Performance Analysis of Cell-Free Massive MIMO Systems with Massive Connectivity

TL;DR

This paper analyzes the uplink performance of cell-free massive MIMO systems with massive connectivity, focusing on joint activity detection, channel estimation, and achievable rates using GAMP and ZF detection.

Contribution

It introduces a GAMP-based method for joint user activity detection and channel estimation in cell-free massive MIMO with massive connectivity, providing new insights into system performance.

Findings

Optimal number of pilots improves detection and estimation accuracy.

Increasing the number of APs enhances achievable uplink rates.

Higher SNR leads to better system performance.

Abstract

In this paper, we investigate the performance of cell-free massive MIMO systems with massive connectivity. With the generalized approximate message passing (GAMP) algorithm, we obtain the minimum mean-squared error (MMSE) estimate of the effective channel coefficients from all users to all access points (APs) in order to perform joint user activity detection and channel estimation. Subsequently, using the decoupling properties of MMSE estimation for large linear systems and state evolution equations of the GAMP algorithm, we obtain the variances of both the estimated channel coefficients and the corresponding channel estimation error. Finally, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO system. With numerical results, we analyze the impact of the number of pilots used for joint activity detection and channel estimation, the number of APs, and signal-to-noise ratio (SNR) on the achievable rates.