Ahsan, Waleed and Yi, Wenqiang and Qin, Zhijin and Liu, Yuanwei and Nallanathan, Arumugam (2021) Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach. IEEE Transactions on Wireless Communications, 20 (8). pp. 5083-5098. DOI https://doi.org/10.1109/twc.2021.3065523
Ahsan, Waleed and Yi, Wenqiang and Qin, Zhijin and Liu, Yuanwei and Nallanathan, Arumugam (2021) Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach. IEEE Transactions on Wireless Communications, 20 (8). pp. 5083-5098. DOI https://doi.org/10.1109/twc.2021.3065523
Ahsan, Waleed and Yi, Wenqiang and Qin, Zhijin and Liu, Yuanwei and Nallanathan, Arumugam (2021) Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach. IEEE Transactions on Wireless Communications, 20 (8). pp. 5083-5098. DOI https://doi.org/10.1109/twc.2021.3065523
Abstract
Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This article develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput.
Item Type: | Article |
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Uncontrolled Keywords: | Deep reinforcement learning; Internet of Things; non-orthogonal multiple access; power allocation; SARSA learning; user clustering |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 04 Oct 2024 11:47 |
Last Modified: | 30 Oct 2024 21:32 |
URI: | http://repository.essex.ac.uk/id/eprint/39336 |
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