Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks

• Published in: IEEE transactions on wireless communications Vol. 21; no. 5; pp. 3478 - 3492
• Main Authors: Gao, Xinyu, Liu, Yuanwei, Liu, Xiao, Song, Lingyang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Array signal processing; Beamforming; Clustering; Clustering algorithms; Decoding; Deep Q-network (DQN); Gaussian mixture model (GMM); intelligent reflecting surface (IRS); Machine learning; NOMA; non-orthogonal multiple access (NOMA); Nonorthogonal multiple access; Prediction algorithms; Probabilistic models; Radio equipment; Resource allocation; Resource management; User requirements; Wireless networks

A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum-rate of all...