A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network

Abstract

Spectrum saturation problem is a critical issue now a day due to huge number of users with wireless capable devices joined the network every day, but the spectrum resources are limited. To overcome this issue, cognitive radio (CR) technology was first proposed in year of 1999. The main objective of CR is to use licensed primary users’ (PUs’) spectrum by secondary users (SUs) without interfering the PUs. To detect PU channels, spectrum detection technique plays a major role to find the presence or absence of PUs to avoid interference. To protect licensed PUs’ from unwanted interference, the channel detection scheme is required to perform well in low signal to noise ratio (SNR) environments. Most of the work in the literature were performed based on computer based simulations and very few with experimental workflow, but they have used heavy laboratory instruments or stationary sensors for channel detection. Considering this issue, this thesis presents a method for real-time channel detection technique in a wireless local area network (WLAN) based CR network using Android based smartphones/tablet PCs. Also, designed an automatic channel selection (ACS) algorithm, which is practically experimented with an adaptive threshold determination technique with the 2.4 GHz WLAN. The algorithm is designed to work especially with Android based smartphones and tablets. Energy detection method with free space path loss (FSPL) environment is considered throughout the experiment for available PU channel detection.