Performance Optimization in UAV-Assisted Wireless Powered mmWave Networks for Emergency Communications

In this paper, we explore how a rotary-wing unmanned aerial vehicle (UAV) acts as an aerial millimeter wave (mmWave) base station to provide recharging service and radio access service in a postdisaster area with unknown user distribution. The addressed optimization problem is to find out the optimal path starting and ending at the same recharging point to cover a wider area under limited battery capacity, and it can be transformed to an extended multiarmed bandit (MAB) problem. We propose the two improved path planning algorithms to solve this optimization problem, which can improve the ability to explore the unknown user distribution. Simulation results show that, in terms of the total number of served user equipment (UE), the number of visited grids, the amount of data, the average throughput, and the battery capacity utilization level, one of our algorithms is superior to its corresponding comparison algorithm, while our other algorithm is superior to its corresponding comparison algorithm in terms of the number of visited grids.

[1]  Chunxiao Jiang,et al.  Design, Modeling, Control, and Experiments for a Fish-Robot-Based IoT Platform to Enable Smart Ocean , 2021, IEEE Internet of Things Journal.

[2]  Marco Di Renzo,et al.  Performance of Generalized Spatial Modulation MIMO Over Measured 60GHz Indoor Channels , 2018, IEEE Transactions on Communications.

[3]  Honggang Zhang,et al.  Air-Ground Surveillance Sensor Network based on edge computing for target tracking , 2020, Comput. Commun..

[4]  Changqin Huang,et al.  A parallel joint optimized relay selection protocol for wake-up radio enabled WSNs , 2021, Phys. Commun..

[5]  George K. Karagiannidis,et al.  A Unified Spatial Framework for UAV-Aided MmWave Networks , 2019, IEEE Transactions on Communications.

[6]  Mehryar Mohri,et al.  Multi-armed Bandit Algorithms and Empirical Evaluation , 2005, ECML.

[7]  Cheng-Xiang Wang,et al.  Beamspace SU-MIMO for Future Millimeter Wave Wireless Communications , 2017, IEEE Journal on Selected Areas in Communications.

[8]  Saeedeh Parsaeefard,et al.  Analytical Channel Models for Millimeter Wave UAV Networks Under Hovering Fluctuations , 2019, IEEE Transactions on Wireless Communications.

[9]  Jinsong Gui,et al.  Joint access and backhaul resource allocation for D2D-assisted dense mmWave cellular networks , 2020, Comput. Networks.

[10]  Chun-Hung Liu,et al.  MmWave UAV Networks With Multi-Cell Association: Performance Limit and Optimization , 2019, IEEE Journal on Selected Areas in Communications.

[11]  Jie Wu,et al.  Improving Spectrum Efficiency of Cell-Edge Devices by Incentive Architecture Applications With Dynamic Charging , 2021, IEEE Transactions on Industrial Informatics.

[12]  Xilong Liu,et al.  Resource Allocation in UAV-Assisted M2M Communications for Disaster Rescue , 2019, IEEE Wireless Communications Letters.

[13]  Jani Saloranta,et al.  Learning-Based Trajectory Optimization for 5G mmWave Uplink UAVs , 2020, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).

[14]  Yu Lin,et al.  UAV-Assisted Emergency Communications: An Extended Multi-Armed Bandit Perspective , 2019, IEEE Communications Letters.

[15]  Rui Zhang,et al.  Wireless communications with unmanned aerial vehicles: opportunities and challenges , 2016, IEEE Communications Magazine.

[16]  Hyungsik Ju,et al.  Throughput Maximization in Wireless Powered Communication Networks , 2013, IEEE Trans. Wirel. Commun..

[17]  Yongming Huang,et al.  Power-Efficient Communication in UAV-Aided Wireless Sensor Networks , 2018, IEEE Communications Letters.

[18]  Jie Xu,et al.  An Energy Efficient Framework for UAV-Assisted Millimeter Wave 5G Heterogeneous Cellular Networks , 2019, IEEE Transactions on Green Communications and Networking.

[19]  Walid Saad,et al.  Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs , 2015, IEEE Transactions on Wireless Communications.

[20]  Jie Yang,et al.  DSF-NOMA: UAV-Assisted Emergency Communication Technology in a Heterogeneous Internet of Things , 2019, IEEE Internet of Things Journal.

[21]  Arumugam Nallanathan,et al.  Modeling and Coverage Analysis of Downlink UAV Networks with MmWave Communications , 2019, 2019 IEEE International Conference on Communications Workshops (ICC Workshops).

[22]  Rui Zhang,et al.  Placement Optimization of UAV-Mounted Mobile Base Stations , 2016, IEEE Communications Letters.

[23]  Xiang-Gen Xia,et al.  Enabling UAV cellular with millimeter-wave communication: potentials and approaches , 2016, IEEE Communications Magazine.

[24]  Lu Yang,et al.  Hierarchical Codebook and Beam Alignment for UAV Communications , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[25]  Tarik Taleb,et al.  Performance, Fairness, and Tradeoff in UAV Swarm Underlaid mmWave Cellular Networks With Directional Antennas , 2021, IEEE Transactions on Wireless Communications.

[26]  Gan Zheng,et al.  Secrecy Rate Analysis of UAV-Enabled mmWave Networks Using Matérn Hardcore Point Processes , 2018, IEEE Journal on Selected Areas in Communications.

[27]  Xueyuan Wang,et al.  Coverage Analysis for Energy-Harvesting UAV-Assisted mmWave Cellular Networks , 2019, IEEE Journal on Selected Areas in Communications.

[28]  Swades De,et al.  Dynamic Resource Allocation in UAV-Enabled mmWave Communication Networks , 2021, IEEE Internet of Things Journal.

[29]  Feng Zeng,et al.  Routing Algorithm Based on Vehicle Position Analysis for Internet of Vehicles , 2020, IEEE Internet of Things Journal.

[30]  Lu Yang,et al.  Beam Tracking and Optimization for UAV Communications , 2019, IEEE Transactions on Wireless Communications.

[31]  Xiaoheng Deng,et al.  User-Centric Computation Offloading for Edge Computing , 2021, IEEE Internet of Things Journal.

[32]  Qixun Zhang,et al.  Spectrum Management for MmWave Enabled UAV Swarm Networks: Challenges and Opportunities , 2019, IEEE Communications Magazine.

[33]  Jiajia Liu,et al.  Analysis and Optimization of Multiple Unmanned Aerial Vehicle-Assisted Communications in Post-Disaster Areas , 2018, IEEE Transactions on Vehicular Technology.

[34]  Debarati Sen,et al.  Design and Deployment of UAV-Aided Post-Disaster Emergency Network , 2019, IEEE Access.

[35]  Ismail Guvenc,et al.  Improved Throughput Coverage in Natural Disasters: Unmanned Aerial Base Stations for Public-Safety Communications , 2016, IEEE Vehicular Technology Magazine.

[36]  Jinsong Gui,et al.  Improving Lifetime of Cell-Edge Smart Sensing Devices by Incentive Architecture Based on Dynamic Charging , 2019, IEEE Access.

[37]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[38]  Enrico Natalizio,et al.  UAV-assisted disaster management: Applications and open issues , 2016, 2016 International Conference on Computing, Networking and Communications (ICNC).

[39]  Xianbin Wang,et al.  Deep Learning-Based Beam Management and Interference Coordination in Dense mmWave Networks , 2019, IEEE Transactions on Vehicular Technology.

[40]  Zhu Han,et al.  Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones , 2017, IEEE Vehicular Technology Magazine.

[41]  Jun Wu,et al.  UAV Beam Alignment for Highly Mobile Millimeter Wave Communications , 2020, IEEE Transactions on Vehicular Technology.

[42]  Chunxiao Jiang,et al.  AoI-Inspired Collaborative Information Collection for AUV-Assisted Internet of Underwater Things , 2021, IEEE Internet of Things Journal.

[43]  Weidang Lu,et al.  UAV-Assisted Emergency Networks in Disasters , 2019, IEEE Wireless Communications.