Improvement Ant Colony Algorithms and Its Application to Blind Equalization
|School||Nanjing University of Posts and Telecommunications|
|Course||Circuits and Systems|
|Keywords||Blind Equalization Blind detection Ant colony optimization algorithm Simplify the ant colony algorithm Random perturbations ant colony algorithm|
The ant colony algorithm is a new group of intelligent bionic heuristic algorithm , it has the advantages of positive feedback and distributed parallel computer system is robust and easy to combine with other optimization algorithms . With the deepening of the ant colony algorithm , application areas gradually expanded , especially with the development of blind signal processing technology , the ant colony algorithm is applied to blind signal detection technology , and achieved good results . The blind signal detection in many scientific fields , has a wide range of applications , especially in the field of wireless communication system, the biological signal processing . In this paper, the shortcomings of the ant colony algorithm for solving the problem of blind detection literature , two improved blind detection algorithm based on ant colony optimization , and verify algorithm performance simulation and complexity and convergence analysis of the superiority of the improved algorithm . The first chapter discusses the background and status of the study in the full-text , and text overview of the work done . The second chapter describes the principle of ant colony algorithm , the features of the algorithm , the performance parameters and typical applications . Chapter first blind equalization problem into quadratic programming problems is based on a limited character set , and then introduce the basic ant colony optimization blind detection algorithm based SIMO system model , and finally validated by simulation . Chapter two improved algorithms - simplify Ant Colony Optimization blind detection (SACO) algorithm and random disturbance ant colony optimization Blind detection algorithm ( RPACO ) , through the analysis of two improved algorithm performance simulation , and simplify the ant colony optimization blind detection algorithm complexity and convergence theory analysis to verify the superiority of the improved algorithm . Chapter basic ant colony and two improvements ant colony optimization blind detection algorithm extended to send signals from the BPSK QPSK signal random complex channel and multi - level multi- character set QAM ??system , and the error rate convergence experimental simulation , simulation results demonstrate the improved algorithm has stable convergence and blind detection performance . The sixth chapter is the outlook for the full text of the summary and future research direction .