Intelligent Reflecting Surface Aided NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array
Penglu Liu, Yong Li, Wei Cheng, Xiang Gao, Xiaojing Huang
Abstract
In this paper, we propose a downlink intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) for millimeter-wave (mmWave) massive MIMO with lens antenna array, i.e., IRS-aided mmWave beamspace NOMA, where the single-antenna users without direct-link but connected to the base station (BS) with the aid of the IRS are grouped as one NOMA group. Considering the power leakage problem in beamspace channel and the per-antenna power constraint, we propose two multi-beam selection strategies for the BS-IRS link under two channel models, i.e., 2-dimension (2D) channel model and 3-dimension (3D) channel model, respectively, where two corresponding RF chain configuration strategies are designed, respectively. Then, we formulate and solve the optimization problem for maximizing the weighted sum rate by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, where we propose the alternating optimization (AO) method to solve the above joint optimization problem. Especially, different from the stochastic method, based on the beam-splitting technique, we propose the method to initialize the feasible solution for the proposed AO method, where the conditional transmit power minimization problem is formulated and solved. Through simulations, the weighted sum rate performance of the proposed IRS-aided mmWave beamspace NOMA is verified.