Publication

Abstract : The increasing mobile device and unceasing traffic demand enables the deployment of large-scale WiFi systems that offers indoor coverage and high-speed connectivity. The large-scale deployment of WiFi system is an on-going research in wireless system due to its challenging heterogeneous nature of access points. Such access points undergo rapid challenges due to traffic conditions and traffic consumptions with rapidly increasing input data. On other hand, massive connection with heavy traffic laden from the WiFi devices poses increased pressure on backhaul network and reduces the Quality of Service by the users. We have developed using DenseNets that reduces the backhaul traffic due to the WiFi access points. The study explores wide deployment of data cache from massive access points for serving the several thousand active users. The study reduces the backhaul traffic using deep learning model that conducts statistical analysis on the collected user records. Extensive simulations are conducted to study the efficacy of the model that includes the cumulative distribution function per access point traffic/entropy and Jaccard similarity, caching resource utility and cache gain ratio.