Name
Abstract | ||
|---|---|---|
The dynamic nature of the wireless channel poses a challenge to services requiring seamless and uniform network quality of service (QoS). Self-healing, a promising approach under the self-organizing networks (SON) paradigm, and has been shown to deal with unexpected network faults in cellular networks. In this paper, we use simple machine learning (ML) algorithms inspired by SON developments in cellular networks. Evaluation results show that the proposed approach identifies the faulty APs. Our proposed approach improves throughput by 63.6% and reduces packet loss rate by 16.6% compared with standard 802.11. |
Year | Venue | Keywords |
|---|---|---|
2019 | 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) | Wi-Fi networks,seamless handover,wireless channel,unexpected network faults,cellular networks,SON developments,machine learning algorithms,self-healing solutions,self-organizing network paradigm,uniform seamless network quality of service,QoS,ML algorithms |
Field | DocType | ISSN |
Self-healing,Computer science,Computer network,Handover | Conference | 1573-0077 |
ISBN | Citations | PageRank |
978-1-7281-0618-2 | 0 | 0.34 |
References | Authors | |
0 | 2 | |