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Access points deployment in data offloading using evolutionary optimization algorithms
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AI
DOI：10.1007/s00500-025-10964-7.png)
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Abstract
En 中文
With the proliferation of smartphones and other mobile devices, there has been a significant increase in mobile data consumption. To cope with the high load in the access part of cellular networks, network operators employ various strategies. Data offloading is indeed a promising solution for alleviating network congestion and improving data transmission in cellular networks. It involves diverting data traffic from cellular networks to other available wireless technologies, such as Wi-Fi or femtocells, that can provide additional bandwidth and capacity. By addressing performance issues and considering cost aspects through proper deployment, configuration, and management of access points (APs), Wi-Fi-based offloading can be optimized to provide efficient data transmission, alleviate cellular network congestion, and enhance the overall user experience. In this paper, a multi-objective problem is proposed to find the best locations of Wi-Fi APs providing the optimum performance of offloading in terms of throughput and offloaded traffic. Three optimization algorithms are applied to solve the problem include Genetic Algorithm (GA), Krill Herd Algorithm (KHA) and Sine Cosine Algorithm (SCA). The main contribution of this paper is to investigate the capabilities of every optimization method in providing the maximum performance metrics in single or multiple forms for data offloading. The evaluation results indicate that the SCA can provide the best performance in all scenarios due to its solution approach of black box model. The KHA provides more performance improvement than the GA method due to its approach of local optima finding using the exploitation phase. The superiority of GA method is its high convergence speed due to mutation in each iteration.
Keywords:
Offloading
WiFi APs
Cellular
Optimization
Performance
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