Читать книгу The Smart Cyber Ecosystem for Sustainable Development - Группа авторов - Страница 61

In recent years, we see tremendous widespread of WLANs, as they evolve to meet user’s requirements, especially the high speed Internet connection. Accurate prediction of WLANs performance is important for managing network resources. However, due to interference and the interactions between the physical and data link layers as well as the heterogeneity of WLAN devices, predicting and estimating the performance of WLANs is a difficult task. Many of the solutions use the Signal-to-Noise and Interference Ratio (SNIR) parameter. However, it has been proven that relying on this parameter to estimate the performance does not lead to satisfactory results. In fact, the performance of WLANs is more complex to be measured using SNIR, and it is a function of large number of interacting and related parameters that may change over time.

A plethora of research studies has developed various solutions to different challenges based on the traditional architecture of WLANS, aiming to optimally exploit network resources and provide high QoS to users. With the spread of AI systems and the tendency to develop AI-based solutions in all areas, researchers have been trying to adapt the ideas and tools of AI and study the possibility of using these tools for optimal operation and management of WLANs. In this section, we discuss some important studies in this field.

ML and the combination of ML and SDN have been shifting the research in WLANs to a new direction what allows more practical solutions to complex networking problems. Such solutions do not only simplify the management of network but also alleviate the complexity of algorithms and facilitate reaching optimal operation of a dynamically changing network.

The authors of [47] develop a framework solution for the control and management of WLANs based on the SDN approach. The system comprises a set of modules and ML algorithms, stressing the fact that modern and future WLANs will be intelligently controlled. The authors tried to show the strength of the developed solution by addressing the following issues: mobility, security, QoS, channel bandwidth allocation, coordinated transmission power, load balancing, and virtualization of wireless networks.