Research On Non-Feedback Beamforming Based On Wi-Fi Backscatter

In recent years,with the rapid development and large-scale commercialization of new applications of the IOT such as smart cities and smart farms,many types of wireless signal backscatter communication such as Wi-Fi,FM(Frequency Modulation),etc which have low power consumption,low cost and broad application prospects,has quickly become a research hotspot in industry and academia,and the widespread deployment of Wi-Fi equipment also makes the research of backscatter communication technology based on WiFi irreplaceable.Wi-Fi backscatter communication has many unique advantages,but its short communication distance and low signal-to-noise ratio have become the bottleneck restricting its further development.Therefore,how to extend the communication distance and increase the signalto-noise ratio has become the main direction of system performance improvement.However,the existing work may need to provide a small amount of energy consumption for the tag,plus hardware or protocol,so this work aims to propose a beamforming system Wi Beam based on the Wi-Fi backscatter communication system without feedback mechanism,without modification Under the premise of the protocol and hardware equipment,the communication distance and signal-to-noise ratio between the transmitting end and the backscatter passive tag in the Wi-Fi backscatter system are improved.The main research contents are as follows:1.The accuracy of the positioning of the backscattering passive tag will directly affect the performance of the beamforming without feedback mechanism,and the positioning of the backscattering passive tag must first determine the exact position of the receiving end,if the receiving end is located in the "dead zone " of the transmitting end In the area,the positioning accuracy will be greatly reduced.To solve this problem,this paper proposes a KMUSIC algorithm that uses multiple transmitters to improve the positioning accuracy of the "dead zone " area.First,the multiple signal classification algorithm(MUSIC)is used to estimate the angle of arrival(AOA)between multiple transmitters and receivers.Then,the kernel density estimation statistical method is used to learn the phase difference distribution of the AOA estimates,and the confidence in the phase domain is selected.The sample with higher degree is converted to normalized phase angle to obtain the estimated angle and its corresponding confidence.Finally,the estimation result and confidence of multiple transmitters are used to determine the exact position of the receiver.This method locates the "dead zone" with weak positioning effect.Accuracy increased by 75%.2.Based only on the position information of the backscatter passive tags,the sender still cannot obtain the weight matrix necessary for the adaptive beamforming of the backscatter passive tags.For this problem,this paper implements and verifies a channel based on 802.11 n Modeled beamforming method without feedback mechanism to achieve beamforming without feedback mechanism between the sending end and the backscatter passive tag.First,the accurate position parameters of the backscattering passive tags are modeled through the 802.11 n wireless channel to obtain the downlink weight matrix between the sending end and the backscattering passive tags,and then the weighting matrix is obtained by modeling to realize the pairing of the sending end Beamforming of backscatter passive tags.The entire process improves the signal-to-noise ratio of the communication link between the sender and the backscatter passive tag in the Wi-Fi backscatter system without modifying the protocol and hardware equipment,and the communication distance is increased by about 1 Times.