Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach
Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach
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DOI：
10.1109/twc.2021.3065523
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发表时间：
2021-08-01
影响因子：
10.4
通讯作者：
Nallanathan, Arumugam
中科院分区：
文献类型：
--
作者：
Ahsan, Waleed;Yi, Wenqiang;Nallanathan, Arumugam
非正交多址接入（NOMA）利用电力域的潜力来增强物联网（IoT）的连接性。由于通信信道随时间变化，动态用户聚类是提高NOMA-IoT网络吞吐量的一种很有前途的方法。本文针对上行NOMA-IoT通信开发了一种智能资源分配方案。为了最大限度地提高和率的平均性能，这项工作设计了一种有效的优化方法，基于两种强化学习算法，即深度强化学习（DRL）和SARSA学习。对于轻交通，SARSA学习是用来探索最安全的资源分配策略，以低成本。对于繁忙的交通，DRL用于处理交通引入的巨大变量。借助于所考虑的方法，这项工作解决了NOMA技术中公平资源分配的两个主要问题：1）动态分配用户和2）平衡资源块和网络流量。我们分析表明，收敛速度是成反比的网络规模。数值结果表明：1）与最优基准方案相比，所提出的DRL和SARSA学习算法具有较低的复杂度和可接受的精度; 2）NOMA使能的IoT网络在系统吞吐量方面优于传统的基于正交多址的IoT网络。
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.