Joint Waveform Design Of Communication And Navigation And The Optimization Of Receive Method

With the widespread application of wireless sensor networks(WSNs),vehicular networks(VANETs)and unmanned aerial vehicle(UAV),and the vigorous development of wireless communication and navigation technologies,the demand for location-based information services is increasing.Nodes in the network need to communicate quickly and reliably with each other and achieve accurate timing and positioning.The joint design of communication and navigation has gradually become a major research trend.In fact,the two are similar in working principle and implementation method,so they have the foundation of joint design.In order to reduce the resource occupation,increase the frequency spectrum utilization,and improve the robustness of the communication navigation signal processing,new requirements for waveform design arises.Therefore,we consider the joint design of waveform for communication and navigation.Also we evaluate the performance of the proposed waveform.At the same time,due to the dynamic nature of the nodes in the network and the variability of the environment,the impact of time-varying fading channels on communication quality cannot be ignored,which places higher requirements on the optimization of the receiving method.In recent years,due to the strong nonlinear fitting ability of neural networks,more and more communication scholars have paid attention to it.Studies have shown that it has great potential in signal processing in the communication physical layer.This article combines neural networks with channel tracking and equalization techniques to optimize the receiving method.It can break out the limitations of traditional channel equalization methods in complex channel environment and achieve performance improvements.Specific research and innovative work include the following two parts:For joint design of communication and navigation waveform,this paper analyzes the generation methods,autocorrelation characteristics,spectrum characteristics,and code tracking performance of traditional navigation and positioning waveform.The increase of energy at high frequency helps to improve the tracking performance.Based on the above ideas,this paper proposes a waveform with flexible structure.This waveform realizes the compounding of communication and positioning by employing the data channel and pilot channel which modulate different rates of compound sequence.The compound sequence can be composed of a pseudo-random sequence and a subcarrier sequence.Aiming at the proposed waveform,a multi-loop tracking method is proposed to track multiple loops in receiver,thereby reducing the code tracking error of the waveform.The simulation results show that the proposed waveform has similar code tracking performance and better anti-multipath performance compared to the CBOC waveform of the same equivalent code rate.At the same time,as long as the power of the communication data channel and navigation pilot channel is reasonably allocated according to the application requirements,the interaction between communication and navigation is within an acceptable range,and the integrated system can operate normally.For the neural network-based channel tracking and equalization technology,we propose a channel tracking and equalization system model based on deep neural network(DNN)decision feedback.However,because the DNN model has insufficient generalization ability and poor convergence under time-varying multipath channels,we further propose a channel equalization system model(DLSTM)based on the combination of Long Short Term Memory(LSTM)network and DNN.In order to make better use of the forward-backward correlation of multipath signals,a channel equalization system model(DBi LSTM)based on the combination of bidirectional LSTM networks and DNNs is proposed in this paper.Simulations show that the DLSTM and DBi LSTM blind equalization models do not need to send training sequences.Real-time channel tracking and equalization can be achieved using sliding window and decision feedback method.Although the DLSTM and DBi LSTM models do not have convergence performance advantages compared to RLS-DFE blind equalization,their bit error rate performance is significantly better than RLS-DFE blind equalization.Under high dynamic signal-to-noise ratio and low dynamic channels,it can achieve a bit error rate performance similar to the pilot-based MMSE channel equalization system.Under high dynamic channels,the bit error rate performance of the MMSE channel equalization system based on the block processing of the pilot is severely degraded,but the proposed model still has strong robustness.