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The high demand for data rate in the next generation of wirelesscommunication could be ensured by Non-Orthogonal Multiple Access (NOMA)approach in the millimetre-wave (mmW) frequency band. Joint power allocationand beamforming of mmW-NOMA systems is mandatory which could be met byoptimization approaches. To this end, we have exploited Deep ReinforcementLearning (DRL) approach due to policy generation leading to an optimizedsum-rate of users. Actor-critic phenomena are utilized to measure the immediatereward and provide the new action to maximize the overall Q-value of thenetwork. The immediate reward has been defined based on the summation of therate of two users regarding the minimum guaranteed rate for each user and thesum of consumed power as the constraints. The simulation results represent thesuperiority of the proposed approach rather than the Time-Division MultipleAccess (TDMA) and another NOMA optimized strategy in terms of sum-rate ofusers.

Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning