Detection of Abrupt Change in Channel Covariance Matrix for Multi-Antenna Communication

TL;DR

This paper develops a theoretical and practical approach for detecting abrupt changes in channel covariance matrices in multi-antenna systems, enabling quick and accurate detection crucial for adaptive communication.

Contribution

It introduces a genie-aided LLR-based detector with performance bounds and a practical ML-based detector for real-world scenarios, advancing change detection methods in wireless communications.

Findings

The genie-aided detector provides theoretical performance limits.

The ML-based detector effectively detects changes with limited samples.

Proposed methods achieve low error probabilities in practical settings.

Abstract

The knowledge of channel covariance matrices is of paramount importance to the estimation of instantaneous channels and the design of beamforming vectors in multi-antenna systems. In practice, an abrupt change in channel covariance matrices may occur due to the change in the environment and the user location. Although several works have proposed efficient algorithms to estimate the channel covariance matrices after any change occurs, how to detect such a change accurately and quickly is still an open problem in the literature. In this paper, we focus on channel covariance change detection between a multi-antenna base station (BS) and a single-antenna user equipment (UE). To provide theoretical performance limit, we first propose a genie-aided change detector based on the log-likelihood ratio (LLR) test assuming the channel covariance matrix after change is known, and characterize the corresponding missed detection and false alarm probabilities. Then, this paper considers the practical case where the channel covariance matrix after change is unknown. The maximum likelihood (ML) estimation technique is used to predict the covariance matrix based on the received pilot signals over a certain number of coherence blocks, building upon which the LLR-based change detector is employed. Numerical results show that our proposed scheme can detect the change with low error probability even when the number of channel samples is small such that the estimation of the covariance matrix is not that accurate. This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.