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Energy efficiency-driven mobile base station deployment strategy for shopping malls using modified improved differential evolution algorithm
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AI
DOI：10.1007/s10489-022-03358-x.png)
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Abstract
En 中文
The short-time aggregation of human traffic places high demands on the communication capacity of cellular networks. The deployment of expensive permanent infrastructure without continuous high traffic is uneconomical, and the problem poses a challenge. In this study, a green mall traffic model based on mobile base stations with a dynamic sleep strategy is proposed for surges of shopping mall traffic. The model is addressed through a modified improved differential evolution (MIDE) algorithm based on the original improved differential evolution (IDE) algorithm. The algorithm has two sets of mutation and restart policies adapted to different traffic volumes, and can dynamically adjust according to the traffic volume. The effectiveness of the algorithm is verified by simulation experiments. Compared with the traditional differential evolution (DE) algorithm and the DE series algorithms recently published in Swarm and Evolutionary Computation, a journal with high impact factors, MIDE can effectively optimize the system model and improve its energy efficiency, saving 1.1%-56.4% in simulation experiments.
Keywords:
5G
Mobile base station
Shopping mall
Green metrics
IDE algorithm
Journal
IF:
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7.5K
Citations:
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