Performance analysis of iterative decision-directed phase noise estimation

This contribution deals with estimation and compensation of phase noise in single-carrier digital communications. We present an iterative feedforward decision-directed phase noise estimation algorithm, that is based on approximating the phase noise process by an expansion of DCT basis functions containing only a few terms. An extension to the estimation algorithm is proposed, improving the performance in terms of the mean-square error. We demonstrate that the resulting (linearized) mean-square estimation error consists of two contributions: a contribution from the additive noise, that equals the Cramer-Rao lower bound, and a noise-independent contribution that results from the phase noise modeling error. The phase estimate that yields the lowest possible mean-square error is obtained, assuming knowledge of the phase noise statistics at the receiver

A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding

In this work, with combined belief propagation (BP), mean field (MF) and expectation propagation (EP), an iterative receiver is designed for joint phase noise (PN) estimation, equalization and decoding in a coded communication system. The presence of the PN results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the soft-in EKS with similar complexity, leading to significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter

Low-complexity frequency offset and phase noise estimation for burst-mode digital transmission

The presence of a frequency offset (FO) and phase noise can cause severe performance degradation in digital communication systems. This work combines a simple FO estimation technique with a low-complexity phase noise estimation method, inspired by the space-alternating generalized expectation-maximization algorithm. Using a truncated discrete-cosine transform (DCT) expansion, the phase noise estimate is derived from the estimated DCT coefficients of the phase. A number of implementations of the proposed algorithm are discussed. Numerical results indicate that when estimating the FO from pilot symbols only, comparable performance can be reached as the computationally more complex case where the FO is updated iteratively, with small convergence time. The phase noise estimation step is well capable of compensating for the residual FO. For the considered scenario, performing FO compensation before iterative phase noise estimation yields a bit-error rate performance degradation close to the case where the FO is known

Development of Some Spatial-domain Preprocessing and Post-processing Algorithms for Better 2-D Up-scaling

Image super-resolution is an area of great interest in recent years and is extensively used in applications like video streaming, multimedia, internet technologies, consumer electronics, display and printing industries. Image super-resolution is a process of increasing the resolution of a given image without losing its integrity. Its most common application is to provide better visual effect after resizing a digital image for display or printing. One of the methods of improving the image resolution is through the employment of a 2-D interpolation. An up-scaled image should retain all the image details with very less degree of blurring meant for better visual quality. In literature, many efficient 2-D interpolation schemes are found that well preserve the image details in the up-scaled images; particularly at the regions with edges and fine details. Nevertheless, these existing interpolation schemes too give blurring effect in the up-scaled images due to the high frequency (HF) degradation during the up-sampling process. Hence, there is a scope to further improve their performance through the incorporation of various spatial domain pre-processing, post-processing and composite algorithms. Therefore, it is felt that there is sufficient scope to develop various efficient but simple pre-processing, post-processing and composite schemes to effectively restore the HF contents in the up-scaled images for various online and off-line applications. An efficient and widely used Lanczos-3 interpolation is taken for further performance improvement through the incorporation of various proposed algorithms. The various pre-processing algorithms developed in this thesis are summarized here. The term pre-processing refers to processing the low-resolution input image prior to image up-scaling. The various pre-processing algorithms proposed in this thesis are: Laplacian of Laplacian based global pre-processing (LLGP) scheme; Hybrid global pre-processing (HGP); Iterative Laplacian of Laplacian based global pre-processing (ILLGP); Unsharp masking based pre-processing (UMP); Iterative unsharp masking (IUM); Error based up-sampling(EU) scheme. The proposed algorithms: LLGP, HGP and ILLGP are three spatial domain preprocessing algorithms which are based on 4th, 6th and 8th order derivatives to alleviate nonuniform blurring in up-scaled images. These algorithms are used to obtain the high frequency (HF) extracts from an image by employing higher order derivatives and perform precise sharpening on a low resolution image to alleviate the blurring in its 2-D up-sampled counterpart. In case of unsharp masking based pre-processing (UMP) scheme, the blurred version of a low resolution image is used for HF extraction from the original version through image subtraction. The weighted version of the HF extracts are superimposed with the original image to produce a sharpened image prior to image up-scaling to counter blurring effectively. IUM makes use of many iterations to generate an unsharp mask which contains very high frequency (VHF) components. The VHF extract is the result of signal decomposition in terms of sub-bands using the concept of analysis filter bank. Since the degradation of VHF components is maximum, restoration of such components would produce much better restoration performance. EU is another pre-processing scheme in which the HF degradation due to image upscaling is extracted and is called prediction error. The prediction error contains the lost high frequency components. When this error is superimposed on the low resolution image prior to image up-sampling, blurring is considerably reduced in the up-scaled images. Various post-processing algorithms developed in this thesis are summarized in following. The term post-processing refers to processing the high resolution up-scaled image. The various post-processing algorithms proposed in this thesis are: Local adaptive Laplacian (LAL); Fuzzy weighted Laplacian (FWL); Legendre functional link artificial neural network(LFLANN). LAL is a non-fuzzy, local based scheme. The local regions of an up-scaled image with high variance are sharpened more than the region with moderate or low variance by employing a local adaptive Laplacian kernel. The weights of the LAL kernel are varied as per the normalized local variance so as to provide more degree of HF enhancement to high variance regions than the low variance counterpart to effectively counter the non-uniform blurring. Furthermore, FWL post-processing scheme with a higher degree of non-linearity is proposed to further improve the performance of LAL. FWL, being a fuzzy based mapping scheme, is highly nonlinear to resolve the blurring problem more effectively than LAL which employs a linear mapping. Another LFLANN based post-processing scheme is proposed here to minimize the cost function so as to reduce the blurring in a 2-D up-scaled image. Legendre polynomials are used for functional expansion of the input pattern-vector and provide high degree of nonlinearity. Therefore, the requirement of multiple layers can be replaced by single layer LFLANN architecture so as to reduce the cost function effectively for better restoration performance. With single layer architecture, it has reduced the computational complexity and hence is suitable for various real-time applications. There is a scope of further improvement of the stand-alone pre-processing and postprocessing schemes by combining them through composite schemes. Here, two spatial domain composite schemes, CS-I and CS-II are proposed to tackle non-uniform blurring in an up-scaled image. CS-I is developed by combining global iterative Laplacian (GIL) preprocessing scheme with LAL post-processing scheme. Another highly nonlinear composite scheme, CS-II is proposed which combines ILLGP scheme with a fuzzy weighted Laplacian post-processing scheme for more improved performance than the stand-alone schemes. Finally, it is observed that the proposed algorithms: ILLGP, IUM, FWL, LFLANN and CS-II are better algorithms in their respective categories for effectively reducing blurring in the up-scaled images

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

A Survey of the methods on fingerprint orientation field estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods

Compensation of Laser Phase Noise Using DSP in Multichannel Fiber-Optic Communications

One of the main impairments that limit the throughput of fiber-optic communication systems is laser phase noise, where the phase of the laser output drifts with time. This impairment can be highly correlated across channels that share lasers in multichannel fiber-optic systems based on, e.g., wavelength-division multiplexing using frequency combs or space-division multiplexing. In this thesis, potential improvements in the system tolerance to laser phase noise that are obtained through the use of joint-channel digital signal processing are investigated. To accomplish this, a simple multichannel phase-noise model is proposed, in which the phase noise is arbitrarily correlated across the channels. Using this model, high-performance pilot-aided phase-noise compensation and data-detection algorithms are designed for multichannel fiber-optic systems using Bayesian-inference frameworks. Through Monte Carlo simulations of coded transmission in the presence of moderate laser phase noise, it is shown that joint-channel processing can yield close to a 1 dB improvement in power efficiency. It is further shown that the algorithms are highly dependent on the positions of pilots across time and channels. Hence, the problem of identifying effective pilot distributions is studied.The proposed phase-noise model and algorithms are validated using experimental data based on uncoded space-division multiplexed transmission through a weakly-coupled, homogeneous, single-mode, 3-core fiber. It is found that the performance improvements predicted by simulations based on the model are reasonably close to the experimental results. Moreover, joint-channel processing is found to increase the maximum tolerable transmission distance by up to 10% for practical pilot rates.Various phenomena decorrelate the laser phase noise between channels in multichannel transmission, reducing the potency of schemes that exploit this correlation. One such phenomenon is intercore skew, where the spatial channels experience different propagation velocities. The effect of intercore skew on the performance of joint-core phase-noise compensation is studied. Assuming that the channels are aligned in the receiver, joint-core processing is found to be beneficial in the presence of skew if the linewidth of the local oscillator is lower than the light-source laser linewidth.In the case that the laser phase noise is completely uncorrelated across channels in multichannel transmission, it is shown that the system performance can be improved by applying transmitter-side multidimensional signal rotations. This is found by numerically optimizing rotations of four-dimensional signals that are transmitted through two channels. Structured four-dimensional rotations based on Hadamard matrices are found to be near-optimal. Moreover, in the case of high signal-to-noise ratios and high signal dimensionalities, Hadamard-based rotations are found to increase the achievable information rate by up to 0.25 bits per complex symbol for transmission of higher-order modulations