Energy Efficiency Optimization for Multi-cell Massive MIMO: Centralized and Distributed Power Allocation Algorithms

TL;DR

This paper presents novel centralized and distributed algorithms for optimizing energy efficiency in multi-cell massive MIMO systems, leveraging statistical CSI and advanced optimization techniques to improve performance with reduced complexity and signaling overhead.

Contribution

It introduces new optimization schemes for max-min energy efficiency in multi-cell massive MIMO, including closed-form eigenmatrix solutions and low-complexity power allocation algorithms.

Findings

Proposed algorithms outperform existing methods in energy efficiency.

Distributed algorithm reduces backhaul communication requirements.

Numerical results validate the effectiveness of the algorithms.

Abstract

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach's transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results.