Research On Beam Search Algorithm For 5G Millimeter Wave System

In the 5G mobile communication system,Millimeter Wave can provide greater bandwidth and the higher data transmission rates.Because of the transmission characteristics of Millimeter Wave(mm Wave),the base stations are deployed by using massive MIMO technology,which can transmit a large number of high-gain directional narrow beams to increase signal coverage.However,the narrow beams and the large number of beams in millimeter wave communication lead to difficulties in beam alignment and high beam search complexity.Especially in urban micro-cellular scenarios,users need to search large amounts of narrow beams from multiple base stations to find the optimal beam,which will will consume a lot of time and computing resources.How to design a beam search algorithm that enables users and base stations to quickly align to the best beam is a key issue that needs to be solved in mm Wave research.At present,machine learning,as a hot key technology,has been widely used in communication.In the research of modulation and demodulation,signal detection,and channel model,machine learning has excellent performance.Under the condition of a fixed beam codebook,the beam search problem can be regarded as a classification problem in supervised learning,and the beam performance is judged by the received beam information.Therefore,some research ideas on the problem of millimeter wave beam search and beam switching in user movement are proposed.Firstly,a system simulation model is designed based on the 5G channel scenario and beamforming.The system simulation model can provide data samples and simulation verification for subsequent algorithm design.Secondly,a beam search algorithm is designed based on machine learning.The main idea of the algorithm is to use the machine learning model to learn the beam and environment information,so as to make the classification prediction of the beam performance.Compared with the traditional beam search algorithm,this algorithm does not depend on the structure of the antenna array and the design of the beam codebook.It only needs to learn some easily available information in the communication scenario.Therefore,it has high generality.Simulation experiments show that the neural network model is effective in solving the beam search problem,and the screened beams can approach the performance of exhaustive search beams.Finally,acording to the problem of beam switching in user movement,a beam switching algorithm based on neural network is proposed.When the current beam performance of the user is lower than the threshold,a beam switching process is started.The algorithm performs beam search on the candidate beams and switches to the beam with the best performance.After simulation testing,this algorithm predicts candidate beams in advance through a neural network,which can shorten the time required for beam switching while ensuring the accuracy of the algorithm.