基于拟生态算法的CDMA多用户检测方法研究

【作者】 许耀华；

【导师】 胡艳军；

【作者基本信息】 安徽大学 ， 电路与系统， 2004， 硕士

【摘要】 CDMA系统在技术上的优势已经使它成为3G的核心体制，但系统的容量和通信质量受限于多址干扰。多用户检测技术是宽带CDMA通信系统抗干扰的关键技术，其中最优多用户检测方法在理论上可以完全克服多址干扰，但计算复杂度大，因而目前无法实时实现，近年来出现一些复杂度较低的基于神经网络算法及混合遗传算法的多用户检测智能算法。在智能计算领域有一类新型的拟生态系统优化算法，如遗传算法、免疫算法等，它们模拟自然界生态系统机理，具有许多与传统优化算法不同的特点，并已成功运用于求解一些NP难解问题。 本论文结合了安徽省教育厅自然科学研究项目《大用户量CDMA多用户检测NP难解问题的优化算法研究》(2002kj007)，利用算法领域的两种新型拟生态系统优化算法处理CDMA多用户检测问题，寻找新的CDMA多用户检测处理方法。基于基本算法，针对多用户检测实际问题，提出相应的应用方法和改进策略，并通过仿真实例，验证各算法的有效性，本论文的主要研究工作如下： 1、首先，分析了CDMA通信系统中多用户检测问题，从算法复杂性的角度认识CDMA通信中的多用户检测方法。分析了优化算法领域较新出现的拟生态系统优化算法，介绍并比较了其中的蚁群优化算法和粒子群优化算法。 2、应用蚁群算法解决CDMA通信系统中的多用户检测问题。基于基本蚁群算法，结合多用户检测问题的实际特点，采用搜索空间分段和蚂蚁对相遇搜索的策略，提出一种新的基于蚁群算法的CDMA多用户检测方法，理论分析和仿真结果显示算法具有多项式的计算复杂度，并且算法误码率性能好于传统检测和解相关检测。 3、粒子群算法是一种较新的基于群智能的演化计算方法，具有算法简单、收敛快等优点，多用于连续空间优化。本文研究了应用于CDMA多用户检测问题离散空间搜索的离散粒子群算法，提出一种基于离散粒子群算法的CDMA多用户检测方法。分析以及实验仿真表明该方法具有计算复杂度低且安徽人学硕士论文《基于拟生态算法的CDMA多用户检测方法研究》可以.得到较好误码率性能的特点。对于本文提出的两种智能算法多用户检测器的性能和复杂度作了分析和仿真比较。 4、最后，对全文的研究工作进行了总结，展望了CDh乞气多用户检测的具体实现，复杂度的进一步降低，以及拟生态优化算法的进一步研究和应用。更多还原

【Abstract】 Code Division Multiple Access (CDMA) has more advantages than other access technologies, so it has already became the core technology of the third generation wireless communication systems, but these systems are all interference-limited, the system performance and capacity is limited by the Multi-Access Interference (MAI). Multi-user Detector (MUD) is the key interference cancellation technology of the wideband CDMA communication system, the optimal MUD can overcome the MAI theoretically, but it can not be carried out in real time on the current technology condition due to its high complexity, In resent years, there are many new intelligence MUD methods which utilize some neural networks and genetic algorithms and have lower computational complexity. At the same time, In the field of intelligent computation, there are several types of system optimization algorithms that have emerged, these are called Ecological System Optimization Algorithms (ESOAs), this category of novel optimization algorithms emulates the mechanism of natural ecological systems,(e.g. Immune Algorithms, Ant colony Algorithms etc.). There are several distinctive features in the ESOAs that are different from conventional optimization approaches, and they have already been used to solve many classical NP problems.The work of this thesis is a part of the Nature Science Research Project of Anhui Education Department, "Research on the optimization algorithms of NP-hard problem in CDMA multi-user detection with large user numbers". It deals with MUD problem with two kinds of typical new ESOAs, aimed to find out new methods of MUD. Based on their basic structures, some improvements and according application methods to these algorithms are proposed according to the practical problems emerging from Multi-user detection in CDMA communication systems. The validity of these algorithmshas been proven by case studies. The content of this thesis mainly includes:1. Analysis of the MUD problem in CDMA communication systems, coming out from the theory of computational complexity. The newly found Ecological System Optimization Algorithm is then introduced, and both of Ant Colony Optimization Algorithm and Particle Swarm Optimization Algorithm are compared and analyzed briefly.2. Ant Colony Optimization Algorithm for the MUD problem in CDMA communication systems. Based on the typical ant algorithm, our approach takes a pair ants meeting and search space partition mechanisms, and it is utilized to solve the MUD problem in CDMA communications. Analyses and simulation results show that our approach has polynomial computational complexity, and the Bit Error Rate (BER) performance of the algorithm is better than both the conventional detector and decorrelation detector.3. Particle Swarm Optimization Algorithm is a newly developed evolutionary algorithm, which is based on swarm intelligence, and has the properties to converge quickly, with simple rules, and has more applications in continuous space optimization. We study it on discrete space, and describe a discrete Particle Swarm Optimization Algorithm for the MUD problem in CDMA communication systems. Analyses and simulation results show our approach has lower computational complexity, and the BER performance of the algorithm is better than the conventional detector.4. Finally, the work of this thesis is summarized and the direction of further research on ESOAs, realization in the field of CDMA MUD and the simplification of the complexity of these algorithms is discussed.更多还原