Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting
[EN] Channel assignment in Wi-Fi 6 dense networks is a challenging problem which critically impacts the performance of WLAN communications. Although there have been many proposals in the area of IEEE 802.11 channel assignment, LCCS has emerged as the de facto standard for distributed channel selecti...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/220731 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/220731 |
| Access Level: | acceso abierto |
| Palabra clave: | Thermodynamic Threshold Accepting Wi-Fi 6 Channel allocation Optimization |
| Sumario: | [EN] Channel assignment in Wi-Fi 6 dense networks is a challenging problem which critically impacts the performance of WLAN communications. Although there have been many proposals in the area of IEEE 802.11 channel assignment, LCCS has emerged as the de facto standard for distributed channel selection. Notwithstanding, centralized optimizers overcome the performance of distributed channel assignment algorithms, so their study is of utmost importance. One of the most successful optimization proposals in the state-of-the-art for Wi-Fi 4 is based on simulated annealing (SA). However, SA presents a significant limitation: its parameters must be adjusted manually for each scenario, otherwise yielding suboptimal solutions. In this paper, we propose a new technique for channel assignment optimization of Wi-Fi dense networks, called Thermodynamic Threshold Accepting (TTA), including modifications inspired by Threshold Accepting (TA) and Thermodynamic Simulated Annealing (TSA). With respect to SA, TTA avoids manual tuning, being able to adapt to the specific setting automatically. We evaluate TTA in a dense Wi-Fi model and, compared to SA, we show that our approach reaches optimal results without requiring any prior fine-tuning. We also show that TTA is even able to slightly overcome the performance of an accurately calibrated SA optimizer for the same number of iterations. |
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