Distributed CSI Acquisition and Coordinated Precoding for TDD Multicell MIMO Systems

Several distributed coordinated precoding methods exist in the downlink multicell multiple-input-multiple-output (MIMO) literature, many of which assume perfect knowledge of received signal covariance and local effective channels. In this paper, we let the notion of channel state information (CSI) encompass this knowledge of covariances and effective channels. We analyze what local CSI is required in the weighted minimization of the mean square error (WMMSE) algorithm for distributed coordinated precoding, and we study how this required CSI can be obtained in a distributed fashion. Based on pilot-assisted channel estimation, we propose three CSI acquisition methods with different tradeoffs between feedback and signaling, backhaul use, and computational complexity. One of the proposed methods is fully distributed, meaning that it only depends on over-the-air signaling but requires no backhaul, and it results in a fully distributed joint system when coupled with the WMMSE algorithm. Naïvely applying the WMMSE algorithm together with the fully distributed CSI acquisition results in catastrophic performance however; therefore, we propose a robustified WMMSE algorithm (RB-WMMSE) based on the well-known diagonal loading framework. By enforcing properties of the WMMSE solutions with perfect CSI onto the problem with imperfect CSI, the resulting diagonally loaded spatial filters are shown to perform significantly better than the naïve filters. The proposed robust and distributed system is evaluated using numerical simulations and is shown to perform well compared with benchmarks. Under centralized CSI acquisition, the proposed algorithm performs on par with other existing centralized robust WMMSE algorithms. When evaluated in a large-scale fading environment, the performance of the proposed system is promising