一种基于NOMA的Q学习卫星通信随机接入方法

A NOMA-based Q-learning random access scheme for satellite communications

基于非正交多址接入(NOMA)的Q学习(Q-Learning)随机接入方法(NORA-QL)是实现物联网中海量设备泛在接入的一项有效技术。为了解决NORA-QL方法仍存在的传输能效和过载容量较低的问题,提出了一种适合卫星通信网络的改进方法(I-NORA-QL)。针对传输功耗高的问题,I-NORA-QL利用卫星广播的全局信息改进Q学习的学习策略,将用户发射功率用于奖励函数的构造,同时将学习速率设计为与算法迭代次数相关的衰减函数。I-NORA-QL进一步在接入类别限制ACB(Access Class Barring)的基础上,基于学习过程中的Q值特性和负载估计实现ACB限制因子的自适应调整以进行过载控制。仿真结果表明,提出的I-NORA-QL改进方法相比于现有其他方法,能够有效降低用户设备的平均功耗,且在系统过载状态下可以显著提高吞吐量。

The Non-Orthogonal Multiple Access(NOMA)-based Q-learning random access method(NORA-QL)is an effective technique to achieve ubiquitous access to a large number of devices in the Internet of Things.In order to solve the problems of low transmission energy efficiency and low overload capacity in the NORA-QL method,an improved method(I-NORA-QL)suitable for satellite communication networks is proposed.To address the problem of high transmission power consumption,I-NORA-QL improves the learning strategy of Q-learning using global information from satellite broadcasting,the transmitted power of user equipment is used in the construction of the reward function,and the learning rate is designed as a decay function related to the number of iterations of the algorithm.Furthermore,based on the Access Class Barring(ACB),I-NORA-QL realizes the adaptive adjustment of ACB barring factor based on the Q value characteristics and load estimation during the learning process to carry out overload control.Simulation results show that,compared with other existing methods,the proposed I-NORA-QL improved method can effectively reduce the average power consumption of user devices,and significantly improve the throughput under system overload state.