4.7 Article

An Efficient Stochastic Gradient Descent Algorithm to Maximize the Coverage of Cellular Networks

期刊

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 18, 期 7, 页码 3424-3436

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2019.2914040

关键词

Cellular network; coverage optimization; gradient descent; stochastic gradient descent; computational complexity

资金

  1. Joint Foundation of the Ministry of Education (MoE) [MCM20160103, MCM20170108]
  2. China Mobile Group [MCM20160103, MCM20170108]
  3. National Natural Science Foundation of China [61822104, 61370191]
  4. Fundamental Research Funds for the Central Universities [RC1631]

向作者/读者索取更多资源

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.

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