Energy Efficiency Maximization of Massive MIMO Communications With Dynamic Metasurface Antennas

TL;DR

This paper proposes an optimization framework for energy efficiency in DMA-assisted massive MIMO systems, using advanced algorithms to enhance uplink performance with reduced hardware and energy costs.

Contribution

It introduces a novel algorithmic framework for optimizing energy efficiency in DMA-assisted massive MIMO, including a closed-form solution for statistical CSI scenarios.

Findings

Significant EE gains over baseline schemes

Effective convergence of proposed algorithms

Validated performance improvements through numerical results

Abstract

Future wireless communications are largely inclined to deploy massive numbers of antennas at the base stations (BSs) by leveraging cost- and energy-efficient as well as environmentally friendly antenna arrays. The emerging technology of dynamic metasurface antennas (DMAs) is promising to realize such massive antenna arrays with reduced physical size, hardware cost, and power consumption. The goal of this paper is the optimization of the energy efficiency (EE) performance of DMA-assisted massive multiple-input multiple-output (MIMO) wireless communications. Focusing on the uplink, we propose an algorithmic framework for designing the transmit precoding of each multi-antenna user and the DMA tuning strategy at the BS to maximize the EE performance, considering the availability of either instantaneous or statistical channel state information (CSI). Specifically, the proposed framework is shaped around Dinkelbach's transform, alternating optimization, and deterministic equivalent methods. In addition, we obtain a closed-form solution to the optimal transmit signal directions for the statistical CSI case, which simplifies the corresponding transmission design for the multiple-antenna case. Our numerical results verify the good convergence behavior of the proposed algorithms, and showcase the considerable EE performance gains of the DMA-assisted massive MIMO transmissions over the baseline schemes.