Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250400020-6.doi: 10.11896/jsjkx.250400020

• Network & Communication • Previous Articles     Next Articles

Power Regulation Backscatter Communication Sensing System for Distributed Photovoltaic Power Generation Systems

PENG Linyu1, WANG Tao1, LONG Jiao1, CAO Zhongye2, WU Yijie2, WANG Wei2   

  1. 1 Power Dispatch Control Center,Guizhou Power Grid Co.Ltd.,Guiyang 550000,China
    2 School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan 430074,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:PENG Linyu,born in 1993,postgraduate.Her main research interests include power communication and data science.
    WANG Wei,born in 1988,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.I5416M).His main research interests include PHY/MAC design and mobile computing in wireless systems.
  • Supported by:
    National Natural Science Foundation of China(62471194) and Science and Technology Project of Guizhou Power Grid Co., Ltd.(060000KC24100003).

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Abstract: Distributed photovoltaic(PV) systems,characterized by their flexible deployment and wide distribution,align with the development trends of power system intelligence and efficient utilization of renewable energy.However,managing such systems not only requires real-time acquisition of operational status and environmental parameters of PV components but also precises localization of data collection terminals to support fault diagnosis,resource optimization,and efficiency enhancement.This imposes stringent demands on terminal energy consumption,large-scale access communication capabilities,and sensing and localization abilities.Existing technologies struggle to simultaneously meet the collaborative requirements of low-power communication,large-scale terminal access,and high-precision localization,leading to significant challenges in the practical application of distributed PV systems.To address these needs,this paper proposes a low-power backscatter communication and sensing system based on power-controlled non-orthogonal multiple access(NOMA).The system faces the following challenges during implementation.Firstly,existing backscatter communication technologies lack sufficient precision in power control,making it difficult to adapt to the dynamically changing power allocation requirements in distributed PV scenarios.Secondly,spatial distribution differences reduce the effectiveness of power control,affecting overall system performance.Finally,severe concurrent signal interference during multi-tag localization limits positioning accuracy.To tackle these challenges,this paper designs a reflected power control scheme based on tunnel diodes,achieving fine-tuning of signal power;proposes a dynamic power control strategy to optimize the overall bit error rate(BER) of the system,and combines angle-of-arrival(AoA) information embedded in aliased signals with the power domain characteristics of NOMA to design a multi-tag localization scheme,enabling simultaneous multi-tag communication and position estimation.Experimental results show a BER below 0.01% at SNR>15 dB and a localization angle error under 5° at SNR>5 dB.

Key words: Backscatter communication, NOMA, Tunnel diode, Power regulation, Angle of arrival estimation

CLC Number: 

  • TN926
[1] CHEN Z Y,WANG T L.Multi agent evolutionary game and simulation research for promotion of distributed photovoltaics [J].Journal of Chongqing University of Technology(Natural Science),2022,36(12):297-304.
[2] MA X.Design of Distributed Photovoltaic Grid-Connected Access System Based on Power Communication [J].Telecom PowerTechnology,2024,41(17):1-3.
[3] LI Z X,PENG N,WANG X Y.Design and Application of Distributed Photovoltaic Monitoring System Based on Wireless Networking [J].Process Automation Instrumentation,2022,43(1):92-95,101.
[4] LI Y,ZHANG Y L,DING Y,et al.Research progress and evolution prospect of passive internet of things communication [J].Chinese Journal on Internet of Things,2023,7(3):15-23.
[5] ZHENG L M,LIU P G,WANG H Y,et al.Passive Internet of Things:Background,Concept,Challenges and Progress [J].Journal of Electronics & Information Technology,2023,45(7):2293-2310.
[6] YE Y H,XU R,TIAN Y J,et al.Research and development of backscatter communications technology [J].Telecommunications Science,2024,40(1):1-23.
[7] ZHANG X Q,XU Y J.Survey on backscatter communication for zero-power IoT [J].Journal on Communications,2022,43(11):199-212.
[8] ZHU F,ZHAO R,WANG B,et al.Enabling OFDMA in Wi-Fi Backscatter[J].IEEE/ACM Transactions on Networking,2024,32(1):427-444.
[9] HESSAR M,NAJAFI A,GOLLAKOTA S.NetScatter:Enab-ling large-scale backscatter networks[C]//Proceedings of the 16th USENIX Conference on Networked Systems Design and Implementation.Boston:USENIX Press,2019:271-284.
[10] REN Y,CAI P,JIANG J,et al.Prism:High-throughput LoRa backscatter with non-linear chirps[C]//IEEE INFOCOM 2023-IEEE Conference on Computer Communications.IEEE,2023:1-10.
[11] PENG Y,ZHANG Y,XIAO L,et al.Exploiting subcarrier redundancy for concurrent OFDM backscatter communication[J].IEEE Wireless Communications Letters,2023,12(5):828-832.
[12] LUO Z Q,LI W M,WU Y J,et al.Accurate Indoor Localization for Bluetooth Low Energy Backscatter[J].IEEE Internet of Things Journal,2024,12(2):1805-1816.
[13] XU Y,XU R,LI D,et al.Robust resource allocation for wireless-powered backscatter communication systems with NOMA[J].IEEE Transactions on Vehicular Technology,2023,72(9):12288-12299.
[14] CHENG Y F,TIAN H X,LIU Z J.Collaborative Optimization of Joint User Association and Power Control in NOMA Heterogeneous Network [J].Computer Science,2021,48(3):269-274.
[15] DONG H,XIE Y,ZHANG X,et al.Gpsmirror:Expanding accurate gps positioning to shadowed and indoor regions with backscatter[C]//Proceedings of the 29th Annual International Conference on Mobile Computing and Networking.2023:1-15.
[16] SHANG F,CHAMPAGNE B,PSAROMILIGKOS I N.A ML-based framework for joint TOA/AOA estimation of UWB pulses in dense multipath environments[J].IEEE Transactions on Wireless Communications,2014,13(10):5305-5318.
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