基于MMAS的MIMO-OFDM系统上行多用户检测

在多输入多输出-正交频分复用(multiple input multiple output-orthogonal frequency division multiplexing,MIMO-OFDM)系统上行多用户检测(multi-user detection,MUD)中,针对基本蚁群算法(ant colony optimization,ACO)在搜索过程中易出现过早停滞及收敛于局部最优解等问题,提出一种基于最大最小蚁群系统(max-min ant system,MMAS)的MUD新算法.该算法在蚁群每次循环结束后,仅处于最优路径上的单只蚂蚁释放信息素;同时,通过限制每条路径上信息素的取值范围,避免路径间信息素的差值过大,从而使蚂蚁在每次循环时尽可能地选择不同的路径,提高算法的搜索能力.仿真结果表明,MMAS算法能够有效降低蚁群陷入局部最优解的概率,进而提高了检测性能;同时,随用户数的增加,该算法的计算复杂度却远低于最大似然(maximum likelihood,ML)检测算法,因此,该算法能够在检测性能与计算复杂度之间取得较好的折中.

Max-min ant system algorithm for uplink multi-user detection in MIMO-OFDM system

For the uplink multi-user detection (MUD) in MIMO-OFDM systems, the basic ant colony optimization (ACO) algorithm is prone to premature stagnation and converges to a local optimum during the search process. In the paper, a new algorithm based on Max-Min Ant System (MMAS) was proposed, in which only one ant on the optimal path releases pheromone after each iteration. At the same time, the algorithm avoids the excessive pheromone between different paths then the ant chooses different paths as far as possible for each iteration. For these reasons, the search ability of the algorithm is effectively improved. Simulation results show that the MMAS algorithm can reduce the probability of the ant colony converge to a local optimal solution effectively, and then the detection performance is improved. At the same time, the computational complexity of the algorithm is far below the maximum-likelihood (maximum likelihood, ML) detection algorithm with the number of users increased. All in all, the algorithm can get a better tradeoff between detection performance and computational complexity.