111 research outputs found

    Algorithms and structures for long adaptive echo cancellers

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    The main theme of this thesis is adaptive echo cancellation. Two novel independent approaches are proposed for the design of long echo cancellers with improved performance. In the first approach, we present a novel structure for bulk delay estimation in long echo cancellers which considerably reduces the amount of excess error. The miscalculation of the delay between the near-end and the far-end sections is one of the main causes of this excess error. Two analyses, based on the Least Mean Squares (LMS) algorithm, are presented where certain shapes for the transitions between the end of the near-end section and the beginning of the far-end one are considered. Transient and steady-state behaviours and convergence conditions for the proposed algorithm are studied. Comparisons between the algorithms developed for each transition are presented, and the simulation results agree well with the theoretical derivations. In the second approach, a generalised performance index is proposed for the design of the echo canceller. The proposed algorithm consists of simultaneously applying the LMS algorithm to the near-end section and the Least Mean Fourth (LMF) algorithm to the far-end section of the echo canceller. This combination results in a substantial improvement of the performance of the proposed scheme over both the LMS and other algorithms proposed for comparison. In this approach, the proposed algorithm will be henceforth called the Least Mean Mixed-Norm (LMMN) algorithm. The advantages of the LMMN algorithm over previously reported ones are two folds: it leads to a faster convergence and results in a smaller misadjustment error. Finally, the convergence properties of the LMMN algorithm are derived and the simulation results confirm the superior performance of this proposed algorithm over other well known algorithms

    Orthogonal transmultiplexers : extensions to digital subscriber line (DSL) communications

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    An orthogonal transmultiplexer which unifies multirate filter bank theory and communications theory is investigated in this dissertation. Various extensions of the orthogonal transmultiplexer techniques have been made for digital subscriber line communication applications. It is shown that the theoretical performance bounds of single carrier modulation based transceivers and multicarrier modulation based transceivers are the same under the same operational conditions. Single carrier based transceiver systems such as Quadrature Amplitude Modulation (QAM) and Carrierless Amplitude and Phase (CAP) modulation scheme, multicarrier based transceiver systems such as Orthogonal Frequency Division Multiplexing (OFDM) or Discrete Multi Tone (DMT) and Discrete Subband (Wavelet) Multicarrier based transceiver (DSBMT) techniques are considered in this investigation. The performance of DMT and DSBMT based transceiver systems for a narrow band interference and their robustness are also investigated. It is shown that the performance of a DMT based transceiver system is quite sensitive to the location and strength of a single tone (narrow band) interference. The performance sensitivity is highlighted in this work. It is shown that an adaptive interference exciser can alleviate the sensitivity problem of a DMT based system. The improved spectral properties of DSBMT technique reduces the performance sensitivity for variations of a narrow band interference. It is shown that DSBMT technique outperforms DMT and has a more robust performance than the latter. The superior performance robustness is shown in this work. Optimal orthogonal basis design using cosine modulated multirate filter bank is discussed. An adaptive linear combiner at the output of analysis filter bank is implemented to eliminate the intersymbol and interchannel interferences. It is shown that DSBMT is the most suitable technique for a narrow band interference environment. A blind channel identification and optimal MMSE based equalizer employing a nonmaximally decimated filter bank precoder / postequalizer structure is proposed. The performance of blind channel identification scheme is shown not to be sensitive to the characteristics of unknown channel. The performance of the proposed optimal MMSE based equalizer is shown to be superior to the zero-forcing equalizer

    SIMULATION OF ADAPTIVE CHANNEL EQUALIZATION FOR BPSK,QPSK AND 8-PSK SCHEMES

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    The distortion and inter symbol interference caused by multipath effects of channel degrades the quality of signal transmission in transmission system of digital baseband. Adaptive channel equalization is used commonly to compensate these effects so as to increase the reliability of propagation. Recursive Least Squares (RLS) algorithm is most commonly used adaptive algorithm because of its simplicity and fast convergence. In this work, simulation model of finite impulse response adaptive equalizer based on RLS is developed to reduce distortion caused by channel. The constellation diagram before and after equalization is obtained. It is observed that bit error rate is decreased by fifty percent after equalization. Hence this shows that the algorithm appears to reduce channel effects effectively and achieves channel equalization

    Tree-Structured Nonlinear Adaptive Signal Processing

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    In communication systems, nonlinear adaptive filtering has become increasingly popular in a variety of applications such as channel equalization, echo cancellation and speech coding. However, existing nonlinear adaptive filters such as polynomial (truncated Volterra series) filters and multilayer perceptrons suffer from a number of problems. First, although high Order polynomials can approximate complex nonlinearities, they also train very slowly. Second, there is no systematic and efficient way to select their structure. As for multilayer perceptrons, they have a very complicated structure and train extremely slowly Motivated by the success of classification and regression trees on difficult nonlinear and nonparametfic problems, we propose the idea of a tree-structured piecewise linear adaptive filter. In the proposed method each node in a tree is associated with a linear filter restricted to a polygonal domain, and this is done in such a way that each pruned subtree is associated with a piecewise linear filter. A training sequence is used to adaptively update the filter coefficients and domains at each node, and to select the best pruned subtree and the corresponding piecewise linear filter. The tree structured approach offers several advantages. First, it makes use of standard linear adaptive filtering techniques at each node to find the corresponding Conditional linear filter. Second, it allows for efficient selection of the subtree and the corresponding piecewise linear filter of appropriate complexity. Overall, the approach is computationally efficient and conceptually simple. The tree-structured piecewise linear adaptive filter bears some similarity to classification and regression trees. But it is actually quite different from a classification and regression tree. Here the terminal nodes are not just assigned a region and a class label or a regression value, but rather represent: a linear filter with restricted domain, It is also different in that classification and regression trees are determined in a batch mode offline, whereas the tree-structured adaptive filter is determined recursively in real-time. We first develop the specific structure of a tree-structured piecewise linear adaptive filter and derive a stochastic gradient-based training algorithm. We then carry out a rigorous convergence analysis of the proposed training algorithm for the tree-structured filter. Here we show the mean-square convergence of the adaptively trained tree-structured piecewise linear filter to the optimal tree-structured piecewise linear filter. Same new techniques are developed for analyzing stochastic gradient algorithms with fixed gains and (nonstandard) dependent data. Finally, numerical experiments are performed to show the computational and performance advantages of the tree-structured piecewise linear filter over linear and polynomial filters for equalization of high frequency channels with severe intersymbol interference, echo cancellation in telephone networks and predictive coding of speech signals

    Constrained Linear and Non-Linear Adaptive Equalization Techniques for MIMO-CDMA Systems

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    Researchers have shown that by combining multiple input multiple output (MIMO) techniques with CDMA then higher gains in capacity, reliability and data transmission speed can be attained. But a major drawback of MIMO-CDMA systems is multiple access interference (MAI) which can reduce the capacity and increase the bit error rate (BER), so statistical analysis of MAI becomes a very important factor in the performance analysis of these systems. In this thesis, a detailed analysis of MAI is performed for binary phase-shift keying (BPSK) signals with random signature sequence in Raleigh fading environment and closed from expressions for the probability density function of MAI and MAI with noise are derived. Further, probability of error is derived for the maximum Likelihood receiver. These derivations are verified through simulations and are found to reinforce the theoretical results. Since the performance of MIMO suffers significantly from MAI and inter-symbol interference (ISI), equalization is needed to mitigate these effects. It is well known from the theory of constrained optimization that the learning speed of any adaptive filtering algorithm can be increased by adding a constraint to it, as in the case of the normalized least mean squared (NLMS) algorithm. Thus, in this work both linear and non-linear decision feedback (DFE) equalizers for MIMO systems with least mean square (LMS) based constrained stochastic gradient algorithm have been designed. More specifically, an LMS algorithm has been developed , which was equipped with the knowledge of number of users, spreading sequence (SS) length, additive noise variance as well as MAI with noise (new constraint) and is named MIMO-CDMA MAI with noise constrained (MNCLMS) algorithm. Convergence and tracking analysis of the proposed algorithm are carried out in the scenario of interference and noise limited systems, and simulation results are presented to compare the performance of MIMO-CDMA MNCLMS algorithm with other adaptive algorithms

    Estimation and detection of transmission line characteristics in the copper access network

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    The copper access-network operators face the challenge of developing and maintaining cost-effective digital subscriber line (DSL) services that are competitive to other broadband access technologies. The way forward is dictated by the demand of ever increasing data rates on the twisted-pair copper lines. To meet this demand, a relocation of the DSL transceivers in cabinets closer to the customers are often necessary combined with a joint expansion of the accompanying optical-fiber backhaul network. The equipment of the next generation copper network are therefore becoming more scattered and geographically distributed, which increases the requirements of automated line qualification with fault detection and localization. This scenario is addressed in the first five papers of this dissertation where the focus is on estimation and detection of transmission line characteristics in the copper access network. The developed methods apply model-based optimization with an emphasis on using low-order modeling and a priori information of the given problem. More specifically, in Paper I a low-order and causal cable model is derived based on the Hilbert transform. This model is successfully applied in three contributions of this dissertation. In Paper II, a class of low-complexity unbiased estimators for the frequency-dependent characteristic impedance is presented that uses one-port measurements only. The so obtained characteristic impedance paves the way for enhanced time domain reflectometry (a.k.a. TDR) on twisted-pair lines. In Paper III, the problem of estimating a nonhomogeneous and dispersive transmission line is investigated and a space-frequency optimization approach is developed for the DSL application. The accompanying analysis shows which parameters are of interest to estimate and further suggests the introduction of the concept capacitive length that overcomes the necessity of a priori knowledge of the physical line length. In Paper IV, two methods are developed for detection and localization of load coils present in so-called loaded lines. In Paper V, line topology identification is addressed with varying degree of a priori information. In doing so, a model-based optimization approach is employed that utilizes multi-objective evolutionary computation based on one/two-port measurements. A complement to transceiver relocation that potentially enhances the total data throughput in the copper access network is dynamic spectrum management (DSM). This promising multi-user transmission technique aims at maximizing the transmission rates, and/or minimizing the power consumption, by mitigating or cancelling the dominating crosstalk interference between twisted-pair lines in the same cable binder. Hence the spectral utilization is improved by optimizing the transmit signals in order to minimize the crosstalk interference. However, such techniques rely on accurate information of the (usually) unknown crosstalk channels. This issue is the main focus of Paper VI and VII of this dissertation in which Paper VI deals with estimation of the crosstalk channels between twisted-pair lines. More specifically, an unbiased estimator for the square-magnitude of the crosstalk channels is derived from which a practical procedure is developed that can be implemented with standardized DSL modems already installed in the copper access network. In Paper VII the impact such a non-ideal estimator has on the performance of DSM is analyzed and simulated. Finally, in Paper VIII a novel echo cancellation algorithm for DMT-based DSL modems is presented
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