Reconfigurable intelligent surfaces aided mmWave NOMA: joint power allocation, phase shifts, and hybrid beamforming optimization

In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a maximum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization (AO) algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem by keeping fixed the phase shifts of the RIS and the hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed AO algorithm outperforms existing schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate.Ministry of Education (MOE)The work of Yue Xiu, Zhongpei Zhang, and Ning Wei was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1802000 and in part by the National Natural Science Foundation of China (NSFC) under Grant 91938202 and Grant 61871070. The work of Jun Zhao was supported by the Singapore Ministry of Education Academic Research Fund Tier 2 under Grant MOE2019-T2-1-176 and in part by the AI Singapore (AISG) 100 Experiments (100E) Program. The work of Marco Di Renzo was supported in part by the European Commission through the H2020 ARIADNE Project under Agreement 871464 and through the H2020 RISE-6G Project under Agreement 101017011