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Resource Allocation in Uplink NOMA-IoT Networks A Reinforcement-Learning Approach

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Document pages: 32 pages

Abstract: Non-orthogonal multiple access (NOMA) exploits the potential of the powerdomain to enhance the connectivity for the Internet of Things (IoT). Due totime-varying communication channels, dynamic user clustering is a promisingmethod to increase the throughput of NOMA-IoT networks. This paper develops anintelligent resource allocation scheme for uplink NOMA-IoT communications. Tomaximise the average performance of sum rates, this work designs an efficientoptimization approach based on two reinforcement learning algorithms, namelydeep reinforcement learning (DRL) and SARSA-learning. For light traffic,SARSA-learning is used to explore the safest resource allocation policy withlow cost. For heavy traffic, DRL is used to handle traffic-introduced hugevariables. With the aid of the considered approach, this work addresses twomain problems of fair resource allocation in NOMA techniques: 1) allocatingusers dynamically and 2) balancing resource blocks and network traffic. Weanalytically demonstrate that the rate of convergence is inversely proportionalto network sizes. Numerical results show that: 1) Compared with the optimalbenchmark scheme, the proposed DRL and SARSA-learning algorithms have lowercomplexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperformthe conventional orthogonal multiple access based IoT networks in terms ofsystem throughput.

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