Research On Beamforming Training Technology Of Next Generation Millimeter Wave WLAN

With the development of wireless multimedia applications,traditional low-frequency wireless communication technologies have gradually become difficult to meet the needs of users.The introduction of millimeter waves has brought the already scarce spectrum resources back to life.Millimeter-wave wireless local area networks(Wireless Local Area Network,WLAN)have attracted the attention of scientific researchers due to their sufficient bandwidth and high transmission rate.But its physical characteristics reveal the difficulties in practical applications: serious path loss,limited coverage,and high design costs.In order to make up for the path loss in transmission,beamforming technology is widely used in millimeter wave WLAN,so it is crucial to design an efficient beam training method.How to improve its beamforming training performance is one of the current research hotspots.For this reason,this article focuses on IEEE 802.11 ad beamforming training technology.The main research contents are as follows:First,the key technology of the Medium Access Control(MAC)layer in the next generation millimeter wave WLAN is studied,and the beamforming training process of the millimeter wave WLAN is focused on.By studying the access mechanism and interaction process of the user's access channel in each phase,and comparing the beamforming training process in different phases,it is concluded that there is still room for improvement in the millimeter wave WLAN beamforming training protocol,which can be designed by adjusting the protocol parameters an efficient beamforming training method.Secondly,by studying the beamforming training protocol of millimeter wave WLAN,a theoretical analysis model based on a two-dimensional Markov chain is established.This model can describe the node in the associated beamforming training(Associated beamforming training,A-BFT)stage.The process of state transition and two-level back-off,and accurately calculate various performance indicators of beamforming training.The model takes the number of collisions and backoff time of the node as the state of the Markov chain.The state transition is determined by whether the node has completed the beamforming training.Through the analysis of the steady-state probability,the parameters that affect the performance of the node's beamforming training are obtained,and passed A large number of simulations verify the correctness of the theoretical analysis.Finally,in the context of high-density user scenarios,the severe collision of nodes during beamforming training in the A-BFT phase and the resulting degradation of system communication quality,a beamforming training method based on dynamic adjustment of time slots is proposed,which can improve Beamforming training performance in dense user scenarios,thereby improving system communication quality.This method reduces the sector sweep range and uses collision time slots to estimate the number of nodes,thereby dynamically adjusting time slots and setting state transition thresholds.The simulation performance verification proves that the designed method can significantly improve the beamforming training performance in a high-density user scenario and can effectively improve the communication quality of the system in this scenario.