Dynamic optimization of neighbor list to reduce changeover latency for wi-fi networks

Hao, Lina; Ng, Bryan; Qu, Ying

doi:10.1145/3145777.3145787

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…In this section, the performance of the proposed self-optimizing approach for scanning parameters is evaluated compared with the 802.11 standard using the fixed timers suggested in [26] and the previous neighbor list mechanism (NLM) handover scheme [136]. The reason why the proposed self-optimizing approach is compared with NLM is to evaluate whether the performance using dynamic scanning parameters is better than using fixed values in NLM.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The self-healing network architecture is shown in Figure 5 Measured AP data is collected periodically and recorded in a table and stored in the APC. The neighbor APs information on each AP, which can be obtained based on the "Neighbor Report" mechanism specified in the IEEE 802.11k [136,139], is saved in the table. The machine learning algorithms are triggered to detect the faulty APs periodically.…”

Section: Self-healing Systemmentioning

confidence: 99%

“…Handover can be used to keep the data connection active when STAs move from one AP to another in wireless networks. However, handover is not explicitly supported in the 802.11 standard for WiFi networks [2]. The standard 802.11 only supports the STAs switching between two APs when the STAs move out of range of a serving AP.…”

Section: Introductionmentioning

confidence: 99%

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Self-Organizing Network (SON) Functionalities for Mobility Management in WiFi Networks

Hao¹

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<p>WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks. In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management. Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.</p>

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Self-healing Systemmentioning

confidence: 99%