SOCIAL ASSISTED CHANNEL ASSIGNMENT IN IEEE 802.11 WLANS

Abstract

Wireless Local Area Network (WLAN) such as Wi-Fi, are being deployed in large numbers in almost every facet of life today, including homes, offices, universities, hostels, railway stations, and other public places. An increase in Wi-Fi deployment is being carried out in response to unclear situations such as Covid-19 and the possibility of working from home. Since only a tiny portion of the spectrum is available to all the Access Points (APs) and mobile clients, there may be interference between APs serving different users, which may result in degraded performance of Wi-Fi and an increase in data transfer delays, resulting in a decrease in throughput. There are many different types of channel assignment algorithms available, which can operate in either a centralized or decentralized fashion and take into account a variety of transmission properties such as SINR, traffic load, channel congestion, the number of mobile clients in channels, the use-rate of APs, and so on, in order to estimate the best possible channel assignment for a given transmission. In light of the proliferation of intelligent portable mobile devices and the popularity of social networks, it is speculated that incorporating social domain conceptions may even aid in the improvement of Wi-Fi channel assignment. We investigated several Centralized and Decentralized methods of channel assignment in order to present a comprehensive picture of data transmission in Wi-Fi. Additionally, we introduced a novel method of improving the performance of Wi-Fi networks by utilizing social networks, which we refer to as Social-Assisted Random (SAR) and Social-Assisted Default (SAD) Channel Assignment. Our work reflects that leveraging social interactions among interfering APs can aid in achieving improved network performance in general. Keywords: Access Point (AP), Client Station (STA), IEEE 802.11 WLAN, Social network, Social Tie, CSMA/CA, Interference.