Research On Multidimensional Matrix Based Cluster UAV Signal Receiving Technology

In wireless communication system,the channel state information(CSI)is very important to design an efficient communication system and evaluate its performance.The multidimensional matrix model is a kind of mathematical model which can be used for signal processing.In addition,the multidimensional matrix model combined with the blind estimation signal processing method can realize the joint estimation of channel and symbol with little or no pilot sequence.At present,the use of multidimensional matrix model for signal processing has become a research hotspot in the field of wireless communication systems.The application of UAV is a rapidly developing research field.CSI is critical to designing an efficient communications system and evaluating its performance.Aiming at the challenging problems of high mobility,unstable connection,high pilot frequency overhead and high real-time requirement in the communication process of cluster UAV,this paper studies the joint signal estimation and symbol detection technology based on multidimensional matrix.The specific method is as follows: the received signal is modeled as a multidimensional matrix related to the number of receiving and sending antennas,encoding length and time frame length.A semi-blind channel estimation method based on nested Parallel Factor(PARAFAC)model and PARAFACTUCK2(PARAFAC Tuck2)is proposed under the condition of its uniqueness decomposition.Based on the model,the receiving algorithm is used to process the received signals respectively.Finally,the proposed receiver algorithm is verified by numerical simulation with the derived receiver algorithm.In addition,the identification conditions of the model are analyzed and the value ranges of each parameter are derived.In order to realize the accurate estimation of symbols in UAV communication system,column ambiguity should not exist in the low-rank decomposition results of multidimensional matrix model.The main works of this paper are as follows:(1)To solve the problem of symbol transmission of cluster UAV,this paper uses MKRST coding scheme to integrate and precode multiple UAV signals and send them.Relay node uses the corresponding protocol to process and forward the precoded signals to the destination node.The signal received by the destination node is a multidimensional matrix related to the encoding length,the number of antennas and the time frame.The multi-dimensional matrix model constructed by the destination node is used to estimate the channel matrix and signal matrix.The signal was decoded by SVD algorithm for many times to obtain the symbol matrix of each uav.The combination of coding and spatial diversity technology opens a new dimension for UAV wireless communication and provides an effective solution for wireless communication channel.(2)For Amplify-and-Forward(AF)MIMO relay system,a nested PARATUCK2 model is studied in this paper.By constructing the received signal as a multidimensional matrix,the blind estimation method is used to fit the constructed model and solve the problem under the condition of the uniqueness decomposition.The simulation results show that the proposed receiver is close to the ideal ZF receiver in the estimation error of sign matrix,and the estimation performance is better than the TST receiver based on training sequence,which proves the effectiveness of the proposed receiver.(3)Based on the previous research,the effects of different relay protocols on receiver estimation performance in three-hop wireless communication system are considered.The performance of the corresponding receiver is analyzed by using the combined action of multiple relay protocols.These protocols include AF protocol,DF protocol and SDF protocol.The multi-dimensional matrix models constructed are nested PARATUCK2 model,PARATUCK2 model,nested PARAFAC model and PARAFAC model,respectively.The simulation results show that the bit error rate of the four receivers is similar and their performance is better than that of TST receivers.However,due to the results of different relay functions,the error values of the four receivers are different for different channels,and the analysis shows that PTALS receivers have better performance in channel estimation.