Cell-free Massive MIMO with Sequential Fronthaul Architecture and Limited Memory Access Points

TL;DR

This paper explores how limited memory at access points in cell-free massive MIMO with sequential fronthaul architecture affects spectral efficiency, proposing compression techniques and analyzing optimal AP configurations under memory constraints.

Contribution

It introduces vector-wise and element-wise compression methods for limited memory APs and analyzes their impact on the optimal number of APs and fronthaul rate.

Findings

Limited memory constrains the depth of sequential processing.

Single-antenna APs are optimal without memory constraints.

Memory capacity influences the rate requirements of fronthaul links.

Abstract

Cell-free massive multiple-input multiple-output (CFmMIMO) is a paradigm that can improve users' spectral efficiency (SE) far beyond traditional cellular networks. Increased spatial diversity in CFmMIMO is achieved by spreading the antennas into small access points (APs), which cooperate to serve the users. Sequential fronthaul topologies in CFmMIMO, such as the daisy chain and multi-branch tree topology, have gained considerable attention recently. In such a processing architecture, each AP must store its received signal vector in the memory until it receives the relevant information from the previous AP in the sequence to refine the estimate of the users' signal vector in the uplink. In this paper, we adopt vector-wise and element-wise compression on the raw or pre-processed received signal vectors to store them in the memory. We investigate the impact of the limited memory capacity in the APs on the optimal number of APs. We show that with no memory constraint, having single-antenna APs is optimal, especially as the number of users grows. However, a limited memory at the APs restricts the depth of the sequential processing pipeline. Furthermore, we investigate the relation between the memory capacity at the APs and the rate of the fronthaul link.