Data-Driven Joint Estimation for Blind Signal Based on GA-PSO Algorithm

doi:10.12142/ZTECOM.201903010

ZTE Communications ›› 2019, Vol. 17 ›› Issue (3): 63-70.DOI: 10.12142/ZTECOM.201903010

• Research Paper • Previous Articles

Data-Driven Joint Estimation for Blind Signal Based on GA-PSO Algorithm

LIU Shen^1,^2,³, QIN Yuannian¹, LI Xiaofan², ZHAO Yubin³, XU Chengzhong⁴

1.Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
2.Shenzhen Institute of Radio Testing & Tech., Shenzhen, Guangdong 518000, China
3.Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518000, China
4.University of Macau, Macau SAR 999078, China

Received:2019-01-21 Online:2019-09-29 Published:2019-12-06
About author:LIU Shen received his B.S. from Zhengzhou University (ZZU), China in 2016. He received his M.S. in electronic and communication engineering from Guilin University of Electronic Technology, China in 2019. He studied and worked in Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences and Shenzhen Institute of Radio Testing & Tech, China from 2017 to 2018. He is currently an engineer in Monolithic Power System (MPS), China. His research interests include indoor localization and software defined radio when he was a student.|QIN Yuannian received his B.S. from University of Electronic Science and Technology, China in 1994. In 2005, he received the title of senior experimental teacher. He is currently employed as a professor. He is the director of the Communication Experimental Center. The Communication Experimental Center has been awarded the title of the Experimental Teaching Demonstration Center of Guangxi Universities. As the main participant, he participated in 6 provincial and ministerial scientific research projects, presided over one Guangxi Natural Fund project, one Guangxi science and technology development project, three horizontal scientific research projects, 14 horizontal scientific research projects as the main personnel, published 22 papers, and participated in the compilation and publication of the textbook Mobile Communications.|LI Xiaofan received her B.S. and Ph.D. degrees from Beijing University of Posts and Telecommunication, China in 2007 and 2012. From 2010 to 2011, she studied in University of Washington, USA as an exchanged Ph.D. student. She joined the State Radio Monitoring Center and Testing Center (SRTC) in 2012 and was transferred to SRTC Shenzhen Lab from 2013. She is now an associate professor in Jinan University, Zhuhai, China. Her research interests include interference analysis among different radio systems, testing and evaluation methods for innovative radio technologies, cooperative communication, cognitive radio, internet of things, radio management strategy, etc.|ZHAO Yubin received his B.S. and M.S. from Beijing University of Posts and Telecommunications (BUPT), China in 2007 and 2010 respectively. He received his Ph.D. degree in computer science in 2014 from Freie Universiteat Berlin (FU Berlin), Germany. He has been an assistant professor in Center for Cloud Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China, since 2014. He serves as the reviewers for many scientific journals and session chairs for several conferences. He has also co-organized the workshop in ICCC 2019. His current research interests include indoor localization and target tracking, wireless power transfer and mobile edge computing.|XU Chengzhong received the Ph.D. degree from the University of Hong Kong in 1993. He is a full professor in the Department of Computer and Information Science, Faculty of Science and Technology, State Key Laboratory of IoT for Smart City, University of Macau, China. His research interests include networked computing systems and applications. He is an IEEE Fellow due to contribution in resource management in parallel and distributed computing.

Abstract

Abstract:

Without any prior information about related wireless transmitting nodes, joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task. Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources. In this paper, we propose a particle swarm optimization to estimate multiple co-frequency “blind” source nodes, which is based on the received power data measured by the sensors. To distract the mix signals precisely, a genetic algorithm is applied, and it further improves the estimation performance of the system. The simulation results show the efficiency of the proposed algorithm.

Key words: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), spatially distributed sensor, blind signal detection

LIU Shen, QIN Yuannian, LI Xiaofan, ZHAO Yubin, XU Chengzhong. Data-Driven Joint Estimation for Blind Signal Based on GA-PSO Algorithm[J]. ZTE Communications, 2019, 17(3): 63-70.

Figures/Tables 6

Figure 1. M transmitters (question marks) and N sensors placed within a certain square area.

Figure 2. The coordinate estimates of unknown node 1 are combined into one gene. The coordinate estimates of unknown node 2 are combined into one gene. Their respective power estimates are separated into a single gene.

Figure 3. Particle gene exchange and genetic mutation process of two unknown nodes.

Figure 4. Mean estimation error distance of all transmitters.

Figure 5. Mean estimation error distance of each transmitter.

Figure 6. Mean estimation error power of each transmitter.

References 20

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Data-Driven Joint Estimation for Blind Signal Based on GA-PSO Algorithm

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