Wireless Communication System, Channel Estimation And Equalization Techniques

Channel Equalization and Estimation technique has always been the indispensable part of wireless communication system and a necessary mean to overcome multi-path effect and inter symbol interference (ISI). Channel equalization can be classified into three sorts: Linear Adaptive Equalization , Bind Equalization and Semi-blind Equalization. Linear Adaptive Equalization primarily depends on training sequence which is known to both source node and destination node to achieve equalization goals. On the other hand, Blind Equalization makes use of some characteristics of transmitted signal which has no connection to signal carrier. And Semi-bind Equalization, integrating both advantages of Linear Adaptive Equalization and Blind Equalization, can get better equalization results by using a small amount of training sequence and innate characteristics of transmitted signal.This paper mainly focused on the convergence performance of equalization algorithm, with the aim of maximally reducing the negative effect of ISI. The main contents are as follows:1. Introduced the research background and purpose of equalization algorithm, and analyzed its the essential effect in wireless communication system;2. In the respect of linear adaptive equalization, in order to improve the performance of LMS adaptive filtering algorithm, an improved robustness adaptive step-size LMS equalization algorithm is presented by establishing a nonlinear relationship between the two relevant statistics for step-size factor and the error signal. Compared with other algorithms, this algorithm can overcome the sensitivity to the noise coming from outside introduced by the statistics of the correlation of error signal. Meanwhile, this algorithm presents some improvement on the principle of robustness. Theoretical analysis and simulation results indicate that this algorithm has a faster convergence speed and a better steady-state error, and can go back to steady state quickly when the channel is varying with time, which shows a better robustness and convergence than other traditional ones.3. In the respect of Blind Equalization, we have studied the features, advantages and disadvantages of CMA, MCMA and ECMA through computer simulation. Besides this, we have analyzed the deficiency of three algorithms when dealing with high-order QAM signal. Two kinds of joint blind equalization algorithm, which utilize Constellation Matching Error to make compensation to MCMA and ECMA, are proposed. Computer simulation has proved that the two algorithms can carry out the 16-QAM signal equalization process effectively.4. In the respect of Semi-Blind Equalization, in order to overcome the disadvantages that the Constant Modulus Algorithm and Modified Constant Modulus Algorithm is of low convergence rate, a fast convergent algorithm for semi-blind equalization is presented. We use the CMA and MCMA to perform channel equalization, and adopt LMS algorithm with decision to compensate CMA and MCMA respectively. Simulations, which are done under linear channel and nonlinear channel respectively, indicate that convergence rate of the new algorithm is four times faster than that of Constant Modulus Algorithm and Modified Constant Modulus Algorithm under linear channel. In case of nonlinear channel, the convergence speed of this new semi-blind equalization algorithm is also four times faster than that of Modified Constant Modulus Algorithm.