Research On Synchronous Signal Detection Algorithm For Vehicle Networking

The Internet of Vehicles is an interactive network composed of vehicle location,speed,route and other information.With the development of communication technology,an integrated network based on in-vehicle network(in-vehicle LAN),workshop network(vehicle-to-vehicle)and in-vehicle Internet(vehicle-to-vehicle Ethernet connection)has been formed.According to the agreed communication protocol and data interaction standard,the integrated network has built a system network for communication and information exchange between V2X(vehicle-to-vehicle,road,pedestrian and Internet),which is used to support the realization of intelligent transportation Intelligent vehicle and intelligent driving information transmission.For the inter-vehicle communication,the data collected by the vehicle can be transmitted in the physical layer,and the synchronization technology is a key technology in the physical layer algorithm.The V2 X communication system of the Internet of Vehicles is sensitive to synchronization errors during data transmission.The synchronization errors will cause the reduction of the system’s signal-to-noise ratio and have a serious impact on the system’s performance.Therefore,the synchronization technology of the V2 X communication system of the Internet of Vehicles needs to be studied urgently.Based on the background of 5G Internet of Vehicles,aiming at the low synchronization accuracy of the V2 X communication system of the Internet of Vehicles in the high carrier frequency,low signal-to-noise ratio and high-speed mobile environment,this paper takes the5G-NR-V2 X communication system as the research object to study the relevant problems in the synchronization process.On the basis of common synchronization algorithms,according to the characteristics of the side-chain Sidelink synchronization signal and pilot in the NRV2 X system,an improved synchronization algorithm is proposed,To meet the requirements for synchronization performance under NR-V2 X system.The main work of this paper is as follows:(1)A joint estimation algorithm of integer carrier frequency offset(ICFO)and side-chain main synchronous signal(S-PSS)in NR-V2 X system and a joint estimation algorithm of segments are proposed.Taking advantage of the fact that S-PSS is two consecutive OFDM symbols in the time domain,after Fourier transform(FFT),the differential average correlation of S-PSS is used,and then the joint estimation of ICFO and SPSS and the joint estimation of segments are performed.Through simulation analysis and comparison of the complexity and detection performance of the two algorithms,the segmented joint estimation algorithm achieves the expected target detection performance while reducing the complexity compared with the joint estimation method.It is suitable for use in the NR-V2 X system to improve the synchronization performance of the NR-V2 X system.(2)Improving the accuracy of Doppler frequency offset estimation in high-speed environment is an important research issue.Based on NR-V2 X and the characteristics of TCN neural network,this paper proposes a Doppler frequency offset estimation algorithm based on depth learning,and compares it with other frequency offset estimation algorithms.The simulation results show that the neural network constructed in this paper can estimate Doppler frequency offset accurately in high-speed mobile environment,and has obvious improvement compared with other algorithms,which can adapt to high-speed environment well.