Beam Training Optimization Based On Mm Wave Channel Measurement And Modelling

Millimeter wave,one of the key technologies in 5G networks,can provide sufficient spectrum resources to meet the increasing demand of data rate.How-ever,due to its large path loss,it is necessary for base station to deploy large-scale antenna array and utilize beamforming to compensate for the propagation loss,which requires base station and user equipment can quickly and accurately find the optimal beam pair for data transmission.The existing beam training schemes are mainly used in WPAN and WLAN,i.e.,hierarchical beam training,which send training sequences in all directions thereby obtaining the optimal beam pair.Although this method is simple and easy to operate,it costs huge time overhead during the training phase because of searching the full space.To this end,the hierarchical method can not meet the demand of low latency in 5G networks.Therefore,we propose a semi-offline hierarchical beam training method leveraging the channel measurement and modelling.The main work of this article can be summarized as follows:1.In order to obtain the channel data,we learn how to use the Keysight channel sounder and measuring instrument.In addition,we make a plan of outdoor measurement and construct the LOS and NLOS scenarios as well as system parameters.2.According to the plan of channel measurement,we implement the 39 GHz field test.After obtaining the real data obtained by the channel measure-ment platform,the channel impulse response is calculated and channel charac-teristic parameters are extracted.The data processing method is used to obtain the power delay profile,the power angular profile and the power delay angular profile in LOS scenario and the power delay angular profile in NLOS scenario.Besides,path loss,delay extension and angle extension are modeled according to the calculation results.3.Some key channel data are obtained through channel measurement,and then used as prior information to optimize the traditional hierarchical train-ing method.Then,the advantages and disadvantages of the exhaustive method and the traditional method are summarized,and the advantages of the proposed training method are verified through simulation.In order to further elaborate the feasibility of the proposed method,this thesis introduces Software Defined Network and Mobile Edge Computing systems,and uses Poisson Point Pro-cess to make a statistical analysis of outdoor cell scenarios.Simulation results prove that the semi-offline beam training method can reduce system latency and improve network throughput.