# On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO

## TL;DR

This paper analyzes the energy efficiency of cell-free Massive MIMO systems with limited backhaul capacity, proposing an optimization scheme that accounts for quantization, channel estimation errors, and power constraints to improve performance.

## Contribution

It introduces a novel energy efficiency maximization framework for cell-free Massive MIMO with limited backhaul, using Bussgang theorem and successive convex approximation techniques.

## Key findings

- Proposed optimization scheme outperforms existing methods.
- Quantization effects significantly impact energy efficiency.
- Distributed MRC with quantized signals is effective under constraints.

## Abstract

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.