Improvement Ant Colony Algorithms And Its Application To Blind Equalization

Ant colony algorithm, which has the advantages of positive feedback, distributed parallel computer, more robustness, and being easy to combine with other optimization algorithms, is a heuristic algorithm with group intelligent bionic. The areas of application gradually expanded with the deepening research. Especially with the development of blind signal processing technology, the ant colony algorithm also has been applied to blind signal detection technology, and achieved good results. Blind signal detection has a wide range of applications in many scientific fields, in particular, the field of wireless communication systems and bio-signal processing. In this paper, two improved blind detection algorithms based on ant colony optimization has been proposed against the shortcomings the ant colony algorithm for solving blind detection problem in the literature. And the superiority of the improved algorithm has been verified through the performance simulations and the complexity and convergence analysis of the algorithm.This paper is organized as follows. The first chapter generally introduces the background, status of the study and the main work of this paper. The second chapter describes the principle of ant colony algorithm, features, performance parameters, typical applications and so on. ChapterⅢdescribes the basic ant colony optimization blind detection algorithm based on the SIMO system model, meanwhile the algorithm is validated by simulation after transferring the problem of blind equalization into quadratic programming problem of finite alphabet. Two improved algorithms are firstly proposed in chapterⅣ, called blind detection algorithms based on SACO and RPACO. The advantages of these algorithms have been proved by analysis of performance simulation results and theoretical research in terms of complexity and the convergence. In the following chapterⅤ, the basic and two improved ant colony optimization blind detection algorithms have been extended from the BPSK signal to QPSK and multi-level character set QAM case. Then the improved algorithms with stable convergence and blind detection performance have been verified by simulating their bit error rate and convergence. The work of this dissertation is summarized and the prospective of future research is discussed in chapterⅥ.