Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management

TL;DR

This paper proposes a novel resource management approach for cell-free massive MIMO systems that reduces energy consumption by optimizing AP-UE associations, federation formations, and AP activations, especially in high-load scenarios.

Contribution

It introduces a new alternating optimization algorithm for joint AP-UE association, federation formation, and AP activation to minimize energy use in CF-mMIMO networks.

Findings

Federations improve energy efficiency under high data rate demands.

Distributed CF-mMIMO architectures are necessary for meeting high data rates.

Less distributed systems with more antennas save energy when feasible.

Abstract

The physical layer foundations of cell-free massive MIMO (CF-mMIMO) have been well-established. As a next step, researchers are investigating practical and energy-efficient network implementations. This paper focuses on multiple sets of access points (APs) where user equipments (UEs) are served in each set, termed a federation, without inter-federation interference. The combination of federations and CF-mMIMO shows promise for highly-loaded scenarios. Our aim is to minimize the total energy consumption while adhering to UE downlink data rate constraints. The energy expenditure of the full system is modelled using a detailed hardware model of the APs. We jointly design the AP-UE association variables, determine active APs, and assign APs and UEs to federations. To solve this highly combinatorial problem, we develop a novel alternating optimization algorithm. Simulation results for an indoor factory demonstrate the advantages of considering multiple federations, particularly when facing large data rate requirements. Furthermore, we show that adopting a more distributed CF-mMIMO architecture is necessary to meet the data rate requirements. Conversely, if feasible, using a less distributed system with more antennas at each AP is more advantageous from an energy savings perspective.