| Optimization problems in scientific research and engineering applications in various fields hasimportant theoretical significance and practical value, in recent years, in order to immune intelligentalgorithms and quantum computing as the representatives of intelligent algorithm is of simplecommon good robustness, fast computation of many characteristics, thus becoming solve complexoptimization problems powerful tool.Blind detection technology does not need to send a trainingsequence,only to estimate transmit sequence according to the received sequence.In this applicationcontext, this paper studied the current blind detection algorithm technology developmentbackground, the problem of blind equalization for SIMO systems, quantum immune optimizationalgorithm used in the blind test, and propose two new quantum immune algorithm, throughsimulation results show that: Improved immune quantum algorithms are able to achieve blinddetection, but also fast convergence rate, so they have a considerable research value.The thesis is divided into six chapters: Chapter I outlines the research background, researchvalue, and this major work done. The second chapter gives quantum information processingtechnology and introduce the principle of immune algorithm, and specifically describes the basicprinciples of quantum immune algorithm and characteristics. The third chapter introduces the basicequalization theory, SIMO system identification model and blind equalization of evaluation index.Chapter IV presents Immune Optimization Based on Quantum blind detection algorithm modelsand flow charts, and the performance of the algorithm simulation. Chapter V gives two methodsbased on quantum genetic algorithm blind detection algorithm-based on adaptive crossover andmutation operator quantum immune algorithm (self-adaptive crossover-mutation operator quantumimmune algorithm,SCOQIA) and the rotation angle based on dynamic variables (dynamic adjustingangle quantum immune algorithm—DAAQIA,DAAQIA) Quantum immune algorithm, and thesetwo kind of improved algorithms and convergence in terms of BER performance analysis. ChapterVI is a summary and outlook. |
