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首页> 外文期刊>IEEE transactions on wireless communications >An Efficient Stochastic Gradient Descent Algorithm to Maximize the Coverage of Cellular Networks
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An Efficient Stochastic Gradient Descent Algorithm to Maximize the Coverage of Cellular Networks

机译：一种有效的随机梯度下降算法，可最大化蜂窝网络的覆盖范围

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摘要

Network coverage and capacity optimization is an important operational task in cellular networks. The network coverage maximization by adjusting azimuths and tilts of antennas is focused and the existing approaches are mainly gradient-free methods. A standard gradient descent algorithm and its improved version, namely a Stochastic Gradient Descent (SGD) algorithm are proposed on the basis of a novel coverage indicator, named as the soft coverage indicator, to approximate the hard version of the original coverage indicator. We prove that the gradient vector is sparse, which accelerates gradient calculation, due to the number limitation of base stations within a specific distance from a given sampling point even if there are many decision variables of azimuths and tilts. Also, the SGD algorithm only requires a small amount of computation based on cheap estimates of the gradients, and thus is applicable to large-scale networks in an efficient manner. The experiments show that the proposed approaches perform well both in their near-optimal solutions and in their computation efficiency compared with the meta-heuristic algorithms. The extensibility and practicality of the proposed algorithms are also discussed.
机译：网络覆盖和容量优化是蜂窝网络中的重要操作任务。通过调整天线的方位角和倾斜度来最大化网络覆盖是关注的焦点，并且现有的方法主要是无梯度方法。在一种称为软覆盖指标的新型覆盖指标的基础上，提出了一种标准的梯度下降算法及其改进版本，即随机梯度下降（SGD）算法，以近似于原始覆盖指标的硬版本。我们证明了梯度向量是稀疏的，这是由于即使有许多方位角和倾斜度决策变量，由于距给定采样点特定距离内的基站数量有限，从而加速了梯度计算。而且，SGD算法仅需要基于廉价的梯度估计就可以进行少量计算，因此可以有效地应用于大规模网络。实验表明，与元启发式算法相比，所提出的方法在接近最优的解决方案和计算效率方面均表现良好。还讨论了所提出算法的可扩展性和实用性。

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