EMF-Aware Energy Efficiency Optimization in Active RIS-Assisted 5G Networks

To address electromagnetic field (EMF) exposure concerns and enhance network performance, this work proposes an energy efficiency (EE) optimization algorithm for 5G networks operating in active reconfigurable intelligent surface (RIS)-assisted environments, considering both power and EMF exposure constraints. The algorithm tackles the challenge of maximizing a constrained EE utility function in multi-active RIS-aided multiple-input multiple-output (MIMO) systems by introducing a joint transmit and reflect beamforming approach. Specifically, EE is maximized by jointly optimizing the transmit beamforming weights and RIS reflection coefficients under stringent power and EMF exposure limitations. To solve the resulting non-convex optimization problem, a novel algorithm is developed to decouple the optimization variables, with its convergence behavior thoroughly validated. A quadratic transform technique is utilized, introducing a coverage identifier vector to recast the non-convex problem into a series of convex subproblems solvable using the CVX toolbox. Simulation results demonstrate that integrating RIS significantly boosts energy efficiency, with the performance gap between RIS-assisted and non-RIS scenarios widening over iterations, ultimately achieving a 31.7% improvement compared to systems without RIS. Furthermore, the proposed algorithm outperforms benchmark techniques, achieving 3.3% and 4.8% higher EE than the Truncated and Boosted beamforming (TBBF) and Equalized beamforming (EBF) techniques, respectively.