A robust detector for multicarrier spread spectrum transmission over partially jammed channels

Multicarrier spread spectrum (MC-SS) is an alternative to the conventional spread spectrum (SS) techniques that behave significantly better when the system is subject to narrow- or partial-band interference. However, successful implementation of the optimum detector requires knowledge of noise and interference variance in each subcarrier band. We propose a suboptimal detector for MC-SS that keeps the significant gain of MC-SS over the conventional SS, with a relatively low loss compared with the optimum MC-SS detector. Theoretical analysis and computer simulations that corroborate the theory are presented.     

An IIR adaptive line enhancer with controlled bandwidth

A previously proposed IIR adaptive line enhancer (ALE) is explored further. We propose a mechanism for controlling the bandwidth of the ALE in an adaptive manner. The proposed mechanism increases the bandwidth of the ALE whenever its peak is not close to a spectral line of the input signal to assure its fast convergence and decreases the bandwidth of the ALE when its peak matches a spectral line of the input signal to enhance that spectral line further. In addition, a new algorithm for robust adaptation of the ALE is proposed     

Analysis and efficient implementation of partitioned block LMSadaptive filters

The frequency domain block LMS (FBLMS) algorithm used for efficient implementation of adaptive filters whose length may exceed a few thousands of taps is explored further by Asharif et al. (1980). To reduce the latency of such filters, it has been proposed that the adaptive filter convolution sum be partitioned into a few smaller sums and then the FBLMS be applied. We show that the scheme proposed by Asharif et al. suffers from a serious eigenvalue spread problem. We identify the source of this problem and propose a solution to that     

On statistical efficiency of the LMS algorithm in system modeling

The conventional least-mean-square (LMS) algorithm is compared with the ideal LMS/Newton (ILMSN) algorithm. It is shown that, although under certain conditions, for similar misadjustment, the output mean-square error (MSE) of the ILMSN algorithm may converge much faster than the MSE of the LMS algorithm, the difference between the two algorithms may not be that great if misalignments of the adaptive filter tap gains are compared. Analytical results are presented, with computer simulations that support their validity     

Fast LMS/Newton Algorithms for Stereophonic Acoustic Echo Cancelation

This paper presents a new class of adaptive filtering algorithms to solve the stereophonic acoustic echo cancelation (AEC) problem in teleconferencing systems. While stereophonic AEC may be seen as a simple generalization of the well-known single-channel AEC, it is a fundamentally far more complex and challenging problem to solve. The main reason being the strong cross correlation that exists between the two input audio channels. In the past, nonlinearities have been introduced to reduce this correlation. However, nonlinearities bring with it additional harmonics that are undesirable. We propose an elegant linear technique to decorrelate the two-channel input signals and thus avoid the undesirable nonlinear distortions. We derive two low complexity adaptive algorithms based on the two-channel gradient lattice algorithm. The models assume the input sequences to the adaptive filters to be autoregressive (AR) processes whose orders are much lower than the lengths of the adaptive filters. This results in an algorithm, whose complexity is only slightly higher than the normalized least-mean-square (NLMS) algorithm; the simplest adaptive filtering method. Simulation results show that the proposed algorithms perform favorably when compared with the state-of-the-art algorithms.     

Filter Bank Spectrum Sensing for Cognitive Radios

The primary task in any cognitive radio (CR) network is to dynamically explore the radio spectrum and reliably determine portion(s) of the frequency band that may be used for the communication link(s). Accordingly, each CR node in the network has to be equipped with a spectrum analyzer. In this paper, we propose filter banks as a tool for spectrum sensing in CR systems. Various choices of filter banks are suggested and their performance are evaluated theoretically and through numerical examples. Moreover, the proposed spectrum analyzer is contrasted with the Thomson's multitaper (MT) method - a method that in the recent literature has been recognized as the best choice for spectrum sensing in CR systems. A novel derivation of the MT method that facilitates our comparisons as well as reveals an important aspect of the MT method that has been less emphasized in the recent literature is also presented.     

Channel equalization via channel identification: algorithms andsimulation results for rapidly fading HF channels

The problem of channel equalization via channel identification (CEQCID) that has previously been considered by a handful of researchers is explored further. An efficient algorithm for mapping the channel parameters to the equalizers coefficients is proposed. The proposed scheme is compared with a lattice least squares (LS) based receivers. For the particular application of the high frequency (HF) radio channels, we find that the CEQCID has lower computational complexity. In terms of the tracking performance, also, the CEQCID has been found to be superior to the LS based receivers. We emphasize on the implementation of a fractionally tap-spaced decision feedback equalizer (DFE) and compare that with the T-spaced DFE. We show that the former is a better choice for the multipath HF channels     

Variable-step-size LMS algorithm: new developments and experiments

The variable-step-size least-mean-square (VSLMS) algorithm is explored and adopted for tracking of time-varying environments. Two implementations of the VSLMS algorithm are proposed. The emphasis is on implementations sizes with different step sizes at various taps of the adaptive filter. General analysis of the VSLMS algorithm appears to be somewhat involved. However, for one implementation a limited analysis of the algorithm is found possible. For this implementation it is shown that, when the input samples to the adaptive filter are zero-mean, Gaussian and uncorrelated with one another, the VSLMS algorithm can adapt itself to select the optimum set of step sizes which results in the best-tracking performance. Simulation experiments with the VSLMS algorithm show that, under fairly mild conditions, both of the proposed implementations adapt toward the optimum step sizes     

A novel fast approach for estimating error propagation in decisionfeedback detectors

The study of error-burst statistics is important for all detection systems, and more so for the decision feedback class. In data storage applications, many detection systems use decision feedback in one form or another. Fixed-delay tree search with decision feedback (FDTS/DF) and decision feedback equalization (DFE) are the direct forms, whereas the partial response detectors such as the reduced state sequence estimator (RSSE) and noise predictive maximum likelihood (NPML) detectors are the other forms. Although DF reduces the system complexity, it is inevitably linked with error propagation (EP), which can be quantified using error-burst statistics. Analytical evaluation of these statistics is difficult, if not impossible, because of the complexity of the problem. Hence, the usual practice is to use computer simulations. However, the computational time in traditional bit-by-bit simulations can be prohibitive at meaningful signal-to-noise ratios. In this paper, we propose a novel approach for fast estimation of error-burst statistics in FDTS/DF detectors, which is also applicable to other detection systems. In this approach, error events are initiated more frequently than natural by artificially injecting noise samples. These noise samples are generated using a transformation that results in significant reduction in computational complexity. Simulation studies show that the EP performance obtained by the proposed method matches closely with those obtained by bit-by-bit simulations, while saving as much as 99% of simulation time     

Subband adaptive filtering with real-valued subband signals foracoustic echo cancellation

Adaptive filtering in subbands was originally proposed to overcome the limitations of conventional least-mean-square (LMS) algorithms. In general, subband adaptive filters offer computational savings, as well as faster convergence over the conventional LMS algorithm. However, improvements to current subband adaptive filters could be further enhanced by a more elegant choice of their design/structure. Classical subband adaptive filters employ DFT-based analysis and synthesis filter banks which results in subband signals that are complex-valued. The authors modify the structure of subband adaptive filters by using single-sideband (SSB) modulated analysis and synthesis filter banks, which result in subband signals that are real-valued. This simplifies the realisation of subband adaptive filters