基于增强蚁群优化算法与排序的 MIMO 检测算法

基于最大似然比的多输入多输出(multiple input multiple output,MIMO)检测算法的计算复杂度随着天线阵的规模呈指数级增加,提出一种计算复杂度较优的MIMO检测算法.采用基于对数似然比的排序QR分解技术将信道矩阵分解为正交矩阵与上三角矩阵,相应地修改信号的发射顺序,降低错误判断引起的错误传播效应;为传统人工蚁群优化算法的信息素更新策略引入负信息素概念,有效地控制系统的拥塞;根据优化路径的距离积累了信息素.该方法设计了基于负信息素的信息素更新策略,增加MIMO系统的拥塞控制能力,考虑信道的衰落本性,基于路径的距离积累信息素.为了测试该算法的性能,进行了多组对比实验,结果表明,误码率性能优于其他智能优化算法,且对于64×64等大规模天线阵,该算法的计算复杂度随天线规模增长较小.

Enhanced ant colony optimization and ordering based MIMO system symbol detection algorithm

Maximum likelihood MIMO(multiple-input-multiple-output) detection algorithm has best symbol detection performance, but it''s computation complexity increases in exponential order with the scale of antenna matrix, so a MIMO detection algorithm with lower computational complexity is proposed for that problem. Firstly, log maximum likelihood based sorted QR decomposition algorithm is used to decompose channel matrix into an orthonormal matrix and the upper triangular matrix, and the transmit order is changed accordingly, and the error propagation is improved by error decision; then, negative pheromones are introduced to ant colony algorithm, and the system congestion is controlled efficiently; lastly, the pheromones are updated according to the distance of the path. The negative pheromone based pheromone updating strategy is designed to improve the ability of congestion control, and considering the faded character of channel, the pheromone is accumulated based on the distance of the path. Some compared simulation experiments are setup to test the performance of the proposed algorithm. The results show that, the bit error rate performance of the proposed algorithm is better than other artificial intelligence algorithm;the computation complexity of the proposed algorithm increase is lower with antenna number for big scale antenna matrix.