In a world where every single aspect is driven by technology and where the importance and Need of communication in every form is at its zenith, there expectedly arises an exigent need for faster and more efficient techniques of communication to effectively back the myriad utilizations of technology. With burgeoning demand for wireless connectivity, the stress put on an already scarce resource (namely spectrum) is reaching a breaking point. Hence, it has become pivotal to find ways for the efficient and equitable utilization of spectrum. Multiband Orthogonal Frequency Division Multiplexing (MBOFDM) is a technique that is primarily under consideration to tackle the problem of effective utilization of spectrum. One of the most popular systems in usage, the Ultra Wideband (UWB) systems, has a serious flaw. It poses a threat to other wireless connectors such as Wi-Fi, WiMax etc. As such interferences go against the scheme of equitable use; complicated interference cancellation schemes become necessary for the isolation of such networks. Traditionally, error control codes were used to make such troublesome or noisy channels available for reliable communication. But the other method that has recognizably more potential for solving the problem of spectrum congestion is Cognitive Radio.
This paper mainly focuses on the MB-OFDM systems with the following enhancements: 1) Concatenation of convolutional code with ReedSolomon code and to analyze the performance of MBOFDM systems under various modulation schemes 2) Active interference cancellation between primary and secondary users using AIC tones and 3) Spectrum sensing using energy detection method and to train a classifier to do the same using neural network in order to solve the problem of spectrum.