Spread Spectrum Codes Blind Estimation Based On MATLAB

It is usually that the receiver must know the spreading sequence used by the sender to despread with correlators,and then it can recover the transmitted data.Direct sequence spread spectrum communications as the main means of spread spectrum communication,has a good low-emission power spectral density of concealment,the secrecy capacity of pseudo-random coding and signal processing related to anti-jamming capability.Therefore,the spread spectrum signal parameter detection and pattern recognition is an important step on how to detect and identify a direct sequence spread spectrum communication signals.This paper focuses on the use of MATLAB software,intelligent network computing method to achieve the detection of pseudo-random sequences in spread spectrum communication.The main work of the dissertation includes the following aspects:(1)The research in this paper is based on MATLAB software.First of all,it briefly describes the application of MATLAB.At the same time,the key research object is the pseudo-random sequence of the direct-sequence spread-spectrum signal,and several models of the spread spectrum signal are briefly introduced.This paper briefly introduces the architecture of mobile communication network,the application of pseudo-random sequence in mobile communication,briefly describes the communication security in communication,and briefly introduces the application of intelligent network in signal processing.(2)Pseudo-random sequence tracking is a key technology in direct-sequence spread-spectrum communication.The performance of tracking directly affects the performance of the entire receiver.This chapter mainly introduces a pseudo-random sequence combined with spectrum correction in direct-sequence spread spectrum communication.Tracking method.Firstly,the principle and performance index of direct-sequence spread-spectrum receiver are briefly introduced.Then the influence of Doppler frequency offset on tracking of pseudo-random sequence is analyzed.The energy leakage problem due to truncation in tracking pseudo-random sequence is studied.A spectral correction method was used to correct the frequency offset that was tracked,and the results were simulated and verified.(3)It proposed an approach that can estimate the pseudo-noise sequence from low SNR DS / SS signal,which is based on the eigen-decomposition of signal correlation matrix.Further,using the PCA based on variable step-size neural network method to achieve the pseudo-code sequence blind estimation.This method based on the self-adaptive variable step-size learning algorithm,a larger step size in the initial stages,can make faster convergence over time,gradually reducing the step in order to ultimately achieve a better neural network steady-state convergence.The algorithm overcomes the traditional algorithm's inherent conflicts between convergence speed and steady-state error.Based on the research of computational methods for detecting the pseudo-random sequence of direct-sequence spread-spectrum signals,this thesis establishes a simulation experiment using MATLAB software to verify the tracking and detecti on results of the pseudo-random sequence.Theoretical analysis and MATLAB software simulation results show that this method can accurately track and detect longer pseudo-random in the case of lower SNR.