离散人工蜂群在多设备STBC MIMO系统中的高效符号检测

Saeed Ashrafinia, M. Naeem, D. Lee
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引用次数: 3

摘要

针对多设备空时分组码(STBC)多输入多输出(MIMO)通信系统中的联合符号检测问题,提出了一种离散人工蜂群(DABC)算法。查找最佳检测的穷举搜索(最大似然检测)的计算复杂度随着移动设备的数量、每个移动设备的发射天线数量和每个符号的位数呈指数增长。ABC算法是一种新的基于种群的、基于群体的多变量数值函数进化算法,与其他主流进化算法相比,它在连续域问题上表现出了良好的性能。该算法模拟了蜂群的智能觅食行为。提出了一种增强的离散版ABC算法,并将其应用于联合符号检测问题,以实时找到接近最优解。多次独立模拟运行的结果表明,DABC与其他已知的联合符号检测算法(如近最优球体解码、最小均方误差、零强迫和半定松弛)以及其他ea(如遗传算法、分布估计算法和更新颖的基于生物地理的优化算法)的有效性。
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Discrete Artificial Bee Colony for Computationally Efficient Symbol Detection in Multidevice STBC MIMO Systems
A Discrete Artificial Bee Colony (DABC) is presented for joint symbol detection at the receiver in a multidevice Space-Time Block Code (STBC) Mutli-Input Multi-Output (MIMO) communication system. Exhaustive search (maximum likelihood detection) for finding an optimal detection has a computational complexity that increases exponentially with the number of mobile devices, transmit antennas per mobile device, and the number of bits per symbol. ABC is a new population-based, swarm-based Evolutionary Algorithms (EA) presented for multivariable numerical functions and has shown good performance compared to other mainstream EAs for problems in continuous domain. This algorithm simulates the intelligent foraging behavior of honeybee swarms. An enhanced discrete version of the ABC algorithmis presented and applied to the joint symbol detection problem to find a nearly optimal solution in real time. The results of multiple independent simulation runs indicate the effectiveness of DABC with other well-known algorithms previously proposed for joint symbol detection such as the near-optimal sphere decoding, minimum mean square error, zero forcing, and semidefinite relaxation, along with other EAs such as genetic algorithm, estimation of distributions algorithm, and the more novel biogeography-based optimization algorithm.
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