Cognitive Radio (CR) technology creates the opportunity for unlicensed users to make use of the spectral band provided it does not interfere with any licensed user. It is a prominent tool with spectrum sensing functionality to identify idle channels and let the unlicensed users avail them. Thus, the CR technology provides the consumers access to a very large spectrum, quality spectral utilization, and energy efficiency due to spectral load balancing. However, the full potential of the CR technology can be realized only with CRs equipped with accurate mechanisms to predict/sense the spectral holes and vacant spectral bands without any prior knowledge about the characteristics of traffic in a real-time environment. Multi-layered perception (MLP), the popular neural network trained with the back-propagation (BP) learning algorithm, is a keen tool for classification of the spectral bands into "busy" or "idle" states without any a priori knowledge about the user system features. In this dissertation, we proposed the use of an evolutionary algorithm, Bacterial Foraging Optimization Algorithm (BFOA), for the training of the MLP NN. We have compared the performance of the proposed system with the traditional algorithm and with the Hybrid GA-PSO method. With the results of a simulation experiment that this …

In this dissertation a computationally efficient cognitive multiple-input multiple-output (MIMO) orthogonal frequency division duplexing (OFDM) detector is designed to decode perfect space-time coded signals which are able maximize the diversity and multiplexing properties of a rich fading MIMO channel. The adaptive nature of the cognitive detector allows a MIMO OFDM communication system to better meet to needs of future wireless communication networks which require both high reliability and low run-time complexity depending on the propagation environment. The cognitive detector in conjunction with perfect space-time coding is able to achieve up to a 2 dB bit-error rate (BER) improvement at low signal-to-noise ratio (SNR) while also achieving comparable runtime complexity in high SNR scenarios.