Synchronization Techniques for Burst-Mode Continuous Phase Modulation

Synchronization is a critical operation in digital communication systems, which establishes and maintains an operational link between transmitter and the receiver. As the advancement of digital modulation and coding schemes continues, the synchronization task becomes more and more challenging since the new standards require high-throughput functionality at low signal-to-noise ratios (SNRs). In this work, we address feedforward synchronization of continuous phase modulations (CPMs) using data-aided (DA) methods, which are best suited for burst-mode communications. In our transmission model, a known training sequence is appended to the beginning of each burst, which is then affected by additive white Gaussian noise (AWGN), and unknown frequency, phase, and timing offsets. Based on our transmission model, we derive the Cramer-Rao bound (CRB) for DA joint estimation of synchronization parameters. Using the CRB expressions, the optimum training sequence for CPM signals is proposed. It is shown that the proposed sequence minimizes the CRB for all three synchronization parameters asymptotically, and can be applied to the entire CPM family. We take advantage of the simple structure of the optimized training sequence in order to design a practical synchronization algorithm based on the maximum likelihood (ML) principles. The proposed DA algorithm jointly estimates frequency offset, carrier phase and symbol timing in a feedforward manner. The frequency offset estimate is first found by means of maximizing a one dimensional function. It is then followed by symbol timing and carrier phase estimation, which are carried out using simple closed-form expressions. We show that the proposed algorithm attains the theoretical CRBs for all synchronization parameters for moderate training sequence lengths and all SNR regions. Moreover, a frame synchronization algorithm is developed, which detects the training sequence boundaries in burst-mode CPM signals. The proposed training sequence and synchronization algorithm are extended to shaped-offset quadrature phase-shift keying (SOQPSK) modulation, which is considered for next generation aeronautical telemetry systems. Here, it is shown that the optimized training sequence outperforms the one that is defined in the draft telemetry standard as long as estimation error variances are considered. The overall bit error rate (BER) plots suggest that the optimized preamble with a shorter length can be utilized such that the performance loss is less than 0.5 dB of an ideal synchronization scenario

Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction