Читать книгу The Smart Cyber Ecosystem for Sustainable Development - Группа авторов - Страница 65

Latency is a key factor that impacts the performance of modern mobile applications. In [64], the authors found that, latency depends on three main parameters: Channel utilization, the number of online devices, and the SNR. WiFi latency can be modeled using these related factors. The authors developed and compared the performance of supervised ML-based algorithms used to measure, characterize, and predict delay in large-scale WLANs. Training is implemented using data sets obtained from field measurements.

Selecting a proper frame length is an important issue in WLANs, where it impacts the performance and users’ QoE in the network. The selection problem requires advanced techniques able to utilize information on practical settings in real-time.

The work in [65] proposes an SDN-based solution for frame length selection in WLANs. The system proposes inclusion of ML techniques in SD-WLANs to optimize the selection of frame length for each user based on channel conditions as well as overall performance indicators. The supervised learning approach is used, where the algorithm is deployed on the management plane of the SDN architecture. The CP periodically feeds the algorithm with network knowledge about channel conditions and users’ state.

The research work of [66] proposes a ML-based approach for the implementation of QoS management model in wireless networks. The ML system uses both supervised and unsupervised algorithms to identify key quality indicators for network users which represent an estimation of the quality as perceived by users considering influencing factors. Also, the ML concept is used for providing information about areas where corrective actions are required.