基于增强蚁群优化的海量规模MIMO系统快速检测算法

发布时间：2019-02-22 11:46

【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。
[Abstract]:The symbol vector detection algorithm for large scale MIMO (multiple-input multiple-output, multiple input multiple output) systems has a high computational complexity. A fast detection algorithm for large scale MIMO systems with low computational complexity is proposed by combining particle swarm optimization with ant colony optimization. Firstly, a new probabilistic search model is derived, which combines distance based ant colony search with velocity based particle search. Then, combining the distance index of ACO (ant colony optimization, artificial ant colony optimization with the direction of PSO (particle swarm optimization, particle swarm optimization (PSO), the velocity index is combined to generate a new probability index, and the pheromone updating step of ACO is changed into the update of PSO speed. Finally, the MIMO detection problem is modeled as a path finding problem to find the sub-optimal solution of the MIMO symbol detection problem. The simulation results show that the performance of this algorithm is better than that of some traditional algorithms and other novel MIMO detection algorithms. Suitable for massive scale MIMO system.
【作者单位】：肇庆学院电子信息与机电工程学院;吉林大学物理学院;
【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)
【分类号】：TN919.3;TP18

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