Expectation Propagation Detector for Extra-Large Scale Massive MIMO
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The deployment of extremely large-scale antenna array at the base station, referred to as extra-largescale massive multiple-input-multiple-output (MIMO) system, is a brand new communication paradigm allowing for significant performance gain at the cost of excessively large amount of fronthaul data and ultra-high computational complexity. The spatial non-stationarity occurs for such as a system, and thus different array portions observe different channel properties. To address these issues, we consider the subarray-based processing architecture and develop an iterative detector by leveraging the expectation propagation (EP) principle based on the factor graph describing this architecture. In the proposed detector, the non-stationary property is exploited to reduce complexity. In addition, to avoid excessively high-complexity, we propose a hierarchical implementation architecture to enable parallel computation. Moreover, we modify the EP-based detector to require only one feedforward from each subarray to the central processing unit to minimize the transfer latency. The convergence of the proposed detector is verified from the evolution analysis. Moreover, we further demonstrate that different sizes and numbers of subarrays yield similar performance, which makes the EP-based detector very suitable for the extra-large-scale massive MIMO system. Simulation results demonstrate that the proposed detector outperforms its counterparts and verify the validity of our analyses.
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