首页|灾后无人机自组网高动态多信道TDMA调度算法

灾后无人机自组网高动态多信道TDMA调度算法

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
以自然灾害、事故灾难为主要类型的极端突发事件对应急通信网络快速重组与灾情信息实时回传提出了严峻挑战,亟需构建具备快速响应能力、按需动态调整的应急通信网络.为了在断电、断路、断网"三断"极端条件下实现灾情信息实时回传,可通过多无人机形成飞行自组网对受灾区域进行网络通信覆盖.针对灾后复杂环境受限条件下飞行自组织网络通信资源调度不合理引起的信道冲突问题,提出了基于Q-learning的自适应多信道时分多址调度算法.根据无人机间的链路干扰关系建立顶点干扰图,结合图着色理论,将高动态场景下多信道时分多址调度问题抽象为动态二重着色问题.考虑无人机的高速移动性,通过自适应调整Q-learning的学习因子,实现算法的收敛速度与最优解探索能力的权衡优化,以适应高动态的网络拓扑.通过仿真实验证明,所提算法可以实现网络通信冲突和收敛速度的权衡优化,能够解决灾后高动态场景下资源分配决策与快变拓扑适配问题.
Highly dynamic multi-channel TDMA scheduling algorithm for the UAV ad hoc network in post-disaster
Extreme emergencies,mainly natural disasters and accidents,have posed serious challenges to the rapid reorganization of the emergency communication network and the real-time transmission of disaster information.It is urgent to build an emergency communication network with rapid response capabilities and dynamic adjustment on demand.In order to realize real-time transmission of disaster information under the extreme conditions of"three interruptions"of power failure,circuit interruption and network connection,the Flying Ad Hoc Network can be formed by many unmanned aerial vehicles to cover the network communication in the disaster-stricken area.Aiming at the channel collision problem caused by unreasonable scheduling of FANET communication resources under the limited conditions of complex environment after disasters,this paper proposes a multi-channel time devision multiple access(TDMA)scheduling algorithm based on adaptive Q-learning.According to the link interference relationship between UAVs,the vertex interference graph is established,and combined with the graph coloring theory,and the multi-channel TDMA scheduling problem is abstracted into a dynamic double coloring problem in highly dynamic scenarios.Considering the high-speed mobility of the UAV,the learning factor of Q-learning is adaptively adjusted according to the change of network topology,and the trade-off optimization of the convergence speed of the algorithm and the exploration ability of the optimal solution is realized.Simulation experiments show that the proposed algorithm can realize the trade-off optimization of network communication conflict and convergence speed,and can solve the problem of resource allocation decision and fast-changing topology adaptation in post-disaster high-dynamic scenarios.

unmanned aerial vehiclesmulti-channel TDMAgraph theoryadaptive Q-learning

孙彦景、李林、王博文、李松

展开 >

中国矿业大学 信息与控制工程学院,江苏 徐州 221116

中国矿业大学 物联网(感知矿山)研究中心,江苏 徐州 221008

无人机 多信道时分多址 图论 自适应Q-learning

国家自然科学基金国家自然科学基金江苏省自然科学基金江苏省教育厅未来网络科研基金中国矿业大学"工业物联网与应急协同"创新团队资助计划

6210155662071472BK20210489FNSRFP-2021-YB-122020ZY002

2024

10.19665/j.issn1001-2400.20230414

西安电子科技大学学报(自然科学版)
西安电子科技大学

西安电子科技大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.837
ISSN：1001-2400
年,卷(期)：2024.51(2)
  • 27