Energy Efficiency in mmWave mMIMO-RSMA Systems With Multiple AAV-Carried STAR-RISs

This paper investigates the energy efficiency maximization (EEM) in mmWave massive multiple-input multiple-output (mMIMO) rate-splitting multiple access (RSMA) systems, enhanced by multiple simultaneous transmission and reflection reconfigurable intelligent surfaces (STAR-RISs) mounted on autonomous aerial vehicles (AAVs), critical for sustainable 6G networks under imperfect channel state information (CSI). An EEM problem is formulated by jointly optimizing the precoding matrix and STAR-RIS phase shifts, subject to STAR-RIS phase shift, base station power budget, and devices' data rate constraints. This results in a complex mixed- integer programming problem, which is challenging to solve. To tackle this, an alternative optimization approach is proposed that decomposes the EEM problem into phase shift and beamforming subproblems, solved iteratively using a bisection search and the inner approximation method with novel tractable transformations. Numerical results show that the proposed RSMA-based algorithm achieves 15% higher EE than non-orthogonal multiple access and 35% higher than a conventional beamforming system without RSMA for efficient 6G deployments.