Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading

TL;DR

This paper investigates device activity detection in multi-cell massive MIMO systems, demonstrating how knowledge of large-scale fading improves detection performance and reduces sequence length requirements through cooperative strategies.

Contribution

It introduces a phase transition analysis showing the benefits of large-scale fading knowledge and proposes a novel quantization-based cooperation scheme for capacity-constrained fronthaul links.

Findings

Knowledge of large-scale fading reduces sequence length needed for detection.

Cooperative detection can make sequence length independent of the number of cells.

Quantization-based cooperation outperforms direct covariance matrix quantization.

Abstract

This paper studies the device activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, in which the active devices transmit signature sequences to multiple base stations (BSs) that are connected to a central unit (CU), and the BSs cooperate across multiple cells to detect the active devices based on the sample covariance matrices at the BSs. This paper demonstrates the importance of exploiting the knowledge of channel large-scale fadings in this cooperative detection setting through a phase transition analysis, which characterizes the length of signature sequences needed for successful device activity detection in the massive MIMO regime. It is shown that when the large-scale fadings are known, the phase transition for the multi-cell scenario is approximately the same as that of a single-cell system. In this case, the length of the signature sequences required for reliable activity detection in the multi-cell system can be made to be independent of the number of cells through cooperation, in contrast to the case where the large-scale fadings are not known. Further, this paper considers the case in which the fronthaul links between the BSs and the CU have capacity constraints and proposes a novel cooperation scheme based on the quantization of preliminary detection results at the BSs and the reconstruction of the sample covariance matrices at the CU. Simulations show that the proposed method significantly outperforms the scheme of directly quantizing the sample covariance matrices.