Research On Channel Estimation Algorithm For Massive MIMO UAV Communication Systems

Limited by the deployment location and construction period of ground base stations,traditional cellular communication systems are often difficult to meet the needs of communication scenarios such as edge and hotspot areas.Therefore,the industry proposes unmanned aerial vehicle(UAV)aerial mobile base stations.These base stations not only expand the communication coverage area with the advantages of high-altitude deployment,but also can flexibly adjust the position to enhance the signal strength.However,the UAV communication systems have substantial path loss problems,so it is often combined with the massive Multiple-input Multiple-output(MIMO)technology to compensate for this defect.But this also introduces a huge complexity of channel estimation,which brings challenges to real-time reliable communication services.Therefore,this thesis constructs the channel models of a single UAV and the UAV swarm for edge and hotspot scenarios,respectively,and a low-complexity channel estimation algorithm is proposed.The main work of the thesis is as follows:1)This thesis reviews the research status of massive MIMO UAV communication system and its channel estimation.Firstly,the application scenarios,characteristics and research status of swarm positioning of UAV communication system are reviewed.Then,the Doppler Shift(DS)and Direction of Arrival(DOA)in massive MIMO channel estimation technologies under mobile scenarios are reviewed.Finally,the research status of massive MIMO UAV communication channel estimation is summarized and analyzed.The problems existing in the existing research and the feasible direction for the follow-up research are pointed out.2)In a single massive MIMO UAV with dense crowd communication scenarios,a joint DS and channel estimation algorithm based on compressed sensing is proposed for the problems of DS and highdimensional channel solution in the UAV communication system.In this thesis,the communication system based on Massive MIMO is modeled as the Saleh-Valenzuela channel model.The channel parameters are decomposed into DS,DOA and channel gain.The combined algorithm of Fourier Transform and phase rotation is used to obtain the initial result of DS.An optimization problem is established for the channel matrix with the purpose of minimizing the channel error.The errors of the reconstructed signal and the original signal are obtained by using compressed sensing.The suboptimal solution of the proposed optimization problem is realized.The joint estimation result of DS,DOA and channel gain is obtained.Then,the mean square error and Cramer-Rao Lower Bound(CRLB)of the joint estimation algorithm are derived.Simulation results show that the proposed algorithm can effectively improve the estimation accuracy.3)In the case of UAV swarm centralized communication with dense crowds,in view of the high-dimensional channel solution and relative subarray position between UAV swarm communication system,a DOA estimation algorithm based on polynomial rooting and an optimal searchbased subarray interval estimation algorithm based on optimized search are proposed.A channel model based on Saleh-Valenzuela is constructed according to the physical characteristics of the massive MIMO channel,and the channel parameters are decomposed into relative angle,DOA,subarray interval and channel gain.Firstly,the covariance matrix of the received signal is used to construct a angle matrix containing relative angle and DOA information.The relative angle is calculated with the help of the secondary user,and the DOA information is obtained by reducing the rank of the angle matrix by using polynomial rooting.Then,an optimization problem is established for the position matrix containing the information of the sub-array interval to minimize the delay.The linear structure of matrix is used to narrow the search range,and then the integer properties are used to search one by one to achieve the optimal solution of the proposed optimization problem and obtain the information of the subarray interval.Then the channel gain information is obtained using a small number of pilot resources.The theoretical performance bounds are obtained by deriving the CRLB of the subarray interval estimation algorithm.Finally,the accuracy and effectiveness of the proposed algorithm are verified by simulation.