Efficient Adaptive Filter Algorithms Using Variable Tap-length Scheme

Today the usage of digital signal processors has increased, where adaptive filter algorithms are now routinely employed in mostly all contemporary devices such as mobile phones, camcorders, digital cameras, and medical monitoring equipment, to name few. The filter tap-length, or the number of taps, is a significant structural parameter of adaptive filters that can influences both the complexity and steady-state performance characteristics of the filter. Traditional implementation of adaptive filtering algorithms presume some fixed filter-length and focus on estimating variable filter\u27s tap-weights parameters according to some pre-determined cost function. Although this approach can be adequate in some applications, it is not the case in more complicated ones as it does not answer the question of filter size (tap-length). This problem can be more apparent when the application involves a change in impulse response, making it hard for the adaptive filter algorithm to achieve best potential performance. A cost-effective approach is to come up with variable tap-length filtering scheme that can search for the optimal length while the filter is adapting its coefficients. In direct form structure filtering, commonly known as a transversal adaptive filter, several schemes were used to estimate the optimum tap-length. Among existing algorithms, pseudo fractional tap-length (FT) algorithm, is of particular interest because of its fast convergence rate and small steady-state error. Lattice structured adaptive filters, on the other hand, have attracted attention recently due to a number of desirable properties. The aim of this research is to develop efficient adaptive filter algorithms that fill the gap where optimal filter structures were not proposed by incorporating the concept of pseudo fractional tap-length (FT) in adaptive filtering algorithms. The contribution of this research include the development of variable length adaptive filter scheme and hence optimal filter structure for the following applications: (1) lattice prediction; (2) Least-Mean-Squares (LMS) lattice system identification; (3) Recursive Least-Squares (RLS) lattice system identification; (4) Constant Modulus Algorithm (CMA) blind equalization. To demonstrate the capability of proposed algorithms, simulations examples are implemented in different experimental conditions, where the results showed noticeable improvement in the context of mean square Error (MSE), as well as in the context of convergence rate of the proposed algorithms with their counterparts adaptive filter algorithms. Simulation results have also proven that with affordable extra computational complexity, an optimization for both of the adaptive filter coefficients and the filter tap-length can be attained

Blind adaptive equalization for QAM signals: New algorithms and FPGA implementation.

Adaptive equalizers remove signal distortion attributed to intersymbol interference in band-limited channels. The tap coefficients of adaptive equalizers are time-varying and can be adapted using several methods. When these do not include the transmission of a training sequence, it is referred to as blind equalization. The radius-adjusted approach is a method to achieve blind equalizer tap adaptation based on the equalizer output radius for quadrature amplitude modulation (QAM) signals. Static circular contours are defined around an estimated symbol in a QAM constellation, which create regions that correspond to fixed step sizes and weighting factors. The equalizer tap adjustment consists of a linearly weighted sum of adaptation criteria that is scaled by a variable step size. This approach is the basis of two new algorithms: the radius-adjusted modified multitmodulus algorithm (RMMA) and the radius-adjusted multimodulus decision-directed algorithm (RMDA). An extension of the radius-adjusted approach is the selective update method, which is a computationally-efficient method for equalization. The selective update method employs a stop-and-go strategy based on the equalizer output radius to selectively update the equalizer tap coefficients, thereby, reducing the number of computations in steady-state operation. (Abstract shortened by UMI.) Source: Masters Abstracts International, Volume: 45-01, page: 0401. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006

Robust multilevel coherent optical systems with linear processing at the receiver

This paper investigates optical coherent systems based on polarization multiplexing and high-order modulations such as phase-shift keying (PSK) signals and quadrature amplitude modulations (QAM). It is shown that a simple linear receiver processing is sufficient to perfectly demultiplex the two transmitted streams and to perfectly compensate for group velocity dispersion (GVD) and polarization mode dispersion (PMD). In addition, in the presence of a strong phase noise of the lasers at the transmitter and receiver, a symbol-by-symbol detector with decision feedback is able to considerably improve the receiver robustness with a limited complexity increase. We will also discuss the channel estimation and the receiver adaptivity to time-varying channel conditions as well as the problem of the frequency acquisition and tracking. Finally, a new two-dimensional (polarization/time) differential encoding rule is proposed to overcome a polarization-ambiguity problem. In the numerical results, the receiver performance will be assessed versus the receiver complexity

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Channel estimation techniques for filter bank multicarrier based transceivers for next generation of wireless networks

A dissertation submitted to Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Master of Science in Engineering (Electrical and Information Engineering), August 2017The fourth generation (4G) of wireless communication system is designed based on the principles of cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) where the cyclic prefix (CP) is used to combat inter-symbol interference (ISI) and inter-carrier interference (ICI) in order to achieve higher data rates in comparison to the previous generations of wireless networks. Various filter bank multicarrier systems have been considered as potential waveforms for the fast emerging next generation (xG) of wireless networks (especially the fifth generation (5G) networks). Some examples of the considered waveforms are orthogonal frequency division multiplexing with offset quadrature amplitude modulation based filter bank, universal filtered multicarrier (UFMC), bi-orthogonal frequency division multiplexing (BFDM) and generalized frequency division multiplexing (GFDM). In perfect reconstruction (PR) or near perfect reconstruction (NPR) filter bank designs, these aforementioned FBMC waveforms adopt the use of well-designed prototype filters (which are used for designing the synthesis and analysis filter banks) so as to either replace or minimize the CP usage of the 4G networks in order to provide higher spectral efficiencies for the overall increment in data rates. The accurate designing of the FIR low-pass prototype filter in NPR filter banks results in minimal signal distortions thus, making the analysis filter bank a time-reversed version of the corresponding synthesis filter bank. However, in non-perfect reconstruction (Non-PR) the analysis filter bank is not directly a time-reversed version of the corresponding synthesis filter bank as the prototype filter impulse response for this system is formulated (in this dissertation) by the introduction of randomly generated errors. Hence, aliasing and amplitude distortions are more prominent for Non-PR. Channel estimation (CE) is used to predict the behaviour of the frequency selective channel and is usually adopted to ensure excellent reconstruction of the transmitted symbols. These techniques can be broadly classified as pilot based, semi-blind and blind channel estimation schemes. In this dissertation, two linear pilot based CE techniques namely the least square (LS) and linear minimum mean square error (LMMSE), and three adaptive channel estimation schemes namely least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS) are presented, analyzed and documented. These are implemented while exploiting the near orthogonality properties of offset quadrature amplitude modulation (OQAM) to mitigate the effects of interference for two filter bank waveforms (i.e. OFDM/OQAM and GFDM/OQAM) for the next generation of wireless networks assuming conditions of both NPR and Non-PR in slow and fast frequency selective Rayleigh fading channel. Results obtained from the computer simulations carried out showed that the channel estimation schemes performed better in an NPR filter bank system as compared with Non-PR filter banks. The low performance of Non-PR system is due to the amplitude distortion and aliasing introduced from the random errors generated in the system that is used to design its prototype filters. It can be concluded that RLS, NLMS, LMS, LMMSE and LS channel estimation schemes offered the best normalized mean square error (NMSE) and bit error rate (BER) performances (in decreasing order) for both waveforms assuming both NPR and Non-PR filter banks. Keywords: Channel estimation, Filter bank, OFDM/OQAM, GFDM/OQAM, NPR, Non-PR, 5G, Frequency selective channel.CK201

Digital Signal Processing for Coherent Transceivers Employing Multilevel Formats

Digital coherent transceivers have revolutionized optical fiber communications due to their superior performance offered compared to intensity modulation and direct detection based alternatives. As systems employing digital coherent transceivers seek to approach their information theoretic capacity, the use of multilevel modulation formats combined with appropriate forward error correction becomes essential. Given this context, in this tutorial paper, we therefore explore the digital signal processing (DSP) utilized in a coherent transceiver with a focus on multilevel modulation formats. By way of an introduction, we open by discussing the photonic technology required to realize a coherent transceiver. After discussing this interface between the analog optical channel and the digital domain, the rest of the paper is focused on DSP. We begin by discussing algorithms that correct for imperfections in the optical to digital conversion, including IQ imbalance and timing skew. Next, we discuss channel equalization including means for their realization for both quasi-static and dynamic channel impairments. Synchronization algorithms that correct for the difference between the transmitter and receiver oscillators both optical and electrical are then discussed and issues associated with symbol decoding highlighted. For most of the cases, we start with polarization division multiplexed quadrature phase-shift keying (PDM-QPSK) format as a basis and then discuss the extension to allow for high order multilevel formats. Finally, we conclude by discussing some of the open research challenges in the field.This work was supported in part by the EU project ICONE (608099) and EPSRC through INSIGHT (EP/L026155/2) and UNLOC (EP/J017582/1)

Complex-valued Adaptive Digital Signal Enhancement For Applications In Wireless Communication Systems

In recent decades, the wireless communication industry has attracted a great deal of research efforts to satisfy rigorous performance requirements and preserve high spectral efficiency. Along with this trend, I/Q modulation is frequently applied in modern wireless communications to develop high performance and high data rate systems. This has necessitated the need for applying efficient complex-valued signal processing techniques to highly-integrated, multi-standard receiver devices. In this dissertation, novel techniques for complex-valued digital signal enhancement are presented and analyzed for various applications in wireless communications. The first technique is a unified block processing approach to generate the complex-valued conjugate gradient Least Mean Square (LMS) techniques with optimal adaptations. The proposed algorithms exploit the concept of the complex conjugate gradients to find the orthogonal directions for updating the adaptive filter coefficients at each iteration. Along each orthogonal direction, the presented algorithms employ the complex Taylor series expansion to calculate time-varying convergence factors tailored for the adaptive filter coefficients. The performance of the developed technique is tested in the applications of channel estimation, channel equalization, and adaptive array beamforming. Comparing with the state of the art methods, the proposed techniques demonstrate improved performance and exhibit desirable characteristics for practical use. The second complex-valued signal processing technique is a novel Optimal Block Adaptive algorithm based on Circularity, OBA-C. The proposed OBA-C method compensates for a complex imbalanced signal by restoring its circularity. In addition, by utilizing the complex iv Taylor series expansion, the OBA-C method optimally updates the adaptive filter coefficients at each iteration. This algorithm can be applied to mitigate the frequency-dependent I/Q mismatch effects in analog front-end. Simulation results indicate that comparing with the existing methods, OBA-C exhibits superior convergence speed while maintaining excellent accuracy. The third technique is regarding interference rejection in communication systems. The research on both LMS and Independent Component Analysis (ICA) based techniques continues to receive significant attention in the area of interference cancellation. The performance of the LMS and ICA based approaches is studied for signals with different probabilistic distributions. Our research indicates that the ICA-based approach works better for super-Gaussian signals, while the LMS-based method is preferable for sub-Gaussian signals. Therefore, an appropriate choice of interference suppression algorithms can be made to satisfy the ever-increasing demand for better performance in modern receiver design