一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級(jí)搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁(yè)添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機(jī)號(hào)碼
標(biāo)題
留言內(nèi)容
驗(yàn)證碼

基于能量感知的智能反射面輔助無人機(jī)時(shí)效數(shù)據(jù)收集策略

張濤 張遷 朱穎雯 代陳

張濤, 張遷, 朱穎雯, 代陳. 基于能量感知的智能反射面輔助無人機(jī)時(shí)效數(shù)據(jù)收集策略[J]. 電子與信息學(xué)報(bào), 2025, 47(2): 427-438. doi: 10.11999/JEIT240866
引用本文: 張濤, 張遷, 朱穎雯, 代陳. 基于能量感知的智能反射面輔助無人機(jī)時(shí)效數(shù)據(jù)收集策略[J]. 電子與信息學(xué)報(bào), 2025, 47(2): 427-438. doi: 10.11999/JEIT240866
ZHANG Tao, ZHANG Qian, ZHU Yingwen, DAI Chen. Energy Aware Reconfigurable Intelligent Surface Assisted Unmanned Aerial Vehicle Age of Information Enabled Data Collection Policies[J]. Journal of Electronics & Information Technology, 2025, 47(2): 427-438. doi: 10.11999/JEIT240866
Citation: ZHANG Tao, ZHANG Qian, ZHU Yingwen, DAI Chen. Energy Aware Reconfigurable Intelligent Surface Assisted Unmanned Aerial Vehicle Age of Information Enabled Data Collection Policies[J]. Journal of Electronics & Information Technology, 2025, 47(2): 427-438. doi: 10.11999/JEIT240866

基于能量感知的智能反射面輔助無人機(jī)時(shí)效數(shù)據(jù)收集策略

doi: 10.11999/JEIT240866 cstr: 32379.14.JEIT240866
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(62402232),江蘇省高等學(xué)校自然科學(xué)研究項(xiàng)目(23KJB520024)
詳細(xì)信息
    作者簡(jiǎn)介:

    張濤:男,博士,研究方向?yàn)闊o線異構(gòu)網(wǎng)絡(luò)、通感一體低空網(wǎng)絡(luò)、自主決策技術(shù)、智能化無線網(wǎng)絡(luò)

    張遷:男,博士,研究方向?yàn)橛?jì)算機(jī)視覺、5gnr

    朱穎雯:女,副教授,研究方向?yàn)閿?shù)據(jù)挖掘、人工智能、機(jī)器學(xué)習(xí)

    代陳:男,博士,研究方向?yàn)槿怦町悩?gòu)網(wǎng)絡(luò)、感傳算一體網(wǎng)絡(luò)

    通訊作者:

    張遷 zhangqian@jsou.edu.cn

  • 中圖分類號(hào): TN929.5

Energy Aware Reconfigurable Intelligent Surface Assisted Unmanned Aerial Vehicle Age of Information Enabled Data Collection Policies

Funds: The National Natural Science Foundation of China (62402232), The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (23KJB520024)
  • 摘要: 為了應(yīng)對(duì)智能反射面(RIS)輔助的無人機(jī)(UAV)在物聯(lián)網(wǎng)數(shù)據(jù)收集過程中能量高效利用與信息收集時(shí)效性之間的均衡問題,該文提出一種基于深度強(qiáng)化學(xué)習(xí)的數(shù)據(jù)收集優(yōu)化策略。針對(duì)無人機(jī)在數(shù)據(jù)采集過程中的飛行能耗、通信復(fù)雜性及采集信息時(shí)效性(AoI)約束,設(shè)計(jì)了一種基于雙深度Q網(wǎng)絡(luò)(DDQN)的聯(lián)合優(yōu)化方案,涵蓋無人機(jī)軌跡規(guī)劃、物聯(lián)網(wǎng)設(shè)備調(diào)度以及智能反射面相位調(diào)整。該方案有效緩解了傳統(tǒng)Q學(xué)習(xí)方法中Q值過估計(jì)的問題,使無人機(jī)能夠根據(jù)實(shí)時(shí)環(huán)境動(dòng)態(tài)調(diào)整飛行軌跡和通信策略,從而在提升數(shù)據(jù)傳輸效率的同時(shí)降低能量消耗。仿真結(jié)果表明,與傳統(tǒng)方法相比,所提方案能夠顯著提高數(shù)據(jù)收集效率。此外,通過合理分配能量與通信資源,所提方案能夠動(dòng)態(tài)適應(yīng)不同通信環(huán)境參數(shù)變化,確保系統(tǒng)在能耗與AoI之間達(dá)到最佳均衡。
  • 圖  1  RIS輔助UAV數(shù)據(jù)收集模型

    圖  2  RIS輔助UAV時(shí)效數(shù)據(jù)收集策略結(jié)構(gòu)

    圖  3  收斂曲線

    圖  4  UAV移動(dòng)軌跡圖

    圖  5  平均數(shù)據(jù)采集速率需求對(duì)優(yōu)化性能的影響

    圖  6  IoT設(shè)備數(shù)對(duì)優(yōu)化性能的影響

    1  基于DDQN的UAV數(shù)據(jù)采集算法

     輸入:UAV觀測(cè)到的環(huán)境狀態(tài)
     輸出:UAV軌跡和IoT設(shè)備調(diào)度策略
     (1) 隨機(jī)初始化神經(jīng)網(wǎng)絡(luò)參數(shù)
     (2) for episode = 1,2,···,NEP do
     (3)  初始化仿真環(huán)境參數(shù)
     (4)  for t = 1,2,···,T do
     (5)   根據(jù)$ {Q_\pi }({\boldsymbol{s}},{\boldsymbol{a}};\theta ) $選取狀態(tài)$ {\boldsymbol{s}} $對(duì)應(yīng)的動(dòng)作a;
     (6)   根據(jù)式(31)獲得RIS的相位偏移;
     (7)   執(zhí)行動(dòng)作控制UAV飛行和IoT調(diào)度后,使用式(23)計(jì)算
         瞬時(shí)獎(jiǎng)勵(lì)$ r $并獲得下一時(shí)刻的狀態(tài)$ {\boldsymbol{s}}' $;
     (8)   if UAV移動(dòng)躍出邊界 do
     (9)    狀態(tài)回滾$ {\boldsymbol{s}}' \leftarrow {\boldsymbol{s}} $,為$ r $添加懲罰項(xiàng);
     (10)   將環(huán)境數(shù)據(jù)$ ({\boldsymbol{s}},{\boldsymbol{a}},{{r}},{\boldsymbol{s}}') $存入經(jīng)驗(yàn)池;
     (11)   if 經(jīng)驗(yàn)池存滿數(shù)據(jù) do
     (12)    從經(jīng)驗(yàn)池取出Ns個(gè)樣本;
     (13)    對(duì)于每個(gè)樣本,用目標(biāo)值網(wǎng)絡(luò)計(jì)算式(29);
     (14)    更新當(dāng)前Q網(wǎng)絡(luò)以最小化損失式(30);
     (15)    每隔一定步長(zhǎng)$ {\theta ^ - } \leftarrow \theta $;
     (16)  end
     (17) end
    下載: 導(dǎo)出CSV

    表  1  主要仿真參數(shù)

    參數(shù) 數(shù)值
    $ {x^{{\text{MAX}}}} $, $ {y^{{\text{MAX}}}} $(m) 1 000, 1 000
    $ L $,$ {z^{{\text{MIN}}}} $, $ {z^{{\text{MAX}}}} $(m) 10, 60, 80
    $ {l_t} $(m) {0, 1, 2}
    $ {c_1} $, $ {c_2} $ 12.081, 0.113 95[7]
    $ {\mu ^{{\text{LoS}}}} $ , $ {\mu ^{{\text{NLoS}}}} $ 1.445 44, 199.526[7]
    $ {K_1} $, $ {K_2} $ 3, 4
    $ \rho $, $ \beta $ (dBm) –30, –50[8]
    $ {\delta _1} $, $ {\delta _2} $(dBm/Hz) –174, –174
    $ {B_1} $, $ {B_2} $(MHz) 2, 2
    $ {P_1} $, $ {P_2} $(W) 0.5, 0.8
    $ {P_{\mathrm{B}}} $, $ {P_{\mathrm{I}}} $, $ {P_{\mathrm{V}}} $ 88.63, 79.85, 11.46
    $ {\zeta _1} $, $ {\zeta _2} $, $ {\zeta _3} $, $ {\zeta _4} $ 100, 1, 10, 10
    $ \varLambda $, $ \varXi $ 0.6, 0.05
    $ {U_{{\text{tip}}}} $(m/s) 120[29]
    $ \varUpsilon $(kg/m3) 1.225[29]
    $ G $(m2) 0.503[29]
    $ \chi $(bit) 1 000
    下載: 導(dǎo)出CSV
  • [1] 段潔, 胡顯靜, 林歡, 等. 面向物聯(lián)網(wǎng)數(shù)據(jù)特征的信息中心網(wǎng)絡(luò)緩存方案[J]. 電子與信息學(xué)報(bào), 2021, 43(8): 2240–2248. doi: 10.11999/JEIT200631.

    DUAN Jie, HU Xianjing, LIN Huan, et al. Information-centric networking caching scheme for data characteristics of internet of things[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2240–2248. doi: 10.11999/JEIT200631.
    [2] JAVAID S, SAEED N, QADIR Z, et al. Communication and control in collaborative UAVs: Recent advances and future trends[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 5719–5739. doi: 10.1109/TITS.2023.3248841.
    [3] 劉志新, 趙松晗, 楊毅, 等. 智能反射面輔助的無人機(jī)無線攜能通信網(wǎng)絡(luò)吞吐量最大化算法研究[J]. 電子與信息學(xué)報(bào), 2022, 44(7): 2325–2331. doi: 10.11999/JEIT220195.

    LIU Zhixin, ZHAO Songhan, YANG Yi, et al. Throughput maximization algorithm for intelligent reflecting surface-aided unmanned aerial vehicle communication networks with wireless energy transfer[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2325–2331. doi: 10.11999/JEIT220195.
    [4] 張?jiān)阼? 江浩. 智能超表面使能無人機(jī)高能效通信信道建模與傳輸機(jī)理分析[J]. 電子學(xué)報(bào), 2023, 51(10): 2623–2634. doi: 10.12263/DZXB.20221352.

    ZHANG Zaichen and JIANG Hao. Channel modeling and characteristics analysis for high energy-efficient RIS-assisted UAV communications[J]. Acta Electronica Sinica, 2023, 51(10): 2623–2634. doi: 10.12263/DZXB.20221352.
    [5] SAVKIN A V, HUANG Chao, and NI Wei. Joint multi-UAV path planning and LoS communication for mobile-edge computing in IoT networks with RISs[J]. IEEE Internet of Things Journal, 2023, 10(3): 2720–2727. doi: 10.1109/JIOT.2022.3215255.
    [6] SAVKIN A V, HUANG Chao, and NI Wei. Collision-free 3-D navigation of a UAV team for optimal data collection in Internet-of-Things networks with reconfigurable intelligent surfaces[J]. IEEE Systems Journal, 2023, 17(3): 4070–4077. doi: 10.1109/JSYST.2023.3269095.
    [7] LIN Xinzhong, XIE Cong, XIE Wenwu, et al. Security performance analysis of RIS-assisted UAV wireless communication in industrial IoT[J]. The Journal of Supercomputing, 2022, 78(4): 5957–5973. doi: 10.1007/s11227-021-04095-7.
    [8] ZHAI Liangsen, ZOU Yulong, ZHU Jia, et al. RIS-assisted UAV-enabled wireless powered communications: System modeling and optimization[J]. IEEE Transactions on Wireless Communications, 2024, 23(5): 5094–5108. doi: 10.1109/TWC.2023.3324500.
    [9] RANJHA A and KADDOUM G. URLLC facilitated by mobile UAV relay and RIS: A joint design of passive beamforming, blocklength, and UAV positioning[J]. IEEE Internet of Things Journal, 2021, 8(6): 4618–4627. doi: 10.1109/JIOT.2020.3027149.
    [10] ESKANDARI M, HUANG Hailong, SAVKIN A V, et al. Model predictive control-based 3D navigation of a RIS-equipped UAV for LoS wireless communication with a ground intelligent vehicle[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(3): 2371–2384. doi: 10.1109/TIV.2022.3232890.
    [11] LI Linpei, GUAN Wanqing, ZHAO Chuan, et al. Trajectory planning, phase shift design, and IoT devices association in flying-RIS-assisted mobile edge computing[J]. IEEE Internet of Things Journal, 2024, 11(1): 147–157. doi: 10.1109/JIOT.2023.3300700.
    [12] AL-HILO A, SAMIR M, ELHATTAB M, et al. RIS-assisted UAV for timely data collection in IoT networks[J]. IEEE Systems Journal, 2023, 17(1): 431–442. doi: 10.1109/JSYST.2022.3215279.
    [13] TYROVOLAS D, MEKIKIS P V, TEGOS S A, et al. Energy-aware design of UAV-mounted RIS networks for IoT data collection[J]. IEEE Transactions on Communications, 2023, 71(2): 1168–1178. doi: 10.1109/TCOMM.2022.3229672.
    [14] LIU Jianghu and ZHANG Hongtao. Height-fixed UAV enabled energy-efficient data collection in RIS-aided wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2023, 22(11): 7452–7463. doi: 10.1109/TWC.2023.3250988.
    [15] ALMASOUD A M. Robust anti-jamming technique for UAV data collection in IoT using landing platforms and RIS[J]. IEEE Access, 2023, 11: 70635–70651. doi: 10.1109/ACCESS.2023.3294596.
    [16] YANG Bowen, YU Yao, LI Jianqi, et al. An AoI-guaranteed sensor data collection strategy for RIS-assisted UAV communication system[C]. 2023 IEEE/CIC International Conference on Communications in China, Dalian, China, 2023: 1–6. doi: 10.1109/ICCC57788.2023.10233285.
    [17] SAMIR M, ELHATTAB M, ASSI C, et al. Optimizing age of information through aerial reconfigurable intelligent surfaces: A deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2021, 70(4): 3978–3983. doi: 10.1109/TVT.2021.3063953.
    [18] HUANG Hongli, LIU Juan, and XIE Lingfu. Intelligent reflecting surface-assisted fresh data collection in UAV communications[C]. The 11th International Conference in Communications, Signal Processing, and Systems, Singapore, 2022: 189–197. doi: 10.1007/978-981-99-2362-5_24.
    [19] XIAO Xiongbing, WANG Xiumin, and LIN Weiwei. Joint AoI-aware UAVs trajectory planning and data collection in UAV-based IoT systems: A deep reinforcement learning approach[J]. IEEE Transactions on Consumer Electronics, 2024, 70(4): 6484–6495. doi: 10.1109/TCE.2024.3440406.
    [20] JIANG Wenwen, AI Bo, LI Mushu, et al. Average age-of-information minimization in aerial IRS-assisted data delivery[J]. IEEE Internet of Things Journal, 2023, 10(17): 15133–15146. doi: 10.1109/JIOT.2023.3264618.
    [21] CHEN Zhen, GUO Yeyong, ZHANG Peichang, et al. Physical layer security improvement for hybrid RIS-assisted MIMO communications[J]. IEEE Communications Letters, 2024, 28(11): 2493–2497. doi: 10.1109/LCOMM.2024.3427010.
    [22] RUAN Chengyao, ZHANG Zaichen, JIANG Hao, et al. Wideband near-field channel covariance estimation for XL-MIMO systems in the face of beam split[J]. IEEE Transactions on Vehicular Technology. doi: 10.1109/TVT.2024.3471733.
    [23] QIU Chen, WEI Zhiqing, YUAN Xin, et al. Multiple UAV-mounted base station placement and user association with joint fronthaul and backhaul optimization[J]. IEEE Transactions on Communications, 2020, 68(9): 5864–5877. doi: 10.1109/TCOMM.2020.3001136.
    [24] SHI Wangqi, JIANG Hao, XIONG Baiping, et al. RIS-empowered V2V communications: Three-dimensional beam domain channel modeling and analysis[J]. IEEE Transactions on Wireless Communications, 2024, 23(11): 15844–15857. doi: 10.1109/TWC.2024.3434568.
    [25] WEI Zhiqiang, CAI Yuanxin, SUN Zhuo, et al. Sum-rate maximization for IRS-assisted UAV OFDMA communication systems[J]. IEEE Transactions on Wireless Communications, 2021, 20(4): 2530–2550. doi: 10.1109/TWC.2020.3042977.
    [26] XU Peng, NIU Wenqi, CHEN Gaojie, et al. Performance analysis of RIS-assisted systems with statistical channel state information[J]. IEEE Transactions on Vehicular Technology, 2022, 71(1): 1089–1094. doi: 10.1109/TVT.2021.3126374.
    [27] JIANG Hao, SHI Wangqi, ZHANG Zaichen, et al. Large-scale RIS enabled air-ground channels: Near-field modeling and analysis[J]. arXiv: 2403.12781, 2024.
    [28] LIN Na, TANG Hailun, ZHAO Liang, et al. A PDDQNLP algorithm for energy efficient computation offloading in UAV-assisted MEC[J]. IEEE Transactions on Wireless Communications, 2023, 22(12): 8876–8890. doi: 10.1109/TWC.2023.3266497.
    [29] CHEN Guqiao, CHENG Changjun, XU Xiaoli, et al. Minimizing the age of information for data collection by cellular-connected UAV[J]. IEEE Transactions on Vehicular Technology, 2023, 72(7): 9631–9635. doi: 10.1109/TVT.2023.3249747.
    [30] WANG Xu, WANG Sen, LIANG Xingxing, et al. Deep reinforcement learning: A survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(4): 5064–5078. doi: 10.1109/TNNLS.2022.3207346.
    [31] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529–533. doi: 10.1038/nature14236.
    [32] VAN HASSELT H, GUEZ A, and SILVER D. Deep reinforcement learning with double q-learning[C]. The 30th AAAI Conference on Artificial Intelligence, Phoenix, USA, 2016: 2094–2100. doi: 10.1609/aaai.v30i1.10295.
    [33] WANG Liang, WANG Kezhi, PAN Cunhua, et al. Joint trajectory and passive beamforming design for intelligent reflecting surface-aided UAV communications: A deep reinforcement learning approach[J]. IEEE Transactions on Mobile Computing, 2023, 22(11): 6543–6553. doi: 10.1109/TMC.2022.3200998.
    [34] MEI Haibo, YANG Kun, LIU Qiang, et al. 3D-trajectory and phase-shift design for RIS-assisted UAV systems using deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2022, 71(3): 3020–3029. doi: 10.1109/TVT.2022.3143839.
  • 加載中
圖(6) / 表(2)
計(jì)量
  • 文章訪問數(shù):  461
  • HTML全文瀏覽量:  166
  • PDF下載量:  60
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2024-10-14
  • 修回日期:  2025-01-07
  • 網(wǎng)絡(luò)出版日期:  2025-01-11
  • 刊出日期:  2025-02-28

目錄

    /

    返回文章
    返回