Research On Blind Estimation Algorithm Of Spreading Sequences Based On ICA

DS-CDMA is a common communication system using Direct Sequence Spread Spectrum technology to realize Code Division Multiple Access, in non-cooperation communication condition, the spreading sequences estimation method is essential to do blind dispreading and then get the users’ data, so it is significant to do research on blind estimation of spreading sequences in DS-CDMA system. Independent Component Analysis (ICA) is an effective blind source separation method, which can take advantage of the independence of users’spreading sequences when used in spreading sequences estimation, and has the properties of low complexity and little requirement of priori knowledge. As a result, the research on blind spreading sequences estimation based on ICA has become a focus recently.Blind estimation of spreading sequences based on ICA is the main research object in this thesis. The main works can be concluded as follows:Firstly, the relevant contents of DS-CDMA communication system are introduced, and spreading sequences estimation model in DS-CDMA system is obtained after being analyzed. The basic theory of ICA is showed, and some common used objective functions and optimization algorithms are listed.Secondly, in order to get a more accurate result, on the basis of considering the data’s characteristics of spreading sequences estimation fully, some improvements are done to optimize the traditional FastICA, from whitening angle and optimizing angle separately, the KECA-ICA algorithm and BAT-ICA algorithm are proposed after combining KECA method and BAT algorithm. Compared with the traditional FastICA algorithm, these two new ICA algorithms have better performance in blind spreading sequences estimation. What’s more, the KECA-BAT-ICA algorithm is used to estimate the spreading sequences after taking those two improved algorithms’advantages, simulation results prove its superiority.Thirdly, aiming at the spreading sequences estimation when the number of users is unknown, still on the premise of ICA’s Blind Source Separation, this thesis presents an algorithm based on signal classification. Compared with traditional methods, this algorithm not only omits the complex step of estimating the source signals’number, but also avoids recovering the spreading sequences unsuccessfully when the source signals’number is wrong. The simulation results show the effectiveness and superiority of the new proposed algorithm.Finally, the summary and research prospect are given.