Millimeter Wave Phased Array Based Intelligent Beam Management Technology

Millimeter-wave communication systems often use phased array antennas and beamforming techniques to generate high-gain directional beams to compensate for severe path loss at millimeter-wave frequencies.Traditional beam management techniques typically use exhaustive beam search(EBS)or hierarchical beam search(HBS)to obtain optimal transmit and receive beam pairs.However,when the number of beams is large,both traditional methods often have a huge beam training overhead.In this context,this thesis studies the millimeter-wave phased array based intelligent beam management technology,taking advantage of the characteristic that the wide beams can cover larger angular space and the narrow beams have higher beamforming gain.Specifically,this thesis proposes two intelligent beam training algorithms: the wide-to-narrow beam mapping(WNBM)-based intelligent beam training algorithm and the beam mutual information and wide-to-narrow beam mapping(BMI-WNBM)-based intelligent beam training algorithm.The main work and innovation points are as follows:Firstly,to reduce the overhead,this thesis proposes the WNBM algorithm that uses partial wide beam search.The algorithm uniformly divides the entire wide beam space,and each region contains multiple wide beams with equal numbers.In each region,a wide beam,which is not adjacent to others,is selected to form the wide beam training set.This ensures a uniform exploration of the entire wide beam space and reduces the beam training overhead.Simulation results show that the WNBM algorithm outperforms the baseline algorithm in terms of beam alignment accuracy,normalized beamforming gain,and effective throughput.Secondly,in a blocking environment,to address the problem of poor robustness of the WNBM algorithm due to blocking possibly obstructing the wide beam direction selected by the algorithm,this thesis further proposes the BMI-WNBM algorithm.The algorithm first preprocesses the training data to calculate the mutual information between each wide beam and the optimal wide beam,which is called beam mutual information,and then selects the wide beam with the maximum mutual information value in each region to form the wide beam training set.In this way,the BMI-WNBM algorithm can effectively avoid the blocked direction when selecting partial wide beams,thereby enhancing robustness.Simulation results show that in a blocking environment,the BMI-WNBM algorithm outperforms the WNBM algorithm and the baseline algorithm in terms of beam alignment accuracy,normalized beamforming gain,and effective throughput.Finally,compared with the baseline algorithm,the two algorithms mentioned above can achieve low-overhead,high-gain and fast narrow beam alignment without continuous user history information or additional beam training by utilizing partial wide beam training.