摘要
在射频供能的通信方式中,反向散射通信由于过度依赖射频信号导致传输速率低、传输不稳定问题;无线供能通信传输过程易因环境干扰造成数据丢包问题.为此,本文联合考虑反向散射与无线供能通信,以最大化长期平均收包率为目标,考虑有限电池容量和环境动态变化等因素,在马尔可夫决策过程框架下,研究了反向散射辅助无线供能通信系统中的模式选择和功率分配策略.设计了代价函数表示丢包开销,并计算了不同通信方式的误码率与丢包率.基于此,采用SARSA算法求解无先验信息的解,运用深度Q学习方法解决状态空间连续性问题.最后通过仿真实验表明混合传输在动态环境下的稳定性和有效性,此外,基于SARSA和深度Q学习的在线解决方案性能优于基线方案Q学习.
In the communication mode of RF powered,backscatter communication relies on RF signal,which leads to the problems of low transmission rate and unstable transmission;in the transmission process of wireless powered communication,it is easy to cause packet loss due to environmental interference.In order to maximize the long-term average packet reception rate,considering the limited battery capacity and the dynamic changes of the environment,this paper studies the mode selection and power allocation strategy in the wireless powered communication system assisted by backscatter under the framework of Markov decision process.The cost function is designed to represent the packet loss,and the bit error rate and packet loss rate of different communication modes are calculated.Based on this,the SARSA algorithm is used to solve the solution without prior information,and the deep Q learning method is used to solve the state space continuity problem.Finally,the simulation results show that the hybrid transmission is stable and effective in dynamic environment.In addition,the performance of online solution based on SARSA and deep Q learning is better than that of baseline solution Q-learning.
作者
苏小枫
陈清华
SU Xiao-feng;CHEN Qing-hua(School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310014,China;Department of Information Technology,Wenzhou Polytechnic,Wenzhou 325035,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2022年第11期2414-2421,共8页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61472254)资助
温州市科技局项目(G2020017)资助
温州职业技术学院项目(WZYBSZD202101,WZY2021010)资助。
关键词
反向散射
无线供能通信
最优控制
收包率
强化学习
backscatter
wireless powered communication
optimal control
packet reception rate
reinforcement learning
