Channel Estimation For Backscatter Communication Systems With Retrodirective Arrays

After the invention of computer and the Internet,the Internet of Things(IoT),which aims to connect everything,is considered as the third revolution of information industry.With the rapid development of the 5G and 6G network communication technology,traditional sensors cannot satisfy the requirements of actual scenarios.First,the sensors cannot work without the battery,which limits its service life and application scenarios.Besides,the sensors are too expensive to be deployed widely.Backscatter communication technology can effectively make up for the shortcomings of traditional sensors.Specifically,backscatter technology can lower the cost and power consumption of the sensors,which can help realize the vision of connecting everything.Meanwhile,the sensors can be communicated contactlessly.Recently,various new backscatter communication systems have been proposed.Retrodirective backscatter communication can improve the performance of the traditional backscatter communication system with the aid of retrodirective arrays.The accurate channel estimation is vital for the design of backscatter communication system.To our best knowledge,the channel estimation for the retrodirective backscatter system is still an open problem,which motivates our present work.We think two main challenges hinder the work:(1)The channel characteristics in retrodirective backscatter communication differ from the traditional backscatter system.(2)The prior probability of the channel parameter consists of special function.Therefore,we cannot derive the closed-form solution for channel parameter directly.This paper focuses on channel estimation and signal detection in two different backscatter communication systems:(1)In the traditional backscatter communication system,this paper first analyzes the characteristic properties of this channel by modeling this system.Then,this paper deduces the closed-form solution for the least square(LS)and linear minimum mean square(LMMSE)algorithm in this system.Besides,this paper also deduces the Bayesian Cramer-Rao Lower Bound(BCRB)of the channel parameter.Finally,this paper simulates to verify the performance of two classical estimation algorithms.(2)In the retrodirective backscatter communication system,this paper first proposes a more accurate estimator based on bayesian rules,and discusses the differences of the channel characteristics between the retrodirective backscatter and the traditional backscatter.Then,this paper analyzes the probability density function(PDF)of the uplink and downlink signal envelopes.Besides,this paper derives the closed-form solution for LS,LMMSE and the proposed bayesian estimator in this system.To verify whether the proposed estimator can perform well in signal detection,this paper applies the classical signal detection algorithm in this system.Finally,this paper derives the BCRB of the channel parameter and simulates to verify the effectiveness of the proposed estimator in channel estimation and signal detection.