Zhang Qianfeng, An Kang, Yan Xiaojuan, Xi Hongxia, Wang Yuli
Guangxi Key Laboratory of Ocean Engineering Equipment and Technology, Qinzhou 535011, China.
Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology (Beibu Gulf University), Education Department of Guangxi Zhuang Autonomous Region, Qinzhou 535011, China.
Sensors (Basel). 2023 Aug 9;23(16):7062. doi: 10.3390/s23167062.
In this paper, we investigate a user pairing problem in power domain non-orthogonal multiple access (NOMA) scheme-aided satellite networks. In the considered scenario, different satellite applications are assumed with various delay quality-of-service (QoS) requirements, and the concept of effective capacity is employed to characterize the effect of delay QoS limitations on achieved performance. Based on this, our objective was to select users to form a NOMA user pair and utilize resource efficiently. To this end, a power allocation coefficient was firstly obtained by ensuring that the achieved capacity of users with sensitive delay QoS requirements was not less than that achieved with an orthogonal multiple access (OMA) scheme. Then, considering that user selection in a delay-limited NOMA-based satellite network is intractable and non-convex, a deep reinforcement learning (DRL) algorithm was employed for dynamic user selection. Specifically, channel conditions and delay QoS requirements of users were carefully selected as state, and a DRL algorithm was used to search for the optimal user who could achieve the maximum performance with the power allocation factor, to pair with the delay QoS-sensitive user to form a NOMA user pair for each state. Simulation results are provided to demonstrate that the proposed DRL-based user selection scheme can output the optimal action in each time slot and, thus, provide superior performance than that achieved with a random selection strategy and OMA scheme.
在本文中，我们研究了功率域非正交多址接入（NOMA）方案辅助卫星网络中的用户配对问题。在考虑的场景中，假设不同的卫星应用具有各种延迟服务质量（QoS）要求，并采用有效容量的概念来表征延迟QoS限制对所实现性能的影响。基于此，我们的目标是选择用户形成NOMA用户对并有效利用资源。为此，首先通过确保具有敏感延迟QoS要求的用户所实现的容量不小于通过正交多址接入（OMA）方案所实现的容量来获得功率分配系数。然后，考虑到基于延迟受限的NOMA卫星网络中的用户选择是棘手且非凸的，采用深度强化学习（DRL）算法进行动态用户选择。具体而言，仔细选择用户的信道条件和延迟QoS要求作为状态，并使用DRL算法搜索能够在功率分配因子下实现最大性能的最优用户，以便与延迟QoS敏感用户配对，为每个状态形成一个NOMA用户对。提供的仿真结果表明，所提出的基于DRL的用户选择方案可以在每个时隙输出最优动作，因此，提供比随机选择策略和OMA方案所实现的性能更优的性能。
