User Selection and Pairing for Future Power Domain Non-Orthogonal Multiple Access (PD-NOMA) using Deep Learning Techniques
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Abstract
The next-generation wireless networks and communications such as 5G/6G offers various benefits such as low latency, high data rates, and improvement in user numbers with increased base station capacity and quality of service. These advantages are obtained from the increasing receiver complexity through the non-orthogonal multiple access (NOMA) of users. It is the promising radio access approach used to enhance next-generation wireless communications. Among the techniques of NOMA such as power and code domain, this paper concentrates on power domain NOMA. The user in the network for transmission is selected using a deep learning approach called deep neural network (DNN). This user selection results are the training data and the loss function is modified for the selection of users that could meet the constraint the selected user cannot be in both strong and weak groups. The user aggregation/user pairing among the sub-channels is performed through the exhaustive analysis using particle swarm optimization (PSO). The usage of DNN-PSO enables the transmitter and required minimum uplink and downlink transmitting power and guaranteed for Quality of Service of each user. The simulation and comprehensive statistical evaluation are performed with the comparative analysis of energy efficiency and maximum achievable rate with the given spectrum efficiency (SE) of PD-NOMA. The proposed model ensures reduced latency, increased throughput, less energy, achievable data rate, user fairness and increased reliability and quality of service.
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