Research On The Multi-Dimensional Matrix Signal Acquisition Technologies For Unmanned Device Cooperative Coding Systems

With the innovation and progress of science and technology,the status of unmanned devices in human production and life has gradually increased.The communication system based on the unmanned device has become a research hotspot in many fields.However,the increasing shortage of spectrum resources and the rapid growth of the information transmission scale are the difficulties to be solved urgently.In order to overcome these difficulties,technologies such as the cooperative communication have come into being and gained high attention in many industries.In the process of combining unmanned devices with these new technologies,it is very important to obtain the transmitted signals and channel state information(CSI)in the communication link.Since unmanned devices are in a moving state,the channel is time-varying in the information transmission process.However,traditional methods of signal acquisition mostly assume that the CSI of the system is in a static state,and the CSI can be obtained through pilot sequences,so as to estimate the symbols.This will reduce the spectral efficiency of the communication system,and it has limitations in practical scenarios.Multi-dimensional matrix-based signal acquisition methods can effectively improve the spectrum efficiency of the system without using or only using a small number of pilot sequences.At the same time,these kinds of methods combined with the time-varying channel are deeply discussed and studied,which expand the research depth and breadth of the signal acquisition technologies in the field of wireless communication.In unmanned cooperative coding systems,the acquisition of the CSI is quite challenging in practice.This thesis mainly studies the signal acquisition and channel estimation methods based on nested multi-dimensional matrix models,introduces the time-varying channel into the structure of the system model,and makes an in-depth study of two nested models composed of signals received by the destination node.Based on the decomposition characteristics of different multi-dimensional matrix models,novel and reliable receivers are designed.The main research of this thesis is as follows:(1)In the vehicle time-varying cooperative coding communication system,the vehicle in a driving state is used as the source node in the communication link,and sending symbols are encoded with the Alamouti code.The amplify and forward(AF)technology of the relay node is used to transmit the signals to the data terminal node.Therefore,the vehicles,relay and data terminal nodes constitute a dual-hop communication system.Based on a nested parallel factor(PARAFAC)-PARAFAC2(NPP2)model constructed by the data terminal,a semi-blind receiver based on the batch decomposition of eigenvalues(BADPEV)is proposed.It is divided into two stages to estimate the channel and symbol matrices.In stage 1,the pilot sequences are sent by the relay node and received by the data terminal node.At the data terminal node,the second-hop CSI can be estimated by the inverse operation.In stage 2,the signals received by the data terminal node are decomposed,which are sent by the vehicles.The first-hop time-varying CSI and symbols can be estimated based on the second-hop time-invariant CSI estimated by stage 1.Simulation results prove that the performance of the BADPEV receiver is better than the two-stage training(TST)receiver and a two-hop receiver proposed by other authors based on a nested model,and closer to the zero-forcing(ZF)receiver,which is the performance reference boundary.(2)In order to further study the time-varying channels in the communication system,a multi-process alternating least square(MALS)receiver based on a nested Tucker-2-PARAFAC(NT2P)model for the unmanned aerial vehicle(UAV)time-varying cooperative coding communication system is proposed.In this system,the UAV is used as a relay node.Because it is in flight,the CSI in the dual-hop communication link can be set as time-varying.Therefore,the signals received by the data processing centre can be constructed as an NT2 P model.Based on the unfoldings of the nested multi-dimensional matrix model,an iterative method is used to fit it,and the joint estimation of the channel and symbol matrices is carried out.Compared with the TST receiver and the two-hop receiver proposed by other authors,the receiver proposed in this thesis has better performance.Besides,the number of iterations is obviously less than that of the alternating least square(ALS)receiver.