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A Variable Neighborhood Search Algorithm for Massive MIMO Resource Allocation

机译:大规模MIMO资源分配的可变邻域搜索算法。

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In this paper, we consider the problem of maximizing capacity for a Massive Multiple Input Multiple Output (MIMO) system subject to power and antenna assignment constraints. Massive MIMO technology has gained increased attention by the research community within last decade as it has become a strong candidate for 5G wireless communications. Some advantages of this new technology include better performance in terms of data rate and link reliability, transmitting in higher frequency bands which improves coverage, strong signal indoors, and the possibility of more resistant systems to intentional jamming attacks, to name a few. The optimization problem is formulated as a mixed integer nonlinear programming problem for which exact methods cannot be applied efficiently. Consequently, we propose a variable neighborhood search (VNS for short) meta-heuristic algorithm which allows to obtain significantly better solutions compared to a state of the art algorithm. Although, at a higher computational cost for most tested instances.
机译:在本文中,我们考虑了最大限度地提高了大量多输入多输出(MIMO)系统的能力和天线分配约束的容量。由于它已成为5G无线通信的强大候选者,巨大的MIMO技术在过去十年内获得了研究界的关注。这种新技术的一些优点包括在数据速率和链路可靠性方面具有更好的性能,在更高频段中传输,从而提高覆盖范围,强烈的信号在室内,以及更具耐受系统的可能性,以故意干扰攻击,以少数名。优化问题被制定为混合整数非线性编程问题,其无法有效地应用精确方法。因此,我们提出了一种变量邻域搜索(用于短路的VNS)元启发式算法,其允许与最先进的算法相比获得明显更好的解决方案。虽然,对于大多数测试的实例,以更高的计算成本。

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