Identification,Parameter Estimation And Bitstream Extraction Of New Generation Wireless LAN Signal

With the continuous development of wireless local area network(WLAN)technology,the density of wireless access points and user density has also gradually increased.In a WLAN environment with a large number of users,the security of communication is crucial.Common WLAN communication security measures mainly rely on encryption algorithms,protocols,and firewalls at the data link layer,network layer,and transport layer.However,attacks such as rogue access point(AP)spoofing based on the physical layer can bypass these upper-layer security measures to some extent.Therefore,security authentication techniques for the physical layer of WLAN have become important.The fusion of two-dimensional information based on signal radio frequency(RF)fingerprints and MAC addresses can achieve security authentication for physical layer communication.This article focuses on the new generation WLAN communication standard,IEEE802.11 ax,and analyzes the technical aspects and frame structure of the newly introduced protocols.It introduces the key processes for obtaining MAC address information during the traditional Wi-Fi signal reception,including packet detection,symbol synchronization,frequency offset estimation,frame format recognition,and demodulation-decoding processes.Deep learning techniques are introduced to improve or optimize some of these processes to enhance performance.Finally,based on collected data from real environments,the extracted MAC address information is combined with RF fingerprint recognition results to complete the secure authentication of physical layer access devices.The main contributions of this article are as follows:Optimization and improvement of the synchronization and recognition algorithms in the process of obtaining MAC address information.Deep learning methods are introduced to enhance the performance of time parameter estimation algorithms,simplify the process of frequency offset estimation algorithms,and improve the accuracy of frame format recognition results.Simulation results show that the improved algorithms significantly enhance the recovery of packet information,especially the acquisition of MAC addresses.Implementation of RF fingerprint-based identification for transmitting devices.This article uses a convolutional neural network to automatically extract features from RF information such as phase noise and nonlinear distortion introduced by transmitting devices.Through simulation verification under static channel conditions,the network can accurately distinguish devices.Testing with real-world data shows that the identification accuracy of transmitting devices can reach above 96Fusion of MAC address and RF fingerprint features under non-cooperative conditions to achieve physical layer security authentication during user connection to wireless access points.In the process of extracting MAC addresses and RF fingerprint features from wireless routers,this article only needs to receive and process data broadcasted by transmitting devices unilaterally,without needing to authenticate with the router network.Therefore,the physical layer authentication scheme provided in this article can be performed in a non-cooperative manner.Through simulation and testing,this scheme can effectively differentiate between legitimate devices,unknown devices,and rogue APs,ensuring that users can connect to secure wireless networks.