Optimal energy-efficiency beamforming design for SWIPT-enabled sink in sensor cloud based on deep learning

Zhe WANG; Taoshen LI; Lina GE; Guifen ZHANG; Min WU

doi:10.11959/j.issn.1000-436x.2021131

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Optimal energy-efficiency beamforming design for SWIPT-enabled sink in sensor cloud based on deep learning

Papers | 更新时间：2024-06-05

- Optimal energy-efficiency beamforming design for SWIPT-enabled sink in sensor cloud based on deep learning
- Journal on Communications Vol. 42, Issue 7, Pages: 176-188(2021)
- 作者机构：
  
  1. 广西民族大学人工智能学院，广西南宁 530006
  2. 广西混杂计算与集成电路设计分析重点实验室，广西南宁530006
  3. 广西民族大学网络通信工程重点实验室，广西南宁 530006
  4. 广西大学计算机与电子信息学院，广西南宁 530004
  5. 广西电网有限责任公司科技信息部，广西南宁 530023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61862007);The National Natural Science Foundation of China(62066005);Guangxi Natural Science Foundation(2020GXNSFBA297103);Scientific Research Ability Improving Foundation for Young and Middle-Aged University Teachers in Guangxi(2020KY04030);The School Introduces Talents to Start Scientific Research Projects(2019KJQD17)
- DOI：10.11959/j.issn.1000-436x.2021131
  CLC： TN92
- Online First：2021-07，
  
  Published：25 July 2021
- 稿件说明：
移动端阅览
Zhe WANG, Taoshen LI, Lina GE, et al. Optimal energy-efficiency beamforming design for SWIPT-enabled sink in sensor cloud based on deep learning[J]. Journal on Communications, 2021, 42(7): 176-188.
DOI：

Zhe WANG, Taoshen LI, Lina GE, et al. Optimal energy-efficiency beamforming design for SWIPT-enabled sink in sensor cloud based on deep learning[J]. Journal on Communications, 2021, 42(7): 176-188. DOI： 10.11959/j.issn.1000-436x.2021131.

摘要

为了解决传统基于最优化方法所设计的无线网络资源管理策略通常复杂度较高且实时性差，不利于在线决策制定的问题，针对基于SWIPT的传感云系统，建立汇聚（sink）节点能效最大化问题及其数学模型，然后引入深度学习方法，通过对最优化算法的学习实现更低复杂度与更高实时性的算法设计。为了实现深度学习算法在网络资源分配中的应用，首先将 sink 节点最优能效模型转化为高维可求解形式，设计具有迭代形式的SWIPT-WMMSE算法实现最优波束成形矢量的求解，同时证明了算法的收敛性。然后基于DNN逼近误差的传递过程推导了DNN设计准则，并通过对DNN的训练实现其对SWIPT-WMMSE算法的逼近。最后通过仿真实验分别验证了SWIPT-WMMSE算法与DNN算法的有效性，及DNN算法的逼近效果和在提升系统性能方面的优势。

Abstract

To solve the problems of high complexity and poor real-time performance caused by traditional wireless resource management based on optimization methods

the energy efficiency maximization problem of sink node and its mathematical model were established for SWIPT-enabled sensor-cloud system

then the deep learning method was introduced to realize the solving and online decision-making with lower complexity and higher real-time performance.The mathematical model was transformed into a high-dimensional solvable form

and then a SWIFT-WMMSE algorithm with iterated forms was designed to solve optimal beamforming vector.The convergence of SWIPT-WMMSE algorithm was proved.Then

based on error propagation of DNN approximation

the scale design criteria for the DNN was deduced

and the approximation was realized through DNN training.Finally

the simulation results verify the effectiveness of SWIPT-WMMSE and DNN algorithm

as well as the approximation effect of DNN and its system performance gains.

关键词

Keywords

references

ALAMRI A , ANSARI W S , HASSAN M M , et al . A survey on sensor-cloud:architecture,applications,and approaches [J ] . International Journal of Distributed Sensor Networks , 2013 , 9 ( 2 ): 1 - 18 .

王田 , 沈雪微 , 罗皓 , 等 . 基于雾计算的可信传感云研究进展 [J ] . 通信学报 , 2019 , 40 ( 3 ): 170 - 181 .

WANG T , SHEN X W , LUO H , et al . Research progress of trusted sensor-cloud based on fog computing [J ] . Journal on Communications , 2019 , 40 ( 3 ): 170 - 181 .

王哲 , 李陶深 , 葛丽娜 , 等 . 基于无线携能通信的传感云系统 sink节点最优能效策略 [J ] . 控制与决策,2019:doi.org/10.13195/j.kzyjc.2019.1628 .

WANG Z , LI T S , GE L N , et al . Optimal energy efficiency optimiza-tion strategy for SWIPT-enabled sensor-cloud System [J ] . Control and Decision,2019:doi.org/10.13195/j.kzyjc.2019.1628 .

CLERCKX B , ZHANG R , SCHOBER R , et al . Fundamentals of wireless information and power transfer:from RF energy harvester models to signal and system designs [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 1 ): 4 - 33 .

王哲 , 李陶深 , 叶进 , 等 . 基于非线性能量收集的多用户MIMO认知无线供电通讯网络 [J ] . 控制与决策 , 2020 , 35 ( 3 ): 547 - 558 .

WANG Z , LI T S , YE J , et al . Multi-user MIMO cognitive wireless powered communication network based on non-linear energy harvest-ing [J ] . Control and Decision , 2020 , 35 ( 3 ): 547 - 558 .

GREGOR K , LECUN Y . Learning fast approximations of sparse coding [C ] // Proceedings of the 27th International Conference on Machine Learning . Piscataway:IEEE Press , 2010 : 399 - 406 .

HERSHEY J R , ROUX J L , WENINGER F . Deep unfolding:model-based inspiration of novel deep architectures [J ] . arXiv Preprint,arXiv:1409.2574 , 2014 .

SPRECHMANN P , LITMAN R , YAKAR T B , et al . Supervised sparse analysis and synthesis operators [C ] // Advances in Neural Information Processing Systems . Massachusetts:MIT Press , 2013 : 908 - 916 .

SUN H , CHEN X , SHI Q , et al . Learning to optimize:training deep neural networks for interference management [C ] // IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications . Piscataway:IEEE Press , 2017 : 1 - 6 .

MEHRABI M , MOHAMMADKARIMI M , ARDAKANI M , et al . Decision directed channel estimation based on deep neural network$k$-step predictor for MIMO communications in 5G [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 11 ): 2443 - 2456 .

CUI W , SHEN K M , YU W . Spatial deep learning for wireless scheduling [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1248 - 1261 .

HE C F , HU Y , CHEN Y , et al . Joint power allocation and channel assignment for NOMA with deep reinforcement learning [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2200 - 2210 .

ORTIZ A , ASADI A , ENGELHARDT M , et al . CBMoS:combinatorial bandit learning for mode selection and resource allocation in D2D systems [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2225 - 2238 .

NASIR Y S , GUO D N . Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2239 - 2250 .

GARCIA V , ZHOU Y Q , SHI J L . Coordinated multipoint transmission in dense cellular networks with user-centric adaptive clustering [J ] . IEEE Transactions on Wireless Communications , 2014 , 13 ( 8 ): 4297 - 4308 .

LIU L , ZHOU Y Q , ZHUANG W H , et al . Tractable coverage analysis for hexagonal macrocell-based heterogeneous UDNs with adaptive interference-aware CoMP [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 1 ): 503 - 517 .

LIU L , ZHOU Y Q , GARCIA V , et al . Load aware joint CoMP clustering and inter-cell resource scheduling in heterogeneous ultra dense cellular networks [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 3 ): 2741 - 2755 .

JU H , ZHANG R . Throughput maximization in wireless powered communication networks [J ] . IEEE Transactions on Wireless Communications , 2014 , 13 ( 1 ): 418 - 428 .

GONG S Q , MA S D , XING C W , et al . Optimal beamforming and time allocation for partially wireless powered sensor networks with downlink SWIPT [J ] . IEEE Transactions on Signal Processing , 2019 , 67 ( 12 ): 3197 - 3212 .

LIU T , WANG X D , ZHENG L . A cooperative SWIPT scheme for wirelessly powered sensor networks [J ] . IEEE Transactions on Communications , 2017 , 65 ( 6 ): 2740 - 2752 .

ZHOU Y Q , LIU L , WANG L , et al . Service-aware 6G:an intelligent and open network based on the convergence of communication,computing and caching [J ] . Digital Communications and Networks , 2020 , 6 ( 3 ): 253 - 260 .

ZHOU Y Q , TIAN L , LIU L , et al . Fog computing enabled future mobile communication networks:a convergence of communication and computing [J ] . IEEE Communications Magazine , 2019 , 57 ( 5 ): 20 - 27 .

QI Y L , TIAN L , ZHOU Y Q , et al . Mobile edge computing-assisted admission control in vehicular networks:the convergence of communication and computation [J ] . IEEE Vehicular Technology Magazine , 2019 , 14 ( 1 ): 37 - 44 .

LYU X C , REN C S , NI W , et al . Optimal online data partitioning for geo-distributed machine learning in edge of wireless networks [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 10 ): 2393 - 2406 .

WANG S Q , TUOR T , SALONIDIS T , et al . Adaptive federated learning in resource constrained edge computing systems [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 6 ): 1205 - 1221 .

ZHANG R , HO C K . MIMO broadcasting for simultaneous wireless information and power transfer [J ] . IEEE Transactions on Wireless Communications , 2013 , 12 ( 5 ): 1989 - 2001 .

WIN M Z , WINTERS J H . Analysis of hybrid selection/maximal-ratio combining in Rayleigh fading [J ] . IEEE Transactions on Communications , 1999 , 47 ( 12 ): 1773 - 1776 .

王正强 , 蒋铃鸽 , 何晨 . 基于合作博弈的多信道认知无线网络中的频谱共享算法 [J ] . 通信学报 , 2014 , 35 ( 2 ): 70 - 75 .

WANG Z Q , JIANG L G , HE C . Spectrum sharing algorithm in mul-ti-channel cognitive radio network based on cooperative game theo-retic [J ] . Journal on Communications , 2014 , 35 ( 2 ): 70 - 75 .

SHI Q J , RAZAVIYAYN M , LUO Z Q , et al . An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 9 ): 4331 - 4340 .

王哲 , 李陶深 , 叶进 , 等 . 基于不确定理论的能量收集可靠性建模及规划 [J ] . 通信学报 , 2018 , 39 ( 5 ): 166 - 176 .

WANG Z , LI T S , YE J , et al . Reliability modeling and planning of energy harvesting based on uncertainty theory [J ] . Journal on Commu-nications , 2018 , 39 ( 5 ): 166 - 176 .

VERDU S . Multiuser detection [M ] . Cambridge : Cambridge University Press , 1998 .

SOLODOV M V . On the convergence of constrained parallel variable distribution algorithms [J ] . SIAM Journal on Optimization , 1998 , 8 ( 1 ): 187 - 196 .

KUMAR N S , KUMAR K . Performance analysis of m*n equalizer based minimum mean square error (MMSE) receiver for MIMO wireless channel [J ] . International Journal of Computer Applications , 2011 , 6 ( 9 ): 47 - 50 .

SUSSMANN H J . Optimal control [M ] . Berlin : Springer , 1998 .

HORNIK K , STINCHCOMBE M , WHITE H . Multilayer feed forward networks are universal approximators [J ] . Neural Networks , 1989 , 2 ( 5 ): 359 - 366 .

KONEČNÝ J , LIU J , RICHTÁRIK P , et al . Mini-batch semi-stochastic gradient descent in the proximal setting [J ] . IEEE Journal of Selected Topics in Signal Processing , 2015 , 10 ( 2 ): 242 - 255 .

LIAO W C , HONG M Y , LIU Y F , et al . Base station activation and linear transceiver design for optimal resource management in heterogeneous networks [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 15 ): 3939 - 3952 .

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