基于深度强化学习的无线多址接入方法研究

Wireless multiple access method based on deep reinforcement learning

随机多址竞争接入技术的优化可以显著增强无线网关的处理能力,也是边缘计算应用的关键前提。针对无线物联网络中存在的异构协议多址接入系统吞吐量低的问题,提出了一种基于深度强化学习的智能自适应无线多址接入方法。首先通过信道感知、动作反馈和最小化损失机制进行接入状态的强化学习,然后采用改进的近端策略优化(PPO)算法评估最优信道接入策略,实现与传统的TDMA、ALOHA协议共存互补来减少接入时隙的碰撞,从而提高接入资源利用率和网络吞吐量。结果表明,改进算法能够使网络接入吞吐量相较于未使用强化学习时提升26.6%,相比强化学习的深度Q网络(DQN)算法提升2.6%,能有效降低异构多址接入问题的复杂性且显著提高无线网关的多址接入性能。

The optimization of random multiple contention access can significantly enhance the power of wireless gateways and is also a key prerequisite for edge computing applications.Aiming at the problem of low throughput of heterogeneous protocol multiple access systems in wireless IoT networks,an intelligent adaptive wireless multiple access method based on deep reinforcement learning is proposed.First,the access state is reinforced through channel perception,action feedback and loss minimization mechanism.Then,the improved proximal strategy optimization PPO algorithm is used to evaluate the optimal channel access strategy,and complementation with traditional TDMA and ALOHA protocols are achieved to reduce the collision of access time slots,thereby improving access resource utilization and network throughput.The results show that the improved algorithm can increase the throughput by 26.6%compared with the case without reinforcement learning,and by 2.6%compared with the DQN algorithm.It can effectively reduce the complexity of multiple access and significantly improve the multiple access performance of wireless gateways.