Sparse Aggregation-Based Channel Estimation for Massive MIMO Systems With Decentralized Baseband Processing

To cope with the bottlenecks of the high computational complexity and excessive inter-connection communication in the conventional centralized baseband processing architecture, the decentralized baseband processing (DBP) architecture has been proposed, where the antennas are partitioned into multiple clusters, each connected to a local baseband unit (BBU). In this paper, we are interested in the distributed channel estimation (DCE) method under such DBP architecture, which is rarely studied in the literature. Our goal is to devise a DCE algorithm that can perform as well as the centralized scheme but with a small inter-connection communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose an aggregate-then-estimate based DCE algorithm. In contrast to the existing DCE algorithm which requires iterative information exchanges among BBUs, our algorithm only requires one round-trip communication between the nodes. Experiment results are presented to demonstrate the efficacy of the proposed DCE algorithm.