Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1209-1218.DOI: 10.11772/j.issn.1001-9081.2023040482
Special Issue: 网络与通信
• Network and communications • Previous Articles Next Articles
Rui TANG1,2(), Shibo YUE2, Ruizhi ZHANG3, Chuan LIU1, Chuanlin PANG2
Received:
2023-04-25
Revised:
2023-07-12
Accepted:
2023-07-14
Online:
2023-12-04
Published:
2024-04-10
Contact:
Rui TANG
About author:
TANG Rui, born in 1988, Ph. D., associate professor. His research interests include MAC layer design in radio communication systems.Supported by:
唐睿1,2(), 岳士博2, 张睿智3, 刘川1, 庞川林2
通讯作者:
唐睿
作者简介:
唐睿(1988—),男,甘肃兰州人,副教授,博士,CCF会员,主要研究方向:无线通信系统MAC层设计 allanxjtu@163.com基金资助:
CLC Number:
Rui TANG, Shibo YUE, Ruizhi ZHANG, Chuan LIU, Chuanlin PANG. Energy efficiency optimization mechanism for UAV-assisted and non-orthogonal multiple access-enabled data collection system[J]. Journal of Computer Applications, 2024, 44(4): 1209-1218.
唐睿, 岳士博, 张睿智, 刘川, 庞川林. UAV协助下非正交多址接入使能的数据采集系统中能效优化机制[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1209-1218.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023040482
模型 | 参数 | 设置值 |
---|---|---|
系统模型[ | 地表链路信道模型中参数 | -50 dB |
地表信道模型中路径损耗因子 | 3 | |
地表信道模型中小尺度衰落分布 | 瑞利分布 | |
地空信道模型中视距、非视距概率参数 | 9.167 7 | |
地空信道模型中视距、非视距概率参数 | 0.158 1 | |
地空信道模型中路径损耗因子 | 2 | |
地空信道视距传播下 额外路径损耗均值 | 1 dB | |
地空信道视距传播下 额外路径损耗均值 | 20 dB | |
基站侧加性高斯白噪声功率 | -113 dBm | |
总电路功率开销 | 18 dBm | |
传感器侧最大发射功率 | 23 dBm | |
UAV基站的最低飞行高度 | 60 m | |
UAV基站的最高飞行高度 | 100 m | |
DNN[ | DNN的隐含层数 | 3 |
每个隐含层中神经元数 | 128/64/32 | |
隐含层的神经元中激活函数 | ReLU | |
输出层的神经元中激活函数 | sigmoid | |
HHO[ | 种群数 | 20 |
最大迭代次数 | 200 | |
随机生成的传感器拓扑数 | 200 | |
LF函数的参数 | 1.5 |
Tab. 1 Setting of parameters in system model and energy efficiency optimization mechanism
模型 | 参数 | 设置值 |
---|---|---|
系统模型[ | 地表链路信道模型中参数 | -50 dB |
地表信道模型中路径损耗因子 | 3 | |
地表信道模型中小尺度衰落分布 | 瑞利分布 | |
地空信道模型中视距、非视距概率参数 | 9.167 7 | |
地空信道模型中视距、非视距概率参数 | 0.158 1 | |
地空信道模型中路径损耗因子 | 2 | |
地空信道视距传播下 额外路径损耗均值 | 1 dB | |
地空信道视距传播下 额外路径损耗均值 | 20 dB | |
基站侧加性高斯白噪声功率 | -113 dBm | |
总电路功率开销 | 18 dBm | |
传感器侧最大发射功率 | 23 dBm | |
UAV基站的最低飞行高度 | 60 m | |
UAV基站的最高飞行高度 | 100 m | |
DNN[ | DNN的隐含层数 | 3 |
每个隐含层中神经元数 | 128/64/32 | |
隐含层的神经元中激活函数 | ReLU | |
输出层的神经元中激活函数 | sigmoid | |
HHO[ | 种群数 | 20 |
最大迭代次数 | 200 | |
随机生成的传感器拓扑数 | 200 | |
LF函数的参数 | 1.5 |
传感器数K | 运算时间/s | ||||||
---|---|---|---|---|---|---|---|
对比 机制1 | 对比 机制2 | 对比 机制3 | 对比 机制4 | 对比 机制5 | 对比 机制6 | 本文 机制 | |
4 | 0.155 | 0.156 | 0.242 | 3 370 | 0.102 | 0.513 | 0.258 |
6 | 0.132 | 0.158 | 0.275 | 3 317 | 0.114 | 0.522 | 0.282 |
8 | 0.134 | 0.136 | 0.301 | 3 363 | 0.121 | 0.516 | 0.296 |
10 | 0.167 | 0.172 | 0.283 | 3 390 | 0.123 | 0.535 | 0.277 |
12 | 0.149 | 0.186 | 0.292 | 3 604 | 0.124 | 0.537 | 0.288 |
Tab. 2 Computing time changing with sensor number for different mechanisms
传感器数K | 运算时间/s | ||||||
---|---|---|---|---|---|---|---|
对比 机制1 | 对比 机制2 | 对比 机制3 | 对比 机制4 | 对比 机制5 | 对比 机制6 | 本文 机制 | |
4 | 0.155 | 0.156 | 0.242 | 3 370 | 0.102 | 0.513 | 0.258 |
6 | 0.132 | 0.158 | 0.275 | 3 317 | 0.114 | 0.522 | 0.282 |
8 | 0.134 | 0.136 | 0.301 | 3 363 | 0.121 | 0.516 | 0.296 |
10 | 0.167 | 0.172 | 0.283 | 3 390 | 0.123 | 0.535 | 0.277 |
12 | 0.149 | 0.186 | 0.292 | 3 604 | 0.124 | 0.537 | 0.288 |
1 | WEI Z, ZHU M, ZHANG N, et al. UAV assisted data collection for internet of things: a survey[J]. IEEE Internet of Things Journal, 2022, 9(17): 15460-15483. 10.1109/jiot.2022.3176903 |
2 | XU Y, GUI G, GACANIN H, et al. A survey on resource allocation for 5G heterogeneous networks: current research, future trends and challenges[J]. IEEE Communications Surveys & Tutorials, 2021, 23(2): 668-695. 10.1109/comst.2021.3059896 |
3 | ZHAN C, ZENG Y, ZHANG R. Energy-efficient data collection in UAV enabled wireless sensor network[J]. IEEE Wireless Communications Letters, 2018, 7(3): 328-331. 10.1109/lwc.2017.2776922 |
4 | 王岱巍,徐高潮,李龙.无人机辅助的移动边缘计算中的任务分配策略[J].计算机应用,2021,41(10): 2928-2936. 10.11772/j.issn.1001-9081.2020121917 |
WANG D W, XU G C, LI L. Task allocation strategy in unmanned aerial vehicle-assisted mobile edge computing[J]. Journal of Computer Applications, 2021, 41(10): 2928-2936. 10.11772/j.issn.1001-9081.2020121917 | |
5 | ZHAN C, ZENG Y. Completion time minimization for multi-UAV-enabled data collection[J]. IEEE Transactions on Wireless Communications, 2019, 18(10): 4859-4872. 10.1109/twc.2019.2930190 |
6 | WANG Z, LIU R, LIU Q, et al. Energy-efficient data collection and device positioning in UAV-assisted IoT[J]. IEEE Internet of Things Journal, 2020, 7(2): 1122-1139. 10.1109/jiot.2019.2952364 |
7 | FU S, TANG Y, WU Y, et al. Energy-efficient UAV-enabled data collection via wireless charging: a reinforcement learning approach [J]. IEEE Internet of Things Journal, 2021, 8(12): 10209-10219. 10.1109/jiot.2021.3051370 |
8 | CHEN W, ZHAO S, ZHANG R, et al. UAV-assisted data collection with nonorthogonal multiple access[J]. IEEE Internet of Things Journal, 2021, 8(1): 501-511. 10.1109/jiot.2020.3005271 |
9 | MU X, LIU Y, GUO L, et al. Energy-constrained UAV data collection systems: NOMA and OMA[J]. IEEE Transactions on Vehicular Technology, 2021, 70(7): 6898-6912. 10.1109/tvt.2021.3086556 |
10 | JIANG M, LI Y, 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. 10.1109/tcomm.2019.2896973 |
11 | WANG W, ZHAO N, CHEN L, et al. UAV-assisted time-efficient data collection via uplink NOMA[J]. IEEE Transactions on Communications, 2021, 69(11): 7851-7863. 10.1109/tcomm.2021.3106134 |
12 | TANG R, ZHANG R, XU Y, et al. Energy-efficient optimization algorithm in NOMA-based UAV-assisted data collection systems[J]. IEEE Wireless Communications Letters, 2023, 12(1): 158-162. 10.1109/lwc.2022.3219675 |
13 | DAI L, WANG B, YUAN Y, et al. Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends[J]. IEEE Communications Magazine, 2015, 53(9): 74-81. 10.1109/mcom.2015.7263349 |
14 | MARAQA O, RAJASEKARAN A S, AL-AHMADI S, et al. A survey of rate-optimal power domain NOMA with enabling technologies of future wireless networks[J]. IEEE Communications Surveys & Tutorials, 2020, 22(4): 2192-2235. 10.1109/comst.2020.3013514 |
15 | TANG R, ZHANG R, XIA Y, et al. Joint mode selection and power allocation for NOMA systems with D2D communication[C]// Proceedings of the 2021 IEEE/CIC International Conference on Communications in China. Piscataway: IEEE, 2021: 606-611. 10.1109/iccc52777.2021.9580380 |
16 | HERBERT S, WASSELL I, T-H LOH, et al. Characterizing the spectral properties and time variation of the in-vehicle wireless communication channel[J]. IEEE Transactions on Communications, 2014, 62(7): 2390-2399. 10.1109/TCOMM.2014.2328635 |
17 | AL-HOURANI A, KANDEEPAN S, LARDNER S. Optimal LAP altitude for maximum coverage[J]. IEEE Wireless Communications Letters, 2014, 3(6): 569-572. 10.1109/lwc.2014.2342736 |
18 | ZHANG R, TANG R, XU Y, et al. Resource allocation for UAV-assisted NOMA systems with dual connectivity[J]. IEEE Wireless Communications Letters, 2023, 12(2): 341-345. 10.1109/lwc.2022.3226265 |
19 | ZAPPONE A, JORSWIECK E. Energy efficiency in wireless networks via fractional programming theory[J]. Foundations and Trends in Communications and Information Theory, 2015, 11(3/4): 185-396. 10.1561/0100000088 |
20 | TANG R, CHENG J, CAO Z. Energy-efficient power allocation for cooperative NOMA systems with IBFD-enabled two-way cognitive transmission[J]. IEEE Communications Letters, 2019, 23(6): 1101-1104. 10.1109/lcomm.2019.2913424 |
21 | XU X, CHEN Q, MU X, et al. Graph-embedded multi-agent learning for smart reconfigurable THz MIMO-NOMA networks[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(1): 259-275. 10.1109/jsac.2021.3126079 |
22 | HEIDARI A A, MIRJALILI S, FARIS H, et al. Harris hawks optimization: algorithm and applications[J]. Future Generation Computer Systems, 2019, 97: 849-872. 10.1016/j.future.2019.02.028 |
23 | 郑洁锋,占红武,黄巍,等. Lévy Flight的发展和智能优化算法中的应用综述[J].计算机科学,2021,48(2):190-206. 10.11896/jsjkx.200500142 |
ZHENG J F, ZHAN H W, HUANG W, et al. Development of Lévy Flight and its application in intelligent optimization algorithm[J]. Computer Science, 2021, 48(2): 190-206. 10.11896/jsjkx.200500142 | |
24 | 邱锡鹏.神经网络与深度学习[M].北京:机械工业出版社, 2020: 81-104. 10.1016/b978-0-12-818803-3.00030-1 |
QIU X P. Neural Networks and Deep Learning[M]. Beijing: China Machine Press, 2020: 81-104. 10.1016/b978-0-12-818803-3.00030-1 | |
25 | O’SHEA T, HOYDIS J. An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(4): 563-575. 10.1109/tccn.2017.2758370 |
26 | LIU X, WANG J, ZHAO N, et al. Placement and power allocation for NOMA-UAV networks[J]. IEEE Wireless Communications Letters, 2019, 8(3): 965-968. 10.1109/lwc.2019.2904034 |
27 | LIU R, LI M, YU G, et al. User association for millimeter-wave networks: a machine learning approach[J]. IEEE Transactions on Communications, 2020, 68(7): 4162-4174. 10.1109/tcomm.2020.2983036 |
28 | 唐睿,何祖涵,张睿智,等.D2D-NOMA系统中混合离线-在线资源分配机制[J].信息与控制,2023, 52(5): 574-587. |
TANG R, HE Z H, ZHANG R Z, et al. Hybrid offline-online resource allocation mechanism for D2D-NOMA systems[J]. Information and Control, 2023, 52(5): 574-587. | |
29 | WU Q, XU J, ZENG Y, et al. A comprehensive overview on 5G-and-beyond networks with UAVs: from communications to sensing and intelligence[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(10): 2912-2945. 10.1109/jsac.2021.3088681 |
30 | 马礼智,唐睿,张睿智,等.基于无线能量传输的物联网数据采集系统中资源分配机制的设计[J].信息与控制, 2023, 52(2): 220-234. 10.13976/j.cnki.xk.2023.2034 |
MA L Z, TANG R, ZHANG R Z, et al. Design of resource allocation mechanisms for wireless power transfer-based internet-of-things data collection system[J]. Information and Control, 2023, 52(2): 220-234. 10.13976/j.cnki.xk.2023.2034 |
[1] | Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion [J]. Journal of Computer Applications, 2024, 44(9): 2696-2703. |
[2] | Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974. |
[3] | Xiyuan WANG, Zhancheng ZHANG, Shaokang XU, Baocheng ZHANG, Xiaoqing LUO, Fuyuan HU. Unsupervised cross-domain transfer network for 3D/2D registration in surgical navigation [J]. Journal of Computer Applications, 2024, 44(9): 2911-2918. |
[4] | Yexin PAN, Zhe YANG. Optimization model for small object detection based on multi-level feature bidirectional fusion [J]. Journal of Computer Applications, 2024, 44(9): 2871-2877. |
[5] | Yunchuan HUANG, Yongquan JIANG, Juntao HUANG, Yan YANG. Molecular toxicity prediction based on meta graph isomorphism network [J]. Journal of Computer Applications, 2024, 44(9): 2964-2969. |
[6] | Yuhan LIU, Genlin JI, Hongping ZHANG. Video pedestrian anomaly detection method based on skeleton graph and mixed attention [J]. Journal of Computer Applications, 2024, 44(8): 2551-2557. |
[7] | Yanjie GU, Yingjun ZHANG, Xiaoqian LIU, Wei ZHOU, Wei SUN. Traffic flow forecasting via spatial-temporal multi-graph fusion [J]. Journal of Computer Applications, 2024, 44(8): 2618-2625. |
[8] | Qianhong SHI, Yan YANG, Yongquan JIANG, Xiaocao OUYANG, Wubo FAN, Qiang CHEN, Tao JIANG, Yuan LI. Multi-granularity abrupt change fitting network for air quality prediction [J]. Journal of Computer Applications, 2024, 44(8): 2643-2650. |
[9] | Zheng WU, Zhiyou CHENG, Zhentian WANG, Chuanjian WANG, Sheng WANG, Hui XU. Deep learning-based classification of head movement amplitude during patient anaesthesia resuscitation [J]. Journal of Computer Applications, 2024, 44(7): 2258-2263. |
[10] | Huanhuan LI, Tianqiang HUANG, Xuemei DING, Haifeng LUO, Liqing HUANG. Public traffic demand prediction based on multi-scale spatial-temporal graph convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2065-2072. |
[11] | Zhi ZHANG, Xin LI, Naifu YE, Kaixi HU. DKP: defending against model stealing attacks based on dark knowledge protection [J]. Journal of Computer Applications, 2024, 44(7): 2080-2086. |
[12] | Yiqun ZHAO, Zhiyu ZHANG, Xue DONG. Anisotropic travel time computation method based on dense residual connection physical information neural networks [J]. Journal of Computer Applications, 2024, 44(7): 2310-2318. |
[13] | Song XU, Wenbo ZHANG, Yifan WANG. Lightweight video salient object detection network based on spatiotemporal information [J]. Journal of Computer Applications, 2024, 44(7): 2192-2199. |
[14] | Xun SUN, Ruifeng FENG, Yanru CHEN. Monocular 3D object detection method integrating depth and instance segmentation [J]. Journal of Computer Applications, 2024, 44(7): 2208-2215. |
[15] | Yaxing BING, Yangping WANG, Jiu YONG, Haomou BAI. Six degrees of freedom object pose estimation algorithm based on filter learning network [J]. Journal of Computer Applications, 2024, 44(6): 1920-1926. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||