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Volume 45 Issue 10
Oct.  2023
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CHEN Haihua, GAO Feifan, HE Ming. Research on Relay Selection and Trajectory Optimization in Post-disaster Emergency Communication Network[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3648-3656. doi: 10.11999/JEIT221398
Citation: CHEN Haihua, GAO Feifan, HE Ming. Research on Relay Selection and Trajectory Optimization in Post-disaster Emergency Communication Network[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3648-3656. doi: 10.11999/JEIT221398

Research on Relay Selection and Trajectory Optimization in Post-disaster Emergency Communication Network

doi: 10.11999/JEIT221398
Funds:  The National Natural Science Foundation of China (61973173)
  • Received Date: 2022-11-08
  • Rev Recd Date: 2023-06-27
  • Available Online: 2023-07-03
  • Publish Date: 2023-10-31
  • In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used in post-disaster rescue by virtue of their mobility and flexibility. Considering the scenario that a survey UAV performs tasks using the emergency communication network, in order to extend the overall endurance of the emergency communication network, in this paper, the energy efficiency of the system is maximize by jointly optimizing the relay selection and flight trajectory of the UAV. In addition, the available communication energy of the relay UAVs as well as the maximum flight speed and real-time communication quality of the survey UAV are also considered in the optimization. The resultant Nondeterministic Polynomial hard (NP-hard) optimization problem is approximately solved using an alternate algorithm, which consists of successive convex approximation and tabu search algorithm. The alternate algorithm splits the original problem into two subproblems and solves them alternately to obtain the approximate optimal solution of the optimization problem. Simulation results show that the proposed algorithm has a desirable convergence and can significantly improve the energy efficiency of the system. The performance of the proposed algorithm is improved by 31.1% and 28.2% compared to the benchmark schemes of relay or trajectory optimization.
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