This thesis focuses on advanced signal processing techniques for multicarrier modulation, in particular, orthogonal frequency division multiplexing (OFDM). OFDM promises a substantial increase in data rate and robustness against the frequency selectivity of multipath channels. For coherent detection, channel estimation is essential for receiver design. In this thesis, we will present a receiver design where the channel estimator exploits the sparse nature of the physical channel. We present the most popular subspace algorithm from the array processing literature, namely root-MUSIC, recent sparse identification algorithms in the form of orthogonal matching pursuit (OMP) and basis pursuit (BP), and a hybrid method called path identification (PI) algorithm which is the main contribution of this thesis. We also compare the performance of these estimators with that of the conventional estimators such as least-squares (LS) estimator and linear minimum-mean-squares estimator (LMMSE).iii

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The spectrum handoff (SH) is a dynamic spectrum access technique which ensures effective channel utilization, fair resource allocation, as well as uninterrupted real-time connection. Facilitating SH across traffics of dissimilar characteristics in Cognitive Radio Networks (CRNs) is posing difficulty due to manifold interventions from Primary Users (PUs), disagreement among Secondary Users (SUs) and diversified Quality of Experience (QoE) demand. Here, we consider effective channel selection strategy (CSS) and put forward a learning-based handoff scheme to enhance QoE demand of users by the introduction of docition idea. A PU prioritized Markov method is introduced to represent the communications between PUs and SUs for even channel access. The reinforcement learning (RL) is applied to CSS to carry out proper channel selection. Numerical outcomes projects that proposed queuing model, suggested learning based handoff scheme and docitive learning enhances the quality of service by maintaining the average MOS of 3.6.

An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings.From the simulation, it can be inferred that Weibull fading outperforms all the other fading models considered. To accomplish missed detection probability of 1% in the Rayleigh channel, the false alarm probability obtained is almost 0.8 however to obtain the same missed detection probability in the Weibull channel, false alarm probability is less than 0.1 which is very favorable for both indoor and outdoor scenarios. Numerical analyses are carried out here to predict Primary User (PU) channel condition using Hidden Markov Model with the help of Time series forecasting learning method. It is evident that the prediction performance has reached 100% as the result of using the Weibull Fading Model for a period of 200 ms when compared to the Rayleigh model which is achieving only 84.5% accuracy in prediction.

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