Robust Beamforming Design for Fairness-Aware Energy Efficiency Maximization in RIS-Assisted mmWave Communications

TL;DR

This paper proposes a robust beamforming approach for RIS-assisted mmWave systems that maximizes energy efficiency while ensuring user fairness, even with imperfect CSI, using a novel optimization framework.

Contribution

It introduces a new optimization method combining penalty dual decomposition and gradient ascent to balance energy efficiency and fairness under CSI uncertainties.

Findings

Achieves optimal energy efficiency for given fairness levels.

Provides a flexible trade-off between energy efficiency and user fairness.

Demonstrates robustness against CSI errors in system performance.

Abstract

Users in millimeter-wave (mmWave) systems often exhibit diverse channel strengths, which can negatively impact user fairness in resource allocation. Moreover, exact channel state information (CSI) may not be available at the transmitter, rendering suboptimal resource allocation. In this paper, we address these issues within the context of energy efficiency maximization in RIS-assisted mmWave systems. We first derive a tractable lower bound on the achievable sum rate, taking into account CSI errors. Subsequently, we formulate the optimization problem, targeting maximizing the system energy efficiency while maintaining a minimum Jain's fairness index controlled by a tunable design parameter. The optimization problem is very challenging due to the coupling of the optimization variables in the objective function and the fairness constraint, as well as the existence of non-convex equality and fractional constraints. To solve the optimization problem, we employ the penalty dual decomposition method, together with a projected gradient ascent based alternating optimization procedure. Simulation results demonstrate that the proposed algorithm can achieve an optimal energy efficiency for a prescribed Jain's fairness index. In addition, adjusting the fairness design parameter can yield a favorable trade-off between energy efficiency and user fairness compared to methods that exclusively focus on optimizing one of these metrics.