基于可见光和射频融合的通信定位一体化系统

doi:10.11959/j.issn.1000-436x.2023230

通信学报 ›› 2023, Vol. 44 ›› Issue (12): 146-157.doi: 10.11959/j.issn.1000-436x.2023230

• 学术论文 • 上一篇

基于可见光和射频融合的通信定位一体化系统

杨彦兵¹^,², 胡超¹, 鲁邦彦¹, 张频频¹, 孙奕髦¹, 陈冠宇³

¹ 四川大学计算机学院，四川成都 610065
² 四川大学工业互联网研究院，四川成都 610065
³ 深圳华创芯光科技有限公司，广东深圳 518118

修回日期:2023-11-08 出版日期:2023-12-01 发布日期:2023-12-01
作者简介:杨彦兵（1987- ），男，四川绵阳人，博士，四川大学副研究员，主要研究方向为可见光通感一体化、智能感知与通信、嵌入式人工智能
胡超（2000- ），男，河南鹤壁人，四川大学硕士生，主要研究方向为无线网络、Wi-Fi感知、可见光通信
鲁邦彦（2000- ），男，山东济南人，四川大学硕士生，主要研究方向为可见光通信、嵌入式Linux、无线网络
张频频（1996- ），女，河南南阳人，四川大学博士生，主要研究方向为可见光通信、相机光通信等
孙奕髦（1990- ），男，四川德阳人，博士，四川大学特聘副研究员，主要研究方向为物联网、统计信号处理、目标定位、参数估计等
陈冠宇（1987- ），男，陕西宝鸡人，深圳华创芯光科技有限公司总经理，主要研究方向为可见光通信与光计算专业产业化、载波通信等
基金资助:
国家自然科学基金资助项目(61902267);国家自然科学基金资助项目(62272329);国家自然科学基金资助项目(62101359)

Integrated communication and positioning system enabled by fusing visible light and RF

Yanbing YANG¹^,², Chao HU¹, Bangyan LU¹, Pinpin ZHANG¹, Yimao SUN¹, Guanyu CHEN³

¹ College of Computer Science and Technology, Sichuan University, Chengdu 610065, China
² Industrial Internet Research Institute, Sichuan University, Chengdu 610065, China
³ Shenzhen HCCL Co.,Ltd., Shenzhen 518118, China

Revised:2023-11-08 Online:2023-12-01 Published:2023-12-01
Supported by:
The National Natural Science Foundation of China(61902267);The National Natural Science Foundation of China(62272329);The National Natural Science Foundation of China(62101359)

摘要/Abstract

摘要：

针对在可见光和射频融合网络中，传统的基于接收信号强度的用户切换方案未考虑用户切换错误率的问题，提出了基于位置的用户切换方案。具体地，设计并实现了一套基于低成本物联网芯片ESP32的可见光-射频融合的通信定位一体化系统，该系统使用可见光通信下行数据、Wi-Fi上行数据。设计了基于时-频复用的通信定位一体化框架，使用神经网络估计用户的位置，用户根据估计的位置完成切换。在通信方面，系统的吞吐量最高可达35 kbit/s；在定位方面，系统的定位精度可达2.8 cm，比传统系统的定位精度提升了33%。与基于接收信号强度的切换方案相比，所提方案的用户切换错误率降低了4%。

关键词: 可见光通信, 融合通信系统, 神经网络, 通信定位一体化, 用户切换

Abstract:

Aiming at the problem that in the hybrid visible light and RF network, such as the traditional user switching scheme leveraging received signal strength and ignoring the user switching error rate, a location-based user switching scheme was proposed.Specifically, an integrated communication and positioning system built upon low-cost IoT chip ESP32 was designed, which could fuse both visible light communication downlink and Wi-Fi uplink signals for accurate user location.A communication and positioning integration framework utilizing time-frequency multiplexing was designed, which used a neural network to estimate the user’s position, and the user completed the switching based on the estimated position.In terms of communication, the throughput of the system can reach up to 35 kbit/s.In terms of positioning, the positioning accuracy of the system can reach 2.8 cm, making a 33% improvement over traditional system.Compared with the switching scheme relying on received signal strength, user switching error rate of the proposed scheme is reduced by 4%.

Key words: visible light communication, integrated communication system, neural network, integrated communication and positioning, user switching

中图分类号:

TN914
TP393

杨彦兵, 胡超, 鲁邦彦, 张频频, 孙奕髦, 陈冠宇. 基于可见光和射频融合的通信定位一体化系统[J]. 通信学报, 2023, 44(12): 146-157.

Yanbing YANG, Chao HU, Bangyan LU, Pinpin ZHANG, Yimao SUN, Guanyu CHEN. Integrated communication and positioning system enabled by fusing visible light and RF[J]. Journal on Communications, 2023, 44(12): 146-157.

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参考文献 21

[1]	唐维红, 唐胜宏, 刘志华 . 移动互联网蓝皮书：中国移动互联网发展报告（2022）[M]. 北京: 社会科学文献出版社, 2022.
	TANG W H , TANG S H , LIU Z H . Mobile Internet blue book:China mobile Internet development report (2022)[M]. Beijing: Social Science Academic Press, 2022.
[2]	工信部运行监测协调局. 2022年通信业统计公报[J]. 通信企业管理, 2023(2): 8-12.
	Bureau of Operation Monitoring and Coordination,Ministry of Industry and Information Technology. 2022 Telecommunications industry statistical report[J]. C-Enterprise Management, 2023(2): 8-12.
[3]	802.802.11 Wireless LAN Working Group. 11 Wireless LAN Working Group.IEEE Standard for wireless LAN medium access control (MAC) and physical layer (PHY) specification[S]. 2023.
[4]	LAN/WAN Standards Committee of the IEEE Computer Society. IEEE standard for information technology-telecommunications and information exchange between systems local and metropolitan area networks-specific requirements part 11:wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 1:enhancements for high-efficiency WLAN:IEEE 802.11ax-2021[S]. 2021.
[5]	BESJEDICA T , FERTALJ K , LIPOVAC V ,et al. Evolution of hybrid Li-Fi-Wi-Fi networks:a survey[J]. Sensors (Basel,Switzerland), 2023,23(9): 4252.
[6]	ALI Q I , ASGHAR S . A systematic review of the IEEE-802.11 standard’s enhancements and limitations[J]. Wireless Personal Communications, 2023,131(4): 2539-2572.
[7]	IEEE draft standard for information technology:telecommunications and information exchange between systems:local and metropolitan area networks:specific requirements:part 11:wireless LAN medium access control (MAC) and physical layer (PHY) specifications:amendment 3:3650-3700 MHz operation in USA[S]. 2008.
[8]	HU J H , HU F C , JIA J L ,et al. 46.4 Gbit/s visible light communication system utilizing a compact tricolor laser transmitter[J]. Optics Express, 2022,30(3): 4365-4373.
[9]	LIU X , NA Z Y , WANG Y Y ,et al. Joint resource allocation for a novel OFDM-based multicolor VLC network[J]. IEEE Networking Letters, 2021,3(3): 100-104.
[10]	TORRES-ZAPATA E , GUERRA V , RABADAN J ,et al. VLC network design for high mobility users in urban tunnels[J]. Sensors, 2021,22(1): 88.
[11]	BEYSENS J , WANG Q , GALISTEO A ,et al. A cell-free networking system with visible light[J]. IEEE/ACM Transactions on Networking, 2020,28(2): 461-476.
[12]	HSU L S , TSAI D C , CHOW C W ,et al. Using data pre-processing and convolutional neural network (CNN) to mitigate light deficient regions in visible light positioning (VLP) systems[J]. Journal of Lightwave Technology, 2022,40(17): 5894-5900.
[13]	PERGOLONI S , BIAGI M , COLONNESE S ,et al. Optimized LEDs footprinting for indoor visible light communication networks[J]. IEEE Photonics Technology Letters, 2016,28(4): 532-535.
[14]	PURWITA A A , SOLTANI M D , SAFARI M ,et al. Handover probability of hybrid Li-Fi/RF-based networks with randomly-oriented devices[C]// Proceedings of 2018 IEEE 87th Vehicular Technology Conference (VTC Spring). Piscataway:IEEE Press, 2018: 1-5.
[15]	PARAMITA S , SRIVASTAVA A , BOHARA V A ,et al. Demo of hybrid Li-Fi/Wi-Fi network for an indoor environment[C]// Proceedings of 2023 15th International Conference on Communication Systems ＆Networks (COMSNETS). Piscataway:IEEE Press, 2023: 213-215.
[16]	ZHOU H Y , ZHANG M L , REN X M . Design and implementation of wireless optical access system for VLC-IoT networks[J]. Journal of Lightwave Technology, 2023,41(8): 2369-2380.
[17]	LEE H , LEE J . Convolutional model with a time series feature based on RSSI analysis with the Markov transition field for enhancement of location recognition[J]. Sensors, 2023,23(7): 3453.
[18]	IOFFE S , SZEGEDY C . Batch normalization:accelerating deep network training by reducing internal covariate shift[C]// Proceedings of the 32nd International Conference on International Conference on Machine Learning. New York:ACM Press, 2015: 448-456.
[19]	HE K M , SUN J . Convolutional neural networks at constrained time cost[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2015: 5353-5360.
[20]	BASTIAENS S , RAES W , STEVENS N ,et al. Impact of a photodiode’s angular characteristics on RSS-based VLP accuracy[J]. IEEE Access, 2020,8: 83116-83130.
[21]	BEYSENS J , WANG Q , ABEELE M V D ,et al. BlendVLC:a cell-free VLC network architecture empowered by beamspot blending[C]// Proceedings of IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2021: 1-10.

参数	值
LED灯功率/W	20
LED光束角	120°
LED间距/m	1.8
LED高度/m	2.7
PD接收面积/mm²	3.7
数据发送速率/(bit·s^-1)	115 200
定位信号长度/ms	2
定位信号频率/kHz	130,145,160,175
ADC采样率/sps	500

基于可见光和射频融合的通信定位一体化系统

Integrated communication and positioning system enabled by fusing visible light and RF

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