Research On Quantum Ant Colony Optimization Algorithm And Its Application To Signal Detection Of LTE Systems

Quantum ant colony optimization, which combines the quantum computation with ant colonyoptimization, is a classic quantum intelligent optimization algorithm. It has better populationdispersion, better parallelism, faster convergence speed and much strong global search capabilityetc. This dissertation mainly researches on the characteristics and its improvement of the quantumant colony algorithm, and then attempt to apply the algorithm to the signal detection of the LTEsystems. The main research works of this dissertation can be summarized as follows:Firstly, this paper researched the basic principle and the work flow of the binary codedquantum ant colony algorithm (QACO) as well as the continuous quantum ant colony algorithm(CQACO).Analyzed the main strengths and weakness of the QACO and CQACO.The simulationresults of the three benchmark functions showed that the quantum ant colony algorithm has betteroptimization and faster search performance than the ant colony algorithm.Secondly, applied to the adaptive phase angle of rotation policy, combined with the QACO, anovel QACO (BQACO) was proposed in this paper. The simulation results of the five benchmarkfunctions showed that the BQACO has a higher rate of global convergence and better optimizationresults than the QACO and CQACO.Thirdly, Several traditional signal detection techniques were researched and their respectivestrengths and weaknesses were analyzed. Quantum ant colony algorithm based LTE system signaldetection scheme is proposed for the defects of traditional signal detection BER performance. Thenumerical and image transmission simulation results showed that the method proposed in this paperhave the superiority over the traditional detection algorithms in BER performance. It has muchcloser to the ML detection effects. What’s more, the performance of BQACO is better than theperformance of classic ACO.