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In this work, we present and investigate the performance of novel classification schemes for spectrum sensing in cooperative multiple-input multiple-output (MIMO) wireless cognitive radio (CR) networks. In this context, we consider several optimal classification schemes such as support vector classifiers (SVC), logistic regression (LR) and quadratic discrimination (QD) for primary user detection. It is demonstrated that these classification techniques have a significantly reduced complexity of implementation in practical CR applications compared to conventional likelihood based detection schemes as they do not require knowledge of the channel state information and noise power. Further, in the presence of disruptive malicious users, the proposed classifiers have a significantly lower detection error compared to conventional detection schemes. Also, we propose a novel QD classifier for blind MIMO spectrum sensing scenarios. The detection performance of the proposed classifiers is compared with existing schemes in co-operative CR scenarios. It is demonstrated through simulation of several scenarios including the presence of malicious users, Doppler shift, and carrier frequency offset that the proposed classifiers offer a robust and significantly superior alternative to existing schemes for co-operative MIMO CR spectrum sensing.

We propose a novel Bayesian error correction algorithm based on joint channel and data maximal-likelihood (ML) detection in wireless sensor networks (WSN). The proposed algorithm employs the temporal correlation of the narrowband sensor data in conjunction with the channel state information (CSI) for detection and error correction of the data received over the Rayleigh fading wireless channel. The proposed joint maximum-likelihood (JML) algorithm compares the joint channel and data likelihoods along different paths of the data likelihood tree (DLT), which is readily adaptable for efficient practical implementation in WSNs. Further, the JML scheme employs the sphere decoder for computation of the maximally likely sphere sensor data vectors in the WSN and thus has a low computational complexity. Simulation results demonstrate significantly reduced sensor error for the proposed WSN sensor correction technique over competing schemes existing in current literature.

In this work we propose a novel utility based algorithm for dynamic frame scheduling of a scalable video stream for video transmission over a 4G OFDMA wireless network. We consider the GOP structure of a temporally scalable video sequence and derive a scheme to compute the average utility and packet sizes of the frames belonging to the base and enhancement layers. This scheme is based on the rate models derived employing the H.264 joint scalable video model (JSVM) developed by the JVT and MPEG groups, and is hence readily applicable in practical scenarios. Based on the above rate models and standard video quality models existing in literature, we propose a utility index based optimal scheme for time-frequency (TF) resource scheduling in 4G OFDMA wireless systems towards video quality maximization. Simulation results in the context of 4G WiMAX cellular networks clearly demonstrate the superior performance of the proposed scheme compared to competing scheduling schemes existing in literature.