Energy Efficient Massive MIMO through Distributed Precoder Design

TL;DR

This paper introduces a novel distributed precoding scheme for massive MIMO systems that significantly enhances energy efficiency by jointly optimizing power control, interference, and antenna management with guaranteed convergence.

Contribution

It formulates a complex non-convex optimization problem for energy-efficient precoding and develops a convergent distributed algorithm combining augmented multiplier and quadratic programming techniques.

Findings

Achieves higher energy efficiency than existing methods.

Demonstrates faster convergence and robustness in simulations.

Enables distributed implementation reducing backhaul load.

Abstract

This paper presents an energy-efficient downlink precoding scheme with the objective of maximizing system energy efficiency in a multi-cell massive MIMO system. The proposed precoding design jointly considers the issues of power control, interference management, antenna switching and user throughput in a cluster of base stations (BS). We demonstrate that the precoding design can be formulated into a general sparsity-inducing non-convex problem, which is NP-hard. We thus apply a smooth approximation of zero-norm in the antenna power management to enable the application of the gradient-based algorithms. The non-convexity of the problem may also cause slow convergence or even divergence if some classical gradient algorithms are directly applied. We thus develop an efficient alternative algorithm combining features from augmented multiplier (AM) and quadratic programming (QP) to guarantee the convergence. We theoretically prove the convergence conditions for our algorithm both locally and globally under realistic assumptions. Our proposed algorithms further facilitate a distributed implementation that reduces backhaul overhead by offloading data-intensive steps to local computation at each BS. Numerical results confirm that our methods indeed achieve higher energy efficiency with superior convergence rate, compared to some well-known existing methods.