In this post, we present the work on radio coverage in Wlans, accessible at this address: A novel beamforming based model of coverage and transmission costing in IEEE 802.11 WLAN networks, about how to modelize the radio area coverage to enhance costing of the transmission opportunity in such networks. The work argues that IEEE 802.11

In this post we present our work on RRM solution and we present our proposed machine learning (ML) optimisation to a general operation of dynamic RRM. Pelease contact us for any question or report directly to the full text available at: An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks. We argue that in

Dynamic Radio Resource Management helps overcome interferences in dense WLAN deployments. By processing data from upper-layers services, it could optimize and enrich end-to-end wireless client experience. This experience may be tight (related) to a transmit opportunity function that hints on the radio interface condition, and effectiveness of the transmission itself from different points of view:

In this post we present the work fully available under this link: ML-Optimized Beam-based Radio Coverage Processing in IEEE 802.11 WLAN Networks, about radio coverage processing in wlan and its optimization using machine learning (ML). Dynamic Radio Resource Management (RRM) is a major building block of Wireless LAN Controllers (WLC) function in WLAN networks. In

In this post we present the work fully available at this link: A NURBS-optimized dRRM solution in a mono-channel condition for IEEE 802.11 entreprise Wlan networks, about dynamic RRM (Radio Resource Management) and especially on a NURBS optimized solution of RRM. NURBS relates to Non-Uniformal Rational B-Splines surfaces concepts usually known in CAGD (Conception Aided