基于增强蚁群优化的海量规模MIMO系统快速检测算法  

Enhanced ant colony optimization based fast detection algorithm of massive MIMO system

作　　者：陈永建[1] 周艳[1] 崔海宁[2] 

机构地区：[1]肇庆学院电子信息与机电工程学院,广东肇庆526061 [2]吉林大学物理学院,长春130012

出　　处：《计算机应用研究》2017年第4期1208-1212,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61179055);广东省科技计划资助项目(2012B040303007);肇庆学院校级自然科学资助项目(201423)

摘　　要：大规模MIMO(multiple-input multiple-output,多输入多输出)系统的符号向量检测算法计算复杂度较高,对此结合粒子群优化与蚁群优化提出一种低计算复杂度的海量规模MIMO系统快速检测算法。首先,推导出一种新的概率搜索模型,将基于距离的蚁群搜索与基于速度的粒子搜索结合;然后,将ACO(ant colony optimization,人工蚁群优化)距离指标与PSO(particle swarm optimization,粒子群优化)的方向、速度指标结合生成一种新的概率指标,将ACO的信息素更新步骤变为PSO速度的更新;最终,将MIMO检测问题建模为路径寻找问题,寻找MIMO符号检测问题的次优解。对比仿真实验结果表明,本算法的检测性能优于部分传统算法以及其他新颖的MIMO检测算法,在获得与最大似然估计检测法接近的误码率性能下,具有极快的计算速度,适用于海量规模的MIMO系统。Concerned the problem that the detection algorithms of symbol vectors in large MIMO system have high computational complexity, this paper proposed an ant colony optimization based fast detection algorithm of massive MIMO system with low computational complexity combined with particle swarm optimization. Firstly, it derived a new probability search model, the distance based ant colony search was combined with the velocity based PSO search. Then, the distance metric was com- bined with the direction and velocity metrics to output a new probability metric, and the step of pheromone update in ACO was replaced with the velocity update of PSO. Lastly, the algorithm modeled the MIMO detection problem as path finding problem to search the sub-optimal solution of the MIMO detection problem. Compared simulation experimental results show that the de- tection performance of the proposal outperforms some traditional algorithms and other new MIMO detection algorithms. It shows a good computational efficiency on the basis that it realizes similar bit error rate performance with maximum likelihood detection algorithm, and it is suitable to massive MIMO system.

关 键 词：多输入多输出 蚁群优化算法 粒子群优化算法 路径寻找问题 误码率 

分 类 号：TN919.72[电子电信—通信与信息系统]