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Discrete Artificial Bee Colony for Computationally Efficient Symbol Detection in Multidevice STBC MIMO Systems

机译:用于多设备STBC MIMO系统中计算有效符号检测的离散人工蜂群

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摘要

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 algorithm is 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.
机译:提出了一种离散人工蜂群(DABC),用于在多设备空时分组码(STBC)多输入多输出(MIMO)通信系统中的接收器处进行联合符号检测。用于找到最佳检测的穷举搜索(最大似然检测)的计算复杂度随移动设备的数量,每个移动设备的发射天线以及每个符号的位数呈指数增长。 ABC是针对多变量数值函数而提出的一种新的基于人口,基于群体的进化算法(EA),与其他主流EA相比,ABC在连续域中的问题表现出良好的性能。该算法模拟了蜜蜂群的智能觅食行为。提出了一种增强的离散形式的ABC算法,并将其应用于联合符号检测问题,以实时找到几乎最佳的解决方案。多次独立模拟运行的结果表明,DABC与先前提出的用于联合符号检测的其他众所周知的算法(例如近最佳球面解码,最小均方误差,零强迫和半确定松弛)以及其他EA的有效性一样。作为遗传算法,分布估计算法以及更新颖的基于生物地理的优化算法。

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