Multi-Cell Massive MIMO RSMA: SE Analysis and Deep Global Energy Efficiency Optimization
We consider the downlink of a Rician-faded massive multiple-input-multiple-output multi-cell system, where each base station (BS) serves its user equipments (UEs) with assistance from cell-specific intelligent-reflecting surface (IRS). The BSs employ rate splitting multiple access (RSMA) protocol, and maximal-ratio transmission. We derive a closed-form spectral efficiency (SE) expression for this system, and use it to optimize its global energy efficiency (GEE) metric by jointly optimizing the BS transmit power and IRS phases. We develop novel low-complexity closed-form solution to optimize power by using Lagrangian dual, Quadratic and Dinkelbach transforms. We then optimize IRS phases by developing a novel generative diffusion model (GDM)-based deep reinforcement learning (DRL) framework. We numerically characterize, for the first time, the RSMA SE gains over spatial division multiple access in a multi-cell network. We also compare the impact of inter-cell and residual successive interference cancellation interference on the SE of a multi-cell system. We also show that our GDM-DRL framework provides much higher GEE than multiple state-of-the-art solutions.