Dissertation
Dissertation > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Communication theory > Channel equalization

Improvement Ant Colony Algorithms and Its Application to Blind Equalization

Author WangJinWei
Tutor YuShuJuan
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
CLC TN911.5
Type Master's thesis
Year 2012
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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 .

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