Low-Complexity Channel Estimation for Massive MIMO Systems with Decentralized Baseband Processing

TL;DR

This paper proposes low-complexity distributed channel estimation algorithms for decentralized massive MIMO systems that achieve near-centralized performance with minimal inter-BBU communication.

Contribution

It introduces two novel distributed CE algorithms based on diagonal MMSE estimation that require only one round of inter-BBU communication, reducing complexity and communication costs.

Findings

Algorithms achieve comparable accuracy to centralized schemes.

Significant reduction in inter-BBU communication.

Lower computational complexity compared to existing methods.

Abstract

The traditional centralized baseband processing architecture is faced with the bottlenecks of high computation complexity and excessive fronthaul communication, especially when the number of antennas at the base station (BS) is large. To cope with these two challenges, the decentralized baseband processing (DPB) architecture has been proposed, where the BS antennas are partitioned into multiple clusters, and each is connected to a local baseband unit (BBU). In this paper, we are interested in the low-complexity distributed channel estimation (CE) method under such DBP architecture, which is rarely studied in the literature. The aim is to devise distributed CE algorithms that can perform as well as the centralized scheme but with a small inter-BBU communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose two distributed CE algorithms, namely the aggregate-then-estimate algorithm and the estimate-then-aggregate algorithm. In contrast to the existing distributed CE algorithm which requires iterative information exchanges among the nodes, our algorithms only require one roundtrip communication among BBUs. Extensive experiment results are presented to demonstrate the advantages of the proposed distributed CE algorithms in terms of estimation accuracy, inter-BBU communication cost, and computation complexity.