College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
College of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel). 2023 Jan 10;23(2):807. doi: 10.3390/s23020807.
The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps.
本文研究了能量收集（EH）认知无线电网络中单个窃听者对保密通信的威胁，其中安全用户（SU）和干扰器都从主发射机（PT）处收集、存储和利用射频能量。我们的主要目标是优化安全用户和干扰器的能量收集和无线通信时隙，以最大化保密通信的长期性能。提出了一种多智能体深度强化学习（DRL）方法来解决资源分配和性能优化问题。具体来说，将从安全发送器（ST）到安全接收器（SR）链路的每个子信道以及干扰器到窃听者链路都视为一个智能体，负责探索最优的功率分配策略，同时建立时间分配网络以获得最优的 EH 时间分配策略。每个智能体都与无线通信环境动态交互。仿真结果表明，所提出的基于 DRL 的资源分配方法在保密率、收敛速度和平均转换步骤数方面优于现有方案。
