Joint optimization method for UAV trajectory and charging/discharging in cell-free mMIMO system on WPT

WANG Qin; NING Luohan; ZHANG Yuxuan; LIU Ying; CAI Yan; ZHAO Haitao

doi:10.11959/j.issn.2096-3750.2024.00427

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Joint optimization method for UAV trajectory and charging/discharging in cell-free mMIMO system on WPT

Theory and Technology | 更新时间：2025-03-13

- Joint optimization method for UAV trajectory and charging/discharging in cell-free mMIMO system on WPT
- Chinese Journal on Internet of Things Vol. 8, Issue 3, Pages: 26-35(2024)
- 作者机构：
  
  1.南京邮电大学通信与信息工程学院，江苏南京 210003
  2.南京邮电大学物联网学院，江苏南京 210003
- 作者简介：
- 基金信息：
  
  The Jiangsu Provincial Key Research and Development Program (No.‍ ‍BE2022068, No.‍ ‍BE2022068-2), The National Natural Science Foundation of China (No.‍ ‍92367302), Jiangsu Province Frontier Leading Technology Basic Research Special Project (No. ‍‍BK20212001), The Natural Science Foundation of the Jiangsu Higher Education Institutions of China;The Major Research Plan of the National Natural Science Foundation of China(92367302)
- DOI：10.11959/j.issn.2096-3750.2024.00427
  CLC：
- Received：16 September 2024，
  
  Revised：2024-09-25，
  
  Published：10 September 2024
- 稿件说明：
移动端阅览
王琴,宁洛函,张钰瑄等.基于WPT的去蜂窝mMIMO系统中无人机轨迹与充放电联合优化方法[J].物联网学报,2024,08(03):26-35.

WANG Qin,NING Luohan,ZHANG Yuxuan,et al.Joint optimization method for UAV trajectory and charging/discharging in cell-free mMIMO system on WPT[J].Chinese Journal on Internet of Things,2024,08(03):26-35.
王琴,宁洛函,张钰瑄等.基于WPT的去蜂窝mMIMO系统中无人机轨迹与充放电联合优化方法[J].物联网学报,2024,08(03):26-35. DOI： 10.11959/j.issn.2096-3750.2024.00427.

WANG Qin,NING Luohan,ZHANG Yuxuan,et al.Joint optimization method for UAV trajectory and charging/discharging in cell-free mMIMO system on WPT[J].Chinese Journal on Internet of Things,2024,08(03):26-35. DOI： 10.11959/j.issn.2096-3750.2024.00427.

摘要

在去蜂窝大规模多输入多输出（CF-mMIMO

cell-free massive multiple-input multiple-output）系统中，无人机（UAV

unmanned aerial vehicle）作为移动接入点（AP

access point），在通信和任务执行中发挥的作用越来越重要。为了提高UAV执行复杂任务时的续航能力，研究了基于无线电力传输（WPT

wireless power transmission）的CF-mMIMO的UAV能量收发和轨迹设计方法。以中断用户的通信公平性为优化目标，考虑在UAV任务执行过程中由接入点提供能量补给的能耗约束，对UAV的飞行轨迹、充放电时隙以及波束成形进行联合优化。针对该复杂优化问题，采用基于角度搜索的通信辅助深度Q网络（DQN

deep Q-network）算法，通过限制搜索空间的角度范围，对问题进行有限空间搜索。仿真结果表明，在UAV兼顾续航以及通信情况下，该算法能显著提高UAV的使用率并增强中断用户设备（UE

user equipment）的通信公平性，实现区域动态覆盖。

Abstract

In cell-free massive multiple-input multiple-output (CF-mMIMO) system

unmanned aerial vehicles (UAV)

serving as mobile access point (AP)

are increasingly playing a significant role in both communication and task execution. To enhance the endurance of UAV during complex mission executions

the energy transmission and trajectory design for UAV within a cell-free mMIMO system were explored

utilizing wireless power transmission (WPT) as the foundation. With a focus on maintaining communication fairness for users experiencing outages

energy consumption constraints as UAV receive power replenishments from access points throughout their missions were considered. Simultaneously

a joint optimization encompassing the UAV's flight trajectory

charging/discharging time slots

and beamforming configurations were conducted. To address this complex problem

an angle search-based communication-assisted deep Q-network (DQN) algorithm was proposed

facilitating a targeted spatial exploration. Simulation results demonstrate that

while balancing endurance and communication requirements

this algorithm significantly elevates the utilization rate of UAV and enhances communication fairness for interrupted user equipment (UE)

ultimately achieving dynamic regional coverage.

关键词

Keywords

references

MARZETTA T L . Noncooperative cellular wireless with unlimited numbers of base station antennas [J ] . IEEE Transactions on Wireless Communications , 2010 , 9 ( 11 ): 3590 - 3600 .

ALONZO M , BUZZI S , ZAPPONE A , et al . Energy-efficient power control in cell-free and user-centric massive MIMO at millimeter wave [J ] . IEEE Transactions on Green Communications and Networking , 2019 , 3 ( 3 ): 651 - 663 .

LU L , LI G Y , SWINDLEHURST A L , et al . An overview of massive MIMO: benefits and challenges [J ] . IEEE Journal of Selected Topics in Signal Processing , 2014 , 8 ( 5 ): 742 - 758 .

BJÖRNSON E , HOYDIS J , SANGUINETTI L . Massive MIMO has unlimited capacity [J ] . IEEE Transactions on Wireless Communications , 2018 , 17 ( 1 ): 574 - 590 .

ZHANG J , CHEN S , LIN Y , et al . Cell-free massive MIMO: a new next-generation paradigm [J ] . IEEE Access , 2019 ( 99 ): 99878 - 99888 .

NAYEBI E , ASHIKHMIN A , MARZETTA T L , et al . Cell-free massive MIMO systems [C ] // Proceedings of the 2015 49th Asilomar Conference on Signals, Systems and Computers . Piscataway : IEEE Press , 2015 : 695 - 699 .

BUZZI S , D’ANDREA C . Cell-free massive MIMO: user-centric approach [J ] . IEEE Wireless Communications Letters , 2017 , 6 ( 6 ): 706 - 709 .

MOZAFFARI M , SAAD W , BENNIS M , et al . Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs [J ] . IEEE Transactions on Wireless Communications , 2016 , 15 ( 6 ): 3949 - 3963 .

ZENG Y , ZHANG R , LIM T J . Wireless communications with unmanned aerial vehicles: opportunities and challenges [J ] . IEEE Communications Magazine , 2016 , 54 ( 5 ): 36 - 42 .

袁昕旺 , 谢智东 , 谭信 . 无人机边缘计算中的资源管理优化研究综述 [J ] . 计算机科学 , 2022 , 49 ( 11 ): 234 - 241 .

YUAN X W , XIE Z D , TAN X . Survey of resource management optimization of UAV edge computing [J ] . Computer Science , 2022 , 49 ( 11 ): 234 - 241 .

DU J B , XU W , DENG Y S , et al . Energy-saving UAV-assisted multiuser communications with massine MIMO hybricl beamforming [J ] . IEEE Communications Letters , 2020 , 24 ( 5 ): 1100 - 1104 .

ZENG Y , XU J , ZHANG R . Energy minimization for wireless communication with rotary-wing UAV [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 4 ): 2329 - 2345 .

SURENDRAKUMARAN U , NACHIAPPAN A . Performance analysis of wireless power transfer(WPT) through two-coil and three-coil structure [C ] // Proceedings of the 2017 International Conference on Innovative Research In Electrical Sciences (IICIRES) . Piscataway : IEEE Press , 2017 : 1 - 6 .

XU J , ZENG Y , ZHANG R . UAV-enabled wireless power transfer: trajectory design and energy optimization [J ] . IEEE Transactions on Wireless Communications , 2018 , 17 ( 8 ): 5092 - 5106 .

PANG X W , ZHAO N , TANG J , et al . IRS-assisted secure UAV transmission via joint trajectory and beamforming design [J ] . IEEE Transactions on Communications , 2022 , 70 ( 2 ): 1140 - 1152 .

HAO G L , NI W L , TIAN H , et al . Mobility-aware trajectory design for aerial base station using deep reinforcement learning [C ] // Proceedings of the 2020 International Conference on Wireless Communications and Signal Processing (WCSP) . Piscataway : IEEE Press , 2020 : 1131 - 1136 .

GUO J L , HUO Y H , SHI X J , et al . 3D aerial vehicle base station (UAV-BS) position planning based on deep Q-learning for capacity enhancement of users with different QoS requirements [C ] // Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) . Piscataway : IEEE Press , 2019 : 1508 - 1512 .

WANG L , WANG K Z , PAN C H , et al . Deep Q-network based dynamic trajectory design for UAV-aided emergency communications [J ] . Journal of Communications and Information Networks , 2020 , 5 ( 4 ): 393 - 402 .

LIU Q , SHI L , SUN L L , et al . Path planning for UAV-mounted mobile edge computing with deep reinforcement learning [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 5 ): 5723 - 5728 .

HAJIJAMALI A , AZARI M , HU P , et al . Reinforcement learning for energy-efficient trajectory design of UAVs [J ] . IEEE Internet of Things Journal , 2022 . 9 ( 11 ): 9060 - 9070 .

TANG F X , ZHOU Y B , KATO N . Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet [J ] . IEEE Journal on Selected Areas in Communications , 2020 , 38 ( 12 ): 2773 - 2782 .

TANG F X , HOFNER H , KATO N , et al . A deep reinforcement learning-based dynamic traffic offloading in space-air-ground integrated networks (SAGIN) [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 40 ( 1 ): 276 - 289 .

ARANI A H , HU P , ZHU Y Y . Re-envisioning space-air-ground integrated networks: reinforcement learning for link optimization [C ] // Proceedings of the ICC 2021 - IEEE International Conference on Communications . Piscataway : IEEE Press , 2021 : 1 - 7 .

ARANI A H , HU P , ZHU Y Y . Fairness-aware link optimization for space-terrestrial integrated networks: a reinforcement learning framework [J ] . IEEE Access , 2021 , 9 : 77624 - 77636 .

李茜雯 , 陈健锋 , 崔苗 , 等 . 可充电无人机辅助数据采集系统的飞行路线与通信调度优化 [J ] . 物联网学报 , 2022 , 6 ( 3 ): 113 - 123 .

LI Q W , CHEN J F , CUI M , et al . Trajectory and communication scheduling optimization for the rechargeable UAV aided data collection system [J ] . Chinese Journal on Internet of Things , 2022 , 6 ( 3 ): 113 - 123 .

JIANG M , LI Y Q , ZHANG Q , et al . Joint position and time allocation optimization of UAV enabled time allocation optimization networks [J ] . IEEE Transactions on Communications , 2019 , 67 ( 5 ): 3806 - 3816 .

YE H T , KANG X , JOUNG J , et al . Optimization for full-duplex rotary-wing UAV-enabled wireless-powered IoT networks [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 7 ): 5057 - 5072 .

TSE D , VISWANATH P . Fundamentals of wireless communication [M ] . Cambridge, UK : Cambridge University Press , 2005 .

JAIN R , CHIU D , HAWE W . A quantitative measure of fairness and discrimination for resource allocation in shared computer systems [J ] . DEC Research Report TR-301 , 1984 : 1 - 36 .

王志宏 , 冷甦鹏 , 熊凯 . 面向无人机集群协同感知的多智能体资源分配策略 [J ] . 物联网学报 , 2023 , 7 ( 1 ): 18 - 26 .

WANG Z H , LENG S P , XIONG K . Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing [J ] . Chinese Journal on Internet of Things , 2023 , 7 ( 1 ): 18 - 26 .

MNIH V , KAVUKCUOGLU K , SILVER D , et al . Human-level control through deep reinforcement learning [J ] . Nature , 2015 , 518 ( 7540 ): 529 - 533 .

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