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Optimal Power Allocation in NOMA System Based on Artificial Intelligence Methods

Igor Jovanovic, Kritsada Mamat


This paper considers the Non-Orthogonal Multiple Access (NOMA) technique which is one of the core technologies in 5G and beyond. To distinguish users in the power domain, Superposition coding and Successive Interference Cancellation (SIC) are applied at the transmitter and receiver. Power allocation is shown to be significant in affecting the system performance. This work proposes an application of two Artificial Intelligence (AI) methods, Q-learning and Genetic Algorithm (GA), in order to optimize the power allocation in the NOMA system. Namely, the maximization of the minimum bitrate of the overall system as well as the transformation of the NOMA system into both Q-learning and GA components are obtained by setting and solving the power allocation optimization problem. Numerical results demonstrate that Artificial intelligence algorithms provide a higher minimum bitrate in comparison with the existing theoretical power allocation methods. Besides bitrate, the complexity of both methods is analyzed. It is concluded that Q-learning has an exponential, while GA has a linear complexity with the increase of the total number of users.


NOMA; power allocation; Artificial Intelligence; Q-learning, Genetic Algorithm

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DOI: 10.14416/


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