Resource Allocation for 5G-UAV-Based Emergency Wireless Communications

被引:71
|
作者
Yao, Zhuohui [1 ]
Cheng, Wenchi [1 ]
Zhang, Wei [2 ]
Zhang, Hailin [1 ]
机构
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia
基金
中国国家自然科学基金;
关键词
Fading channels; Wireless communication; Shadow mapping; Resource management; Unmanned aerial vehicles; Channel models; Base stations; Emergency wireless communication networks; 5G-UAV; heterogeneous F composite fading channel; intelligent reflecting surface; capacity; energy efficiency; RELAYING COMMUNICATIONS; MIMO; OPTIMIZATION; MODULATION;
D O I
10.1109/JSAC.2021.3088684
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
For unforeseen natural disasters, such as earthquakes, hurricanes, and floods, etc., the traditional communication infrastructure is unavailable or seriously disrupted along with persistent secondary disasters. Under such circumstances, it is highly demanded to deploy emergency wireless communication (EWC) networks to restore connectivity in accident/ incident areas. The emerging fifth-generation (5G)/beyond-5G (B5G) wireless communication system, like unmanned aerial vehicle (UAV) assisted networks and intelligent reflecting surface (IRS) based communication systems, are expected to be designed or re-farmed for supporting temporary high quality communications in post-disaster areas. However, the channel characteristics of post-disaster areas quickly change as the secondary disaster resulted topographical changes, imposing new but critical challenges for EWC networks. In this paper, we propose a novel heterogeneous F composite fading channel model for EWC networks which accurately models and characterizes the composite fading channel with reflectors, path-loss exponent, fading, and shadowing parameters in 5G-UAV based EWC networks. Based on the model, we develop the optimal power allocation scheme with the simple closed-form expression and the numerical results based optimal joint bandwidth-power allocation scheme. We derive the corresponding capacities and compare the energy efficiency between IRS and traditional relay based 5G-UAVs. Numerical results show that the new heterogeneous Fisher-Snedecor F composite fading channel adapted resource allocation schemes can achieve higher capacity and energy efficiency than those of traditional channel model adapted resource allocation schemes, thus providing better communications service for post-disaster areas.
引用
收藏
页码:3395 / 3410
页数:16
相关论文
共 50 条
  • [41] Energy Efficient Resource Allocation for 5G Heterogeneous Networks Using Genetic Algorithm
    Qi, Xiaomin
    Khattak, Shahid
    Zaib, Alam
    Khan, Imdad
    IEEE ACCESS, 2021, 9 : 160510 - 160520
  • [42] Resource Allocation for Covert Wireless Transmission in UAV Communication Networks
    Wang, Danyang
    Zheng, Zeyi
    He, Guangxu
    Qi, Peihan
    Zhao, Yue
    Li, Zan
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [43] Trajectory optimization and resource allocation for UAV-assisted relaying communications
    Liu, Bin
    Zhu, Qi
    Zhu, Hongbo
    WIRELESS NETWORKS, 2020, 26 (01) : 739 - 749
  • [44] Efficient Resource Allocation for Wireless-Powered MIMO-NOMA Communications
    Breesam, Noor K.
    Al-Hussaibi, Walid A.
    Ali, Falah H.
    Al-Musawi, Israa M.
    IEEE ACCESS, 2022, 10 : 130302 - 130313
  • [45] Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications
    Zhang, Chiya
    Li, Zhukun
    He, Chunlong
    Wang, Kezhi
    Pan, Cunhua
    IEEE COMMUNICATIONS LETTERS, 2023, 27 (09) : 2398 - 2402
  • [46] Traffic-Aware Energy-Efficient Resource Allocation for RSMA Based UAV Communications
    Xiao, Meng
    Cui, Huanxi
    Huang, Dianrun
    Zhao, Zhongliang
    Cao, Xianbin
    Wu, Dapeng Oliver
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2024, 11 (03): : 2537 - 2548
  • [47] Energy-Efficient Resource Allocation for NOMA Based Small Cell Networks With Wireless Backhauls
    Muhammed, Alemu Jorgi
    Ma, Zheng
    Zhang, Zhengquan
    Fan, Pingzhi
    Larsson, Erik G.
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 68 (06) : 3766 - 3781
  • [48] Trajectory optimization and resource allocation for UAV-assisted relaying communications
    Bin Liu
    Qi Zhu
    Hongbo Zhu
    Wireless Networks, 2020, 26 : 739 - 749
  • [49] Generative Adversarial LSTM Networks Learning for Resource Allocation in UAV-Served M2M Communications
    Xu, Yi-Han
    Liu, Xin
    Zhou, Wen
    Yu, Gang
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2021, 10 (07) : 1601 - 1605
  • [50] Multi-agent based optimal UAV deployment for throughput maximization in 5 G communications
    Baghnoi, Farjam Mohammadi
    Jamali, Jasem
    Taghizadeh, Mehdi
    Fatehi, Mohammah Hossein
    WIRELESS NETWORKS, 2024, 30 (04) : 2285 - 2296