基于区块链的无人机辅助铁路通信系统低能耗传输方法  

Low-Energy Transmission Method for Blockchain-Based UAV-Assisted Railway Communication System

作　　者：代海波 吴天奇 梁轶群 张哲 李春国[3] DAI Haibo;WU Tianqi;LIANG Yiqun;ZHANG Zhe;LI Chunguo(School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Signal and Communication Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;School of Information Science and Engineering,Southeast University,Nanjing 210096,China)

机构地区：[1]南京邮电大学物联网学院,南京210003 [2]中国铁道科学研究院集团有限公司通信信号研究所,北京100081 [3]东南大学信息科学与工程学院,南京210096

出　　处：《数据采集与处理》2025年第1期72-85,共14页Journal of Data Acquisition and Processing

基　　金：国家铁路智能运输系统工程技术研究中心开放课题(RITS2021KF02);江苏省重点研发计划(BE2021013-3)。

摘　　要：无人机基站具有快速部署、机动覆盖等特性,使其成为铁路应急通信的一种有效解决方案。然而,无人机组成的低空通信网络既有储能有限的约束又存在数据被窃听或篡改的风险。将区块链技术引入到无人机辅助的铁路无线通信系统中以保障数据安全。在传输时延和数据队列稳定性约束下,提出了一种最小化通信系统能耗和区块链系统时延的联合优化问题。为了求解这个非凸混合整数且时变的随机优化问题,提出基于Lyapunov的drift-plus-penalty方法将长期的随机优化问题转化为多个时隙的子问题,并设计基于D3QN-TD3的深度强化学习算法求解,得到每个时隙内的最优关联策略和功率控制。实验结果表明,所提方法在减少能耗和时延方面取得了显著的效果。Unmanned aerial vehicle(UAV)‑mounted base stations possess characteristics,such as rapid deployment and flexible coverage,making them an effective solution for emergency communication in railways.However,the low‑altitude communication network formed by UAVs faces the constraint of the limited energy storage and the risk of data eavesdropping or tampering.This paper introduces blockchain technology into a UAV-assisted railway wireless communication system to ensure data security.Considering the constraints on transmission delay and data queue stability,this paper proposes a joint optimization problem aimed at minimizing the energy consumption of the UAV-assisted communication system and the latency of the blockchain.To solve this non-convex,mixed-integer,and time-varying stochastic optimization problem,a Lyapunov-based drift-plus-penalty method is proposed to transform the long-term stochastic optimization problem into sub-problems of multiple time slots.Deep reinforcement learning based on D3QN-TD3 is designed to solve these sub-problems,and then the optimal association strategies and power control for each time slot are obtained.Experimental results demonstrate the significant effectiveness of the proposed method in reducing the energy consumption and delay.

关 键 词：无人机通信网络 区块链 关联策略 深度强化学习 Lyapunov优化 

分 类 号：U285.21[交通运输工程—交通信息工程及控制]