fully asynchronous manner, though the coordination between subproblems should be synchronous. Numerical results are provided to see the behavior of the algorithm under different degrees of BS coordination. They show that the proposed algorithm yields a good tradeoff between the implementation-level simplicity and the performance.