Abstract
The problem of robust weighted sum-rate maximization (WSRMax) in multicell downlink multi-input single-output systems is considered. We assume that the channel state information (CSI) of all users is imperfectly known at the base stations. The problem is known to be NP-hard even in the case of perfect CSI. Assuming a bounded ellipsoidal model for the CSI errors, we maximize the worst-case weighted sum-rate and proposed a fast but possibly suboptimal algorithm. The proposed algorithm is based on alternating optimization technique and sequential convex programming. Numerical results show that the convergence speed of the proposed algorithm is fast, and it finds a close-to-optimal solution in only a few iterations.
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