TY - JOUR
T1 - Joint Power and Coverage Control of Massive UAVs in Post-Disaster Emergency Networks
T2 - An Aggregative Game-Theoretic Learning Approach
AU - Wu, Jing
AU - Chen, Qimei
AU - Jiang, Hao
AU - Wang, Haozhao
AU - Xie, Yulai
AU - Xu, Wenzheng
AU - Zhou, Pan
AU - Xu, Zichuan
AU - Chen, Lixing
AU - Li, Beibei
AU - Wang, Xiumin
AU - Wu, Dapeng Oliver
PY - 2024/7
Y1 - 2024/7
N2 - In the context of 6G, airborne post-disaster emergency networks (PENs) could be resilient in calamities and offer hope for disaster recovery in the underserved disaster zone. Unmanned aerial vehicles (UAV)-enabled ad-hoc network is such a significant contingency plan for communication after natural disasters, such as typhoon and earthquake. Specially, we present possible technological solutions for PENs targets for counteracting any large-scale disasters to achieve efficient communication and rapid network deployment. To this end, in this paper we jointly take power and coverage control into account during the UAV network configuration. An innovative noncooperative game theoretical model and improved binary log-linear algorithm (BLLA) have been adopted to achieve the optimal system performance. To deal with the challenges brought by highly dynamic post-disaster circumstances, we employ the aggregative game which is able to capture the strategies updating constraint and strategy-deciding error in large-scale UAV networks. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and hence reduce strategy updating time and energy consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Superior performance gains are seen in SNR and network coverage and hence render a great network solution in emergency scenarios. © 2024 IEEE.
AB - In the context of 6G, airborne post-disaster emergency networks (PENs) could be resilient in calamities and offer hope for disaster recovery in the underserved disaster zone. Unmanned aerial vehicles (UAV)-enabled ad-hoc network is such a significant contingency plan for communication after natural disasters, such as typhoon and earthquake. Specially, we present possible technological solutions for PENs targets for counteracting any large-scale disasters to achieve efficient communication and rapid network deployment. To this end, in this paper we jointly take power and coverage control into account during the UAV network configuration. An innovative noncooperative game theoretical model and improved binary log-linear algorithm (BLLA) have been adopted to achieve the optimal system performance. To deal with the challenges brought by highly dynamic post-disaster circumstances, we employ the aggregative game which is able to capture the strategies updating constraint and strategy-deciding error in large-scale UAV networks. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and hence reduce strategy updating time and energy consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Superior performance gains are seen in SNR and network coverage and hence render a great network solution in emergency scenarios. © 2024 IEEE.
KW - Aggregative Game
KW - Autonomous aerial vehicles
KW - Coverage Control
KW - Disasters
KW - Energy consumption
KW - Games
KW - Heuristic algorithms
KW - Post-Disaster Wireless Communications
KW - Signal to noise ratio
KW - Synchronous Learning
KW - UAV
KW - Wireless communication
UR - http://www.scopus.com/inward/record.url?scp=85190172400&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85190172400&origin=recordpage
U2 - 10.1109/TNSE.2024.3385797
DO - 10.1109/TNSE.2024.3385797
M3 - RGC 21 - Publication in refereed journal
SN - 2327-4697
VL - 11
SP - 3782
EP - 3799
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
IS - 4
ER -