Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation, Phase Shifts, and Hybrid Beamforming Optimization

Yue Xiu (1) , Jun Zhao (2) , Wei Sun (3) , Marco Di Renzo (4, 5, 6) , Guan Gui (7) , Zhongpei Zhang (1) , Ning Wei (1)

1 UESTC - University of Electronic Science and Technology of China [Chengdu]
2 Nanyang Technological University [Singapour]
3 Northeastern University [Shenyang]
4 L2S - Laboratoire des signaux et systèmes
5 Université Paris-Saclay
6 CNRS - Centre National de la Recherche Scientifique
7 NJUPT - Nanjing University of Posts and Telecommunications [Nanjing]

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.

Domaines: Traitement du signal et de l'image [eess.SP]

Yue Xiu, Jun Zhao, Wei Sun, Marco Di Renzo, Guan Gui, et al.. Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation, Phase Shifts, and Hybrid Beamforming Optimization. IEEE Transactions on Wireless Communications, 2022, 20 (12), pp.8393-8409. ⟨10.1109/TWC.2021.3092597⟩. ⟨hal-03838733⟩