Hierarchical Semi-Online Optimization for Cooperative MIMO Networks With Information Parsing

We consider cooperative multiple-input multiple-output (MIMO) precoding design with multiple access points (APs) assisted by a central controller (CC) in a fading environment. Even though each AP may have its own local channel state information (CSI), due to the communication delay in the backhaul, neither the APs nor the CC has timely global CSI. Under this hierarchical semi-online setting, our goal is to minimize the accumulated precoding deviation, between the actual local precoders executed by the APs and an ideal cooperative precoder based on timely and perfect global CSI, subject to per-AP transmit power limits. We propose an efficient algorithm, termed Semi-Online Precoding with Information Parsing (SOPIP), which accounts for the network heterogeneity in information timeliness and computational capacity. SOPIP does not require the CC to send the full global CSI to each AP. Instead, it takes advantage of the precoder structure to substantially lower the communication overhead, while allowing each AP to effectively combine its own timely local CSI with the delayed global CSI to enable adaptive precoder updates. We analyze the performance of SOPIP in the presence of multi-slot communication delay, CSI inaccuracy, and gradient estimation error, showing that it has a bounded performance gap from an offline optimal solution. Simulation results under typical cellular system settings further demonstrate the substantial performance gain of SOPIP over other centralized and distributed schemes.