Edge computing and IoT devices may have energy constraints that make it challenging to execute complex algorithms. In this paper, we propose the use of Sums of signed Powers of Two (SoPoT) as a quantization method for MultiLayer Perceptron (MLP) Neural Networks (NN) to reduce the computational burden. The so-called Matching Pursuits with Generalized Bit Planes (MPGBP) algorithm efficiently quan-tizes the coefficients of the MLP into SoPoT. It provides a clear tradeoff between the number of Signed Powers of Two (SPT) and the approximation quality, which is advantageous for computation under limited power availability. We evaluate the quantization impact in a detection problem for Multiple-Input Multiple-Output (MIMO) communication, where the Bit Error Rate (BER) for the detection using the SoPoT quantization and infinite precision are compared. The results show that the proposition reduces the model’s computational burden at the expense of performance losses, resulting in a tradeoff between computational cost and performance.

Effective noise suppression is crucial for the subsequent interpretation tasks of SAR imagery. Traditional SAR image processing techniques often overlook the coherent nature of noise, leading to a loss of vital detail during filtering. With advancements in deep-learning, significant strides have been made in image processing. However, existing deep-learning methods do not fully leverage the imaging mechanisms of SAR, resulting in a lack of specificity and interpretability in the filtering process. To balance noise reduction with detail preservation and to address the “black box” issue in filtering, we propose an interpretable filtering method that employs a correlation-based upward search for density peaks. Initially, we develop an MeanShift-Markov Random Fields filter (MS-MRF) that integrates MeanShift with Markov Random Fields (MRF) in the joint spatial-spectral domain, ensuring both correlation and detail preservation; the derivation of the MS-MRF filter is rigorously grounded in mathematical theory. Subsequently, we integrate MS-MRF with convolutional operations in deep-learning to create a novel convolutional filter, Interpretable MS-MRF Convolution (IMMC), which enhances the model’s interpretability, noise reduction capabilities, and detail retention. Extensive experiments demonstrate that our method outperforms State of the art(SOTA) SAR denoising techniques, achieving an average SSIM of over 85.00% and an average PSNR exceeding 35.00dB across synthetic datasets with varying noise levels, showing significant improvements in noise suppression, detail preservation, and interpretability.

Waveform generation is essential for studying signal propagation and channel characteristics, particularly for objects that are conceptualized but still need to be operational. We introduce a comprehensive guide on creating synthetic signals using channel and delay coefficients derived from the Quasi-Deterministic Radio Channel Generator (QuaDRiGa), which is recognized as a 3GPP-3D and 3GPP 38.901 reference implementation. The effectiveness of the proposed synthetic waveform generation method is validated through accurate estimation of code delay and Doppler shift. This validation is achieved using both the parallel code phase search technique and the conventional tracking method applied to satellites. As the method of integrating channel and delay coefficients to create synthetic waveforms is the same for satellite, HAPS, and gNB PRS, validating this method on synthetic satellite signals could potentially be extended to HAPS and gNB PRS as well. This study could significantly contribute to the field of heterogeneous navigation systems.

This letter presents a Direction-of-arrival (DOA) estimation algorithm in passive sensing systems with Reconfigurable Intelligent Surface (RIS). In order to improve the estimation accuracy in non-Gaussian noise and access point (AP) interference environments, a joint logarithmic function and atomic norm (LFAN) constrained DOA estimation method is proposed, analyzed, and discussed in detail. The proposed LFAN effectively mitigates impulsive noise and AP interference to achieve precise estimation through a minimization problem using logarithmic function and atomic norm constraints. Simulation results show that the proposed LFAN outperforms existing algorithms in terms of estimation performance.

Reconfigurable Intelligent Surface (RIS) can obtain huge passive beamforming gains. However, due to imperfect hardware and deployment environments, RIS is subject to hardware impairments (HWI) and partial elements blockage, resulting in a significant loss of gain. Hence, RIS array diagnosis is of great significance for normal operations of the RIS system. We consider a RIS assisted millimeter-wave (mmWave) communication system where some RIS elements suffer from HWI. A conjugate gradient complex soft threshold (CG-CST) algorithm is proposed to diagnose the RIS array, reducing the overhead and increasing the diagnostic accuracy. Furthermore, the proposed CG-CST algorithm is effective in both single-input single-output (SISO) systems and systems with multiple antennas. Numerical results confirm that the presented CG-CST algorithm has superior performance compared to existing methods.

A general and efficient method for scaling window sidelobe magnitude has been reported by Lim et al. Although the method always converges in practice, a rigorous proof of convergence is unavailable. In this paper, we introduce a new technique with guaranteed convergence for window sidelobe magnitude reduction. Without further modifications, the convergence speed of this new algorithm is quite the same as that of the previous one. Modifications aimed at speeding up convergence while maintaining the guaranteed convergence property are also presented.

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio.

In this work, we propose a voxel-based single-stage fine-grained and efficient point cloud 3D object detection algorithm to address the inadequate granularity in point cloud feature extraction tasks and the imbalance between efficiency and accuracy in single-stage point cloud 3D object detection scenarios. We develop a lightweight multibranch cross-sparse convolution network (LMCCN) that is designed to preserve the feature granularity of the original point cloud while achieving enhanced extraction efficiency. Additionally, we introduce a compact fine-grained self-attention augmented bird’s eye view (BEV) feature extraction module (CFSAM). This module aims to further refine BEV features, enabling the acquisition of both locally and globally enhanced features and thereby augmentingthe perceptual capabilities of the constructed model. Without bells and whistles, the proposed method attains excellent performance on many autonomous driving benchmarks, with detection accuracies of up to 81.67% on KITTI, 72.74% on ONCE, and 84.00% on nuScenes. Moreover, it reaches a peak detection speed of 46.08 FPS, effectively balancing accuracy with speed.

In recent years, deep learning has been widely used in remote sensing, especially in the field of synthetic aperture radar (SAR) image target detection. However, all of these deep learning models continue increasing the network’s depth and width without maintaining a good balance between accuracy and speed. Therefore, in this article, we propose a hybrid representation learning-enhanced SAR target detection algorithm based on the unique features of SAR images from a lightweight perspective called HRLE-SARDet. First, we design a lightweight and scattering feature extraction backbone that is more suitable for SAR image data. Second, for the multiscale feature discrepancy, we design a new multiscale feature fusion neck. Next, to better extract the scattering information from small targets of SAR images and improve the detection accuracy, we design a lightweight hybrid representation learning enhancement module. Finally, to better fit target detection for SAR image datasets, we redesign a more flexible loss function, which allows for an easy adjustment of the importance of polynomial bases according to the target task and dataset. Extensive experimental results on three SAR image ship target datasets (SSDD, AIR-SARShip-2.0, and HRSID) and a newly released large multiclass target SAR dataset (MSAR-1.0) show that our HRLE-SARDet achieves 98.4%, 79.2%, 92.5%, and 88.4% mean average precision (mAP) with only 1.09 M parameters and 2.5 G floating-point operations (FLOPs) on the SSDD, AIR-SARShip-2.0, HRSID, and MSAR-1.0 datasets, respectively, which is an excellent performance.

A rectangular-window sidelobe magnitude reduction method by widening the main lobe width was proposed by Saramäki. A technique for trading off main lobe width against sidelobe magnitude for any arbitrary window was reported by Lim et al.; a fast convergence algorithm for its implementation was proposed by the same authors in another article, where the derivatives of the window function are expressed in Chebyshev polynomials which have high arithmetic complexity. All the coefficients of a rectangular-window are equal; this special property is exploited, in this paper, for deriving the window function’s derivatives without the use of Chebyshev polynomials resulting in a great reduction in the arithmetic complexity.

A technique for trading off the main lobe width against sidelobe magnitude for any arbitrary window was reported in Lim et al. and subsequently, a fast convergence method for its implementation was proposed by the same authors. These methods require the computation of derivatives involving the evaluation of trigonometric and hyperbolic functions. In this paper, we show that the derivatives can be computed without evaluating trigonometric and hyperbolic functions if the window function is a sum of exponentials such as a Fourier series.

Many types of windows have been designed in the past decades for various applications. Each window type has its own specific characteristics. In this letter, we present a general technique for trading off main lobe width against sidelobe magnitude for any arbitrary window while keeping the number of sidelobe peaks and their relative magnitudes unchanged although their exact locations and magnitudes are changed.

Windows such as Dolph-Chebyshev window and Kaiser window are adjustable, whereas windows such as Hamming window and Blackman window are traditionally not adjustable. In [1], a technique for trading off main lobe width against sidelobe magnitude for any arbitrary window, including the traditionally non-adjustable windows, was presented. However, the method in [1] requires a large number of iterations if the specification is very tight. Developed based on a new perspective on the window adjustment principle, a new method to achieve the same adjustment capability as in [1], but at a very much fewer iterations is presented in this letter. Our new method is particular useful if the specification is very tight.

In recent years, the interest in kernel methods has increased exponentially, mainly due to applications including phenomena that cannot be well modeled by linear systems. Furthermore, the demand for high-speed communications and improvement in computer capacity to process information leads to the exploration of more sophisticated resources. The kernel adaptive filtering is an alternative to deal with nonlinear problems. In this paper, we propose the data selection kernel conjugate gradient (DS-KCG) algorithm, which is capable of classifying whether the currently available data brings sufficient innovation to update the filter coefficients. The data could be discarded, avoiding extra computation and performance degradation.

Recently, the improved simple set‐membership affine projection (IS‐SM‐AP) algorithm has been proposed in order to exploit sparsity in system models. Although its update equation resembles that of the set‐membership affine projection (SM‐AP) algorithm, the IS‐SM‐AP algorithm has two fundamental advantages over the SM‐AP algorithm: (i) it can exploit sparsity in system models and (ii) its computational complexity is lower. Up to now, the properties of this algorithm have been addressed only through numerical simulations, and no analytical study has been presented. To fill this gap, in this study, the authors analyse the steady‐state mean squared error of the IS‐SM‐AP algorithm using the energy conservation method. Furthermore, some important implementation issues are addressed, and a time‐varying parameter for the discard function is proposed. Finally, the authors present numerical results corroborating the theoretical analysis and the effectiveness of the proposed time‐varying parameter.

As wireless services proliferate, the demand for available spectrum also grows. As a result, spectral efficiency is still an issue being addressed by many researchers aiming at improving the quality of service to a growing number of users. Massive multiple-input multiple-output (MIMO) has been presented as an attractive technology for the next wireless systems since it can alleviate the expected spectral shortage. Nevertheless, such a technique requires a dedicated chain of radio frequency (RF) components for each antenna element which result in high costs at base station (BS) side. To reduce the number of RF chains, we propose several transmit antenna selection schemes aiming at minimizing the mean square reception error and also reducing the transmission power which is one of the main contributions of our work. The proposed strategies are inspired by the matching pursuit technique and its quantized version, named matching pursuit with generalized bit planes. The presented results show that reliable reception can be accomplished with low computationally intensive algorithms for antenna selection.

Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both convergence speed and steady-state mean-squared error is the choice of the so-called constraint vector. Optimal constraint vectors were recently proposed relying on convex optimization tools, which might sometimes lead to prohibitive computational burden. This paper proposes a convex combination of simpler constraint vectors whose performance approaches the optimal solution closely, utilizing much fewer computations. Some illustrative examples confirm that the sub-optimal solution follows the accomplishments of the optimal one.

The huge volume of data that are available today requires data-selective processing approaches that avoid the costs in computational complexity via appropriately treating the non-innovative data. In this paper, extensions of the well-known adaptive filtering LMS-Newton and LMS-Quasi-Newton Algorithms are developed that enable data selection while also addressing the censorship of outliers that emerge due to high measurement errors. The proposed solutions allow the prescription of how often the acquired data are expected to be incorporated into the learning process based on some a priori information regarding the environment. Simulation results on both synthetic and real-world data verify the effectiveness of the proposed algorithms that may achieve significant reductions in computational costs without sacrificing estimation accuracy due to the selection of the data.

This paper aims at studying the achievable data rate of discrete cosine transform (DCT)-based multicarrier modulation (MCM) systems. To this end, a general formulation is presented for the full transmission/reception process of data in Type-II even DCT and Type-IV even DCT-based systems. This paper focuses on the use of symmetric extension and zero padding as redundancy methods. Furthermore, three cases related to the channel order and the length of the redundancy are studied. In the first case, the channel order is less than or equal to the length of the redundancy. In the second and third cases, the channel order is greater than the length of the redundancy; the interference caused by the channel impulse response is calculated, and theoretical expressions for their powers are derived. These expressions allow studying the achievable data rate of the DCT-based MCM systems, besides enabling the comparison with the conventional MCM based on the discrete Fourier transform.

In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity. This work proposes a set of LMS-type algorithms, collectively called Feature LMS (F-LMS) algorithms, setting forth a hidden feature of the unknown parameters, which ultimately would improve convergence speed and steady-state mean-squared error. The key idea is to apply linear transformations, by means of the so-called feature matrices, to reveal the sparsity hidden in the coefficient vector, followed by a sparsity-promoting penalty function to exploit such sparsity. Some F-LMS algorithms for lowpass and highpass systems are also introduced by using simple feature matrices that require only trivial operations. Simulation results demonstrate that the proposed F-LMS algorithms bring about several performance improvements whenever the hidden sparsity of the parameters is exposed.

Recently, some online kernel principal component analysis (KPCA) techniques based on the generalized Hebbian algorithm (GHA) were proposed for use in large data sets, defining kernel components using concise dictionaries automatically extracted from data. This brief proposes two new online KPCA extraction algorithms, exploiting orthogonalized versions of the GHA rule. In both the cases, the orthogonalization of kernel components is achieved by the inclusion of some low complexity additional steps to the kernel Hebbian algorithm, thus not substantially affecting the computational cost of the algorithm. Results show improved convergence speed and accuracy of components extracted by the proposed methods, as compared with the state-of-the-art online KPCA extraction algorithms.

In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for sparse systems (S-RLS) and l0-norm Recursive least-Squares (l0-RLS), in order to exploit the sparsity of an unknown system. The first algorithm, applies a discard function on the weight vector to disregard the coefficients close to zero during the update process. The second algorithm, employs the sparsity-promoting scheme via some non-convex approximations to the l0-norm. In addition, we consider the respective versions of these algorithms in data-selective versions in order to reduce the update rate. Simulation results show similar performance when comparing the proposed algorithms with standard Recursive Least-Squares (RLS) algorithm while the proposed algorithms require lower computational complexity.

In this paper, we present an improved set-membership partial-update affine projection (I-SM-PUAP) algorithm, aiming at accelerating the convergence, and decreasing the update rates and the computational complexity of the set-membership partial-update affine projection (SM-PUAP) algorithm. To meet these targets, we constrain the weight vector perturbation to be bounded by a hypersphere instead of the threshold hyperplanes as in the standard algorithm. We use the distance between the present weight vector and the expected update in the standard set-membership affine projection (SM-AP) algorithm to construct the hypersphere. With this strategy, the new algorithm shows better behavior in the early iterations. Simulation results verify the excellent performance of the proposed algorithm related to the convergence rate and the required number of updates.

There are two main families of algorithms that tackle the problem of sparse system identification: the proportionate family and the one that employs sparsity-promoting penalty functions. Recently, a new approach was proposed with the l0-IPAPA algorithm, which combines proportionate updates with sparsity-promoting penalties. This paper proposes some modifications to the l0-IPAPA algorithm in order to decrease its computational complexity while preserving its good convergence properties. Among these modifications, the inclusion of a dataselection mechanism provides promising results. Some enlightening simulation results are provided in order to verify and compare the performance of the proposed algorithms.

The increasing scarcity in the available spectrum for wireless communication is one of the current bottlenecks impairing further deployment of services and coverage. The proper exploitation of white spaces in the radio spectrum requires fast, robust, and accurate methods for their detection. This paper proposes a new strategy to detect adaptively white spaces in the radio spectrum. Such strategy works in cognitive radio (CR) networks whose nodes perform spectrum sensing based on energy detection in a cooperative way or not. The main novelty of the proposal is the use of a cost-function that depends upon a single parameter which, by itself, contains the aggregate information about the presence or absence of primary users. The detection of white spaces based on this parameter is able to improve significantly the deflection coefficient associated with the detector, as compared to other state-of-the-art algorithms. In fact, simulation results show that the proposed algorithm outperforms by far other competing algorithms. For example, our proposal can yield a probability of miss-detection 20 times smaller than that of an optimal soft-combiner solution in a cooperative setup with a predefined probability of false alarm of 0.1.

We analyze two algorithms, viz. the affine projection algorithm for sparse system identification (APA-SSI) and the quasi APA-SSI (QAPA-SSI), regarding their stability and steady-state mean-squared error (MSE). These algorithms exploit the sparsity of the involved signals through an approximation of the l0norm. Such approach yields faster convergence and reduced steady-state MSE, as compared to algorithms that do not take the sparse nature of the signals into account. In addition, modeling sparsity via such approximation has been consistently verified to be superior to the widely used l1norm in several scenarios. In this paper, we show how to properly set the parameters of the two aforementioned algorithms in order to guarantee convergence, and we derive closed-form theoretical expressions for their steady-state MSE. A key conclusion from the proposed analysis is that the MSE of these two algorithms is a monotonically decreasing function of the sparsity degree. Simulation results are used to validate the theoretical findings.

We propose two versions of affine projection (AP) algorithms tailored for sparse system identification (SSI). Contrary to most adaptive filtering algorithms devised for SSI, which are based on the l1norm, the proposed algorithms rely on homotopic l0norm minimization, which has proven to yield better results in some practical contexts. The first proposal is obtained by direct minimization of the AP cost function with a penalty function based on the l0norm of the coefficient vector, whereas the second algorithm is a simplified version of the first proposal. Simulation results are presented in order to evaluate the performance of the proposed algorithms considering three different homotopies to the l0norm as well as competing algorithms.

Nowadays, most wireless communication systems utilize coherent detection which implies the necessity of multiplexing reference symbols between data-symbols for the purpose of channel estimation. In Long Term Evolution (LTE), these reference symbols can consume up to 14.2% of the total bandwidth. In this work, we search for an optimal pilot-symbol pattern design using a post-equalization Signal to Interference and Noise Ratio (SINR) framework. We show how close to optimally choose the distance between adjacent pilot-symbols and how to distribute the available power between pilot and data symbol at the same time. We confirm the performance of our analytical solution by means of simulation. Compared to a system with a fixed distance between pilot-symbol and unit power distribution, the proposed system configuration yields a gain of around 30% in terms of capacity.

Recent standards for cellular transmission systems offer a lot of flexibility, such as the choice of transmission modes, modulation alphabets, coding rates, and precoding matrices. Despite this trend, pilot-symbol patterns in today's standards remain fixed, although such an approach is suboptimal. In this paper, we show how to design optimal pilot-symbol patterns by maximizing an upper bound of a constrained capacity that takes channel estimation errors and Inter Carrier Interference into account. Furthermore, we propose adaptive pilot-symbol patterns that follow changing channel statistics. As a proof of concept, we present throughput simulation results of two competitive systems, a transmission system compliant with the Long Term Evolution (LTE)-standard and an improved system utilizing the proposed adaptive pilot patterns. The transmission system utilizing adaptive pilot patterns outperforms an LTE-standard compliant system in all considered scenarios. The throughput gain for a single input single output system ranges between 3% and 80%. For a 4 × 4 transmission system, the performance gain is significantly higher and can reach up to 850% compared to a conventional LTE system.

This article describes a physical-layer simulator for both uplink and downlink connections of LTE systems, whose performances are assessed by simulating standardized environments. The simulator is compliant with Release 9 of LTE standard and it is publicly available for educational purposes, allowing students and researchers to test the performance of Signal Processing and Digital Communications techniques in an easy-to-use MATLAB framework. Users may benefit from implemented features such as channel estimation using different demodulation reference signals, channel coding, equalization, multiple access schemes in which multiple cells are employed, as well as diversity, spatial multiplexing, and beamforming transmissions. As an example, we evaluate the impact on the performance of an uplink connection due to inaccuracy in channel estimation and multi-user interference. In addition, we include the evaluation of using diversity and spatial multiplexing transmissions on downlink connections.

Current Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) based systems for wireless communications enable to adjust power radiated at the pilot symbols. Under time-invariant channels, a power increase at the pilot symbols results in improved quality of the channel estimate. However, under time-variant channels, this is not necessarily the case. Due to the decreased temporal channel correlation, the channel estimation performance becomes saturated. If under time-variant channels more power is assigned to the data symbols, such approach increases inter layer interference due to the imperfect channel knowledge at the receiver. In this paper, we show how to distribute power among data and pilot symbols in a power efficient way. Using our proposed method, up to 50% of the transmit power can be saved in a 4x4 Long Term Evolution (LTE) downlink transmission at a Signal to Noise Ratio (SNR) of 20 dB.

Infrared focal-plane array (IRFPA) detectors suffer from fixed-pattern noise (FPN) that degrades image quality, which is also known as spatial nonuniformity. FPN is still a serious problem, despite recent advances in IRFPA technology. This paper proposes new scene-based correction algorithms for continuous compensation of bias and gain nonuniformity in FPA sensors. The proposed schemes use recursive least-square and affine projection techniques that jointly compensate for both the bias and gain of each image pixel, presenting rapid convergence and robustness to noise. The synthetic and real IRFPA videos experimentally show that the proposed solutions are competitive with the state-of-the-art in FPN reduction, by presenting recovered images with higher fidelity.

A new method for the design of finite impulse response (FIR) filters whose discrete coefficient space is the power-of-two space is presented. We employ a vector successive approximation technique successfully used in data compression algorithms to produce a design method with a very low computational complexity that generates filters with implementation cost as low as those obtained by other, much more complex, optimization methods.

This article presents a low-complexity parametric algorithm for the estimation of the carrier frequency offset (CFO) in orthogonal frequency division multiplexing systems. The proposed algorithm is equivalent to the Unitary ESPRIT algorithm, originally devised for estimating the direction-of-arrival of a wave front, now applied to the CFO scenario. It is verified that the proposed algorithm reduces the overall computational complexity in around 40%, while sustaining the error-rate performance achieved by the standard ESPRIT algorithm.

Among the adaptive filtering algorithms the set-membership affine projection (SM-AP) algorithm has the attractive feature of not trading off misadjustment with convergence speed. This paper presents an analysis of the steady-state mean-square error (MSE) of the SM-AP algorithm. Our analysis relies on the energy conservation method and does not assume a specific probability distribution for the input vector. Moreover, since the SM-AP algorithm with a fixed-modulus error-based constraint vector generalizes some important algorithms, such as the SM normalized least-mean-square (SM-NLMS) algorithm, the results can be directly applied to these algorithms. Simulation results confirm the accuracy of our analysis.

Multicarrier and single-carrier block-based transceivers with minimum redundancy have proved to be an alternative to classical orthogonal frequency-division multiplex (OFDM) and single-carrier with frequency-domain equalization (SC-FD) systems. In general, these minimum-redundancy transceivers have superior throughput performance than OFDM and SC-FD systems, requiring the same asymptotic complexity, viz. O(M log2M), for M data symbols. However, the previous proposals of such transceivers rely on the channel-state information (CSI) assumption. In addition, they also assume that the equalizer was previously designed, focusing on the equalization problem only. The aim of this work is to present some theoretical results related to the design of the equalizers that employ minimum redundancy, without assuming CSI. The key result of this paper is to show that it is possible to design those equalizers based on pilot information and using fast-converging iterative algorithms that require O(M log2M) operations per iteration.

Recent works have proposed practical zero-forcing (ZF) and linear minimum mean squared-error (LMMSE) solutions for fixed and memoryless block-based transceivers with minimum redundancy, using only half the amount of redundancy employed in standard systems. Their equalization processes require only O(M log2M) operations. However, it may be difficult to apply LMMSE equalizers with minimum redundancy in some practical systems, given their higher number of operations. This letter proposes novel suboptimal LMMSE equalizers with minimum redundancy that require the same amount of computations of ZF equalizers, with a mild decrease in the throughput performance when compared to the optimal LMMSE solution.

This paper proposes an affine projection adaptive filtering algorithm incorporating a data selection strategy based on the set-membership concept along with a partial update technique. The resulting algorithm is flexible in the sense that it allows more general tradeoff between speed of convergence and misadjustment while constraining the overall computational complexity. Simulation experiments in a typical echo cancellation environment confirm the effectiveness of the proposed algorithm.

In orthogonal frequency division multiplexing (OFDM), carrier frequency offset (CFO) must be mitigated since it generates interference between received symbols transmitted through different sub-carriers. This paper presents a new algorithm for CFO estimation with reduced computational complexity. The new approach is based on the segmentation of the input-signal autocorrelation matrix into the noise and signal subspaces, the latter being employed to estimate the desired CFO. Simulations validate the effectiveness of the new algorithm in comparison to the traditional parametric estimation technique ESPRIT.

This paper proposes a new semi-blind data-selective affine projection algorithm for channel equalization. This algorithm generalizes the concept of set-membership filtering to be used when the transmitted symbols belong to an M-PSK constellation. Moreover, the criterion for updating the estimate is based on error measure considered relevant to the constellation. The proposed algorithm incorporates appropriate constraints to the set-membership affine projection (SM-AP) algorithm resulting in a generalized adaptive algorithm for M-PSK equalization (SM-AP-CMA) which results in fast convergence while keeping a reduced number of coefficient updates. Simulation results using a UMTS channel model are presented in order to confirm the improved features attained by the proposed algorithm.

Parametric methods for direction-of-arrival (DoA) estimation have become very popular due to their low computational complexity and good accuracy. Among parametric DoA algorithms, ESPRIT is one of the most widely used, since it presents low computational complexity in comparison to other parametric methods. Covariance-based DoA (CB-DoA) estimation algorithm provides an even lower complexity alternative to ESPRIT, while imposing the same constraints on the geometry of the receiving array. This letter presents a new algorithm, based on the CB-DoA approach, comprising only real operations. The constraints on the algorithm are the same imposed to the unitary ESPRIT algorithm, allowing a reduction of about 67% on the required computational effort, for equivalent error measure.

Under anechoic conditions, a system that generates 3D binaural sound should simulate the head-related transfer functions (HRTFs) that represent sound changes from the source to the listener's ears. Such a system could directly use HRTFs measured over a sphere around the listener. However, since it is not feasible to perform measurements for every position, practical systems interpolate the necessary functions between measured HRTFs. This paper proposes a generalization of the continuous variable digital delay (CVDD) described in the literature, yielding a two spatial-variable dependent transfer function. It performs a polynomial interpolation whose coefficients are optimized on a region over the sphere, so that the angular coordinates of any point inside the region map directly into the corresponding HRTFs. Performance assessment is done through graphical means. The issue of continuity in moving sound generation is also approached, through the combination of adjacent-region structures

In this letter, we address rate-distortion-optimum compression of signals from electric power system disturbances, using atomic decompositions. Usually, such optimization is obtained assuming a single dictionary and consists of finding the best compromise between the quantization of the coefficients in the atomic decomposition and its number of terms. Here, several parameterized dictionaries are used instead. This allows the selection of the dictionary leading to the best rate-distortion (R-D) compromise. Distinct dictionaries correspond to different quantizers for the parameters of the atoms. Side information must be transmitted in order to indicate the dictionary employed. The R-D performance in this case depends on a complex interplay between the quantizers of the parameters of the atoms and the coefficient quantizers. Using a training stage, we select a reduced set of parameter and coefficient quantizers that give near-optimum R-D performance. Simulation results show that the proposed scheme indeed achieves near-optimum R-D performance with low computational complexity in the coding stage

Recently, the use of redundant memoryless single-carrier transmitters has been reported as an efficient choice to reduce distortions introduced by finite-impulse response (FIR) channels. In this work, redundant FIR transceivers are proposed to address not only channel frequency selectivity but additive colored noise, which strongly degrades the performance of memoryless transceivers. The transmitter is shown to be paraunitary, resulting in a simple receiver. The proposed system is optimized like a modulated filter bank. Channel shortening and post-combiner equalizers are used to improve system performance. Comparisons with recent proposed schemes are presented, illustrating the efficiency of the new structure for selective channels with colored noise.

This work presents blind constrained adaptive filtering algorithms based on the set-membership concept and incorporates time-varying bounds for CDMA interference suppression. Constrained constant modulus (CCM) and constrained minimum variance (CMV) gradient type algorithms designed in accordance with the specifications of the set-membership filtering concept are proposed. Furthermore, the important issue of bound specification is addressed in a new framework that takes into account parameter estimation dependency and multi-access (MAI) and inter-symbol interference (ISI) for multiuser communications. Simulations show that the new algorithms are capable of outperforming previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or underbounding

This paper proposes a new data-selective affine projection algorithm for echo cancellation. The algorithm generalizes the concepts of the conventional set-membership affine-projection by incorporating a lower bound on the output error in order to prevent undesirable attenuation of the far-end signal. It is shown that the echo signal can more reliably be removed from the far-end user signals by employing the new algorithm in double talk situations. In addition, the proposed algorithm retains the fast convergence of the conventional SM-AP algorithm while keeping a reduced number of coefficient updates. Simulation results, using the ITU-T G.168 recommendation setup parameters, are presented in order to confirm the good features of the proposed algorithm.

This work presents set-membership adaptive algorithms based on time-varying error bounds. The important issue of error bound specification is addressed in a new framework that takes into account parameter estimation dependency, multi-access (MAI) and intersymbol interference (ISI) for DS-CDMA communications. An algorithm for tracking and estimating the interference power is presented and incorporated into the time-varying error bound. Computer simulations show that the algorithms are capable of outperforming previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or under bounding

This paper proposes a data-selective affine projection algorithm. The algorithm generalizes the ideas of the conventional set-membership affine-projection (SM-AP) algorithm to include a variable data-reuse factor. By utilizing the information provided by the data-dependent step size, we propose an assignment rule that automatically adjust the number of data reuses. A particular reduced-complexity implementation of the proposed algorithm is also considered in order to reduce the dimensions of the matrix inversions involved in the computation of update. Simulations show that a significant reduction in the overall complexity can be obtained with the algorithm as compared with the conventional SM-AP algorithm. In addition, the proposed algorithm retains the fast convergence of the conventional SM-AP algorithm, and the low steady-state error of the SM-NLMS algorithm.

The problem of designing an optimal prefilter transform for finite impulse response (FIR) multiple-input multiple-output (MIMO) communication systems is addressed. The bit error rate (BER) is minimized under a power constraint. Binary signaling is assumed. The transmitter requires knowledge of the channel transfer function coefficients and the receiver transform. It is shown that the proposed prefiltering transform outperforms three previously known prefilter transform methods in terms of BER versus channel quality. The proposed prefilter transform is applicable to downlink communication systems where the base station can estimate the channel while receiving data from the mobile station.

In this article, we show that a code division multiple access (CDMA) system employing long codes can be interpreted as a multiple input multiple output system where the transmit filters are time-varying. We derive conditions for existence of zero-forcing equalizers and verify through simulations that these conditions are useful guidelines for the design of appropriate solution in the presence of noise. Compared to similar works in the literature, the derived conditions allow a reduction in the length of the equalizer filters, while imposing no constraints on the channel order.

The frequency-response masking (FRM) method allows the design of selective prototype filters for cosine-modulated filter banks (CMFB) with a reduced number of distinct coefficients. Such a methodology may result in filter banks with a large number of bands (e.g. 1024 or more) and a simplified optimization procedure, as there are less parameters to adjust. This work introduces a numerically efficient optimization procedure, based on a quasi-Newton algorithm, for designing selective FRM-based CMFB. The proposed method uses a perfect-reconstruction FRM prototype filter as a starting point and updates the number of bands of the filter bank during the optimization procedure. Examples provided indicate that figures-of-merit, such as intersymbol and intercarrier interference, for the optimized FRM-CMFB structure are significantly improved without increasing the complexity of the resulting structure.

This paper addresses the interpolation of Head-Related Transfer Functions (HRTFs) for 3D-sound generation through headphones. HRTFs are transfer functions associated with the paths between sound sources and the ears, and are usually measured for a finite set of source locations around the listener. Generation of 3D-sound at any other virtual positions requires interpolation procedures. In this work, the Inter-positional Transfer Function (IPTF) is introduced and an IPTF-based new method for HRTF interpolation is proposed. Comparisons between the IPTF-based and the bilinear interpolation methods are provided, with focus on interpolation accuracy and computational complexity.

In this work we derived conditions that guarantee the existence of zero-forcing (ZF) equalizers in communication systems employing block transmissions. Compared to previous works in this field, the obtained conditions allow a reduction in the length of the equalizer filters, reduce the necessary redundancy and allow transmission through channels with impulse response longer than the channel model. We also show experimental results obtained via computer simulations for evaluation of the system performance.

Partial-update adaptive filtering algorithms only update part of the filter coefficients at each time instant, leading to reduced computational complexity as compared with their conventional counterparts. In this paper, the ideas of the partial-update NLMS-type algorithms found in the literature are extended to the framework of set-membership filtering, from which data-selective NLMS type of algorithms with partial update are derived. The new algorithms combine data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. Simulation results verify the good performance of the new algorithms in terms of convergence speed, final misadjustment, and reduced computational complexity.

A recursive least square (RLS) based complex adaptive notch filter (ANF) using the Steiglitz-McBride (SM) method is proposed to suppress the radio frequency interference (RFI) in very-high-speed digital subscriber line (VDSL) systems. The proposed RLS-SM ANF converges fast and requires less computational complexity than the existing direct form constrained ANF using a recursive prediction error (RPE) algorithm. The proposed algorithm is specially advantageous when dealing with multiple RFI's.

In this work we present an efficient structure based on the Wiener filter to perform channel equalization in a block communications system. We show that it is possible to derive a structure to estimate a block of transmitted symbols that use the same Wiener filter to estimate all symbols in the block. In terms of bit error rate, experimental results show that the system performs better than discrete multitone (DMT) for a wide range of signal-to-noise ratios. A multistage Wiener filter recently introduced was used to implement a reduced-rank version of the equalizer, and the performance of the system was shown to remain almost unchanged for rank values much smaller than the signal autocovariance matrix rank.

We propose an alternative technique to the implementation of discrete wavelet multitone systems based on fractional-delay filter banks. The technique yields orthogonal subband signals and allows independent equalization for each subband at the receiver end. When compared to systems employing cosine-modulated filter banks, our technique shows better performance in terms of bit-error rate, particularly for low signal-to-noise ratios and highly selective channels.

Adaptive filtering techniques in subbands have been recently developed for a number of applications including acoustic echo cancellation and wideband active noise control. In such applications, hundreds of taps are required resulting in high computational complexity and low convergence rate when using LMS-based algorithms. For fullband systems, new algorithms which try to overcome these drawbacks have been investigated. A class of these algorithms employing variants of the filtered gradient adaptive (FGA) algorithm has been successfully developed. We apply these techniques to a recently proposed subband adaptive filter structure in order to improve the convergence rate and the computational load. Computer simulations show the benefits obtained with these proposed algorithms.

We use the frequency-response masking (FRM) approach to design prototype filters for cosine-modulated filter banks in the nearly perfect reconstruction case. With such an approach, it is possible to design a FRM filter with overall order almost equal to the direct-form FIR design, with only slight changes in the values of the inter-carrier and inter-symbol interferences and the attenuation of the bank filters. The result is an efficient design with reduced number of multipliers for the overall structure.

This paper proposes a general nonlinear digital filter structure for echo cancellation applications. Although echo cancelers employing linear digital filter structures are more widely used, there are many applications where nonlinear filters must be used. We propose using the DABNet (Decoupled A-B Net) filter, which is composed of a decoupled linear dynamic system followed by a nonlinear static map, for echo cancellation. The linear dynamic system is initially spanned by a set of discrete Laguerre systems, and then cascaded with a single hidden layer perceptron. A model reduction technique can be performed not only to identify the main time constants, but also to reduce the dimensionality of the perceptron input. The DABNet structure is able to approximate any nonlinear, causal, discrete time invariant, multiple-input single output system with fading memory. Comparisons between echo cancelers implemented with the DABnet and nonlinear FIR filters are presented.

This letter presents a new data selective adaptive filtering algorithm, the set-membership affine projection (SM-AP) algorithm. The algorithm generalizes the idea of the set-membership NLMS (SM-NLMS) algorithm to include constraint sets constructed from the past input and desired signal pairs. The resulting algorithm can be seen as a set-membership version of the affine-projection (AP) algorithm with an optimized step size. Also, the SM-AP algorithm does not trade convergence speed with misadjustment and computational complexity as most adaptive filtering algorithms. Simulations show the good performance of the algorithm, especially for colored input signals, in terms of convergence, final misadjustment, and reduced computational complexity.

Subband adaptive filtering has been studied by a large number of researchers. The main alternatives are structures with critical sampling and noncritical sampling, that use local errors or global error in the adaptation algorithm. In this paper a theoretical convergence analysis of an oversampled subband adaptive filtering structure with global error is presented. The convergence rate of the adaptation algorithm can be estimated from the results of this analysis. Computer simulations are presented to illustrate the convergence behavior of the subband adaptive algorithm and to verify the theoretical results.

This work presents analytical expressions as well as related curves to determine the maximum magnitude and phase deviations caused by a zero-pole pair, an individual pole or an individual zero on a discrete-time transfer function. The main motivation is to provide a clear link between the position of zeros and poles, considering their magnitudes and phases, and the effects of discarding them from a given transfer function, since evaluating these effects is crucial to attain the order reduction of physical system models.

This work proposes a model, originated from the power spectral density concept, for the sub-band processes obtained by analysis of an ARMA process by a decimating filter bank. As an example, the model for an AR process analyzed in octaves by a tree-structured FIR filter bank is derived in a recursive way, and some interpretations are given.

Subband adaptive filtering has been recently studied by a large number of researchers. The main alternatives are structures with critical sampling and noncritical sampling, and structures that use local errors and global error in the adaptive algorithm. In this paper a theoretical convergence analysis for the case of an oversampled subband adaptive altering structure with local errors is presented. The convergence rate and misadjustment of the algorithm can be estimated from the results of this analysis. Computer simulations are presented to illustrate the convergence behavior of the subband adaptive algorithm and to verify the theoretical results.

Implementation of overlapped block filtering using Scheduling by Edge Reversal (SER) is proposed in this paper. SER is a very simple and powerful synchronizer. It allows more efficient implementation of parallel structures. This technique is applied for the first time to FIR filters using the overlapped block digital filtering, and implemented on a parallel computer platform. The results confirm the expected reduction in computation time.

Providing a quantitative mean-squared-error analysis of adaptation algorithms is of great importance for determining their usefulness and for comparison with other algorithms. However, when the algorithm reutilizes previous data, such analysis becomes very involved as the independence assumption cannot be used. In this paper, a thorough mean-squared-error analysis of the binormalized data-reusing LMS algorithm is carried out. The analysis is based on a simplified model for the input-signal vector, assuming independence between the continuous radial probability distribution and the discrete angular probability distribution. Throughout the analysis only parallel and orthogonal input-signal vectors are used in order to obtain a closed-form formula for the excess mean-squared error. The formula agrees closely with simulation results even when the input-signal vector is a delay line. Furthermore, the analysis can be readily extended to other algorithms with expected similar accuracy.

In this paper, we present an analysis of the delayless subband adaptive filter structure proposed by Merched et al. We derive a simple expression for the excess MSE of the proposed structure, and show that it requires up to 3.7 times less computational complexity than the corresponding fullband LMS structure. Also, we establish a connection between subband and block adaptive filtering, where the latter can be interpreted as a special case of the former. Some computer simulations are presented in order to verify the performance of the proposed structure and the theoretical results.

A new method for adaptive autoregressive spectral stimulation based on the least-squares criterion with multi-band decomposition of the linear prediction error and analysis of each band through independent variable forgetting factors is presented. The proposed method localizes the forgetting factor adaptation scheme in the frequency domain and in the time domain, in the sense that variations on the statistics of the input signal are independently evaluated for each band along the time. In this paper, a new forgetting factor adaptation technique depending exclusively on the input signal is introduced and applied to the multi-window analysis of the linear prediction error structure to generate time-varying autoregressive spectral estimates. An improvement on the fidelity of estimates is shown in computer experiments which compare the proposed method with conventional and multi-band least-squares methods with fixed forgetting factors.

This paper addresses the design of high performance wavelets for image coding using a perceptual criterium defined as the product of the theoretical coding gain and an index recently reported called peak-to-peak ratio (PPR). It presents some new results in biorthogonal linear-phase wavelet design for image compression. A simplified design procedure was developed using a technique to generate high-order filters from lower-order ones. With this function the perceptual quality expected for the compressed images can be evaluated without having to go through tests with experts. Although this technique is sub-optimal, it yields wavelets which are optimal or very close to it. Some examples are given.

This letter presents a new fast QR algorithm based on Givens rotations using a priori errors. The principles behind the triangularization of the weighted input data matrix via QR decomposition and the type of errors used in the updating process are exploited in order to investigate the relationships among different fast algorithms of the QR family. These algorithms are classified according to a general framework and a detailed description of the new algorithm is presented.

The recursive least squares (RLS) algorithms for FIR adaptive filtering are particularly attractive due to their fast convergence, especially for correlated input signals. A detailed analysis of the QR-RLS algorithm in finite and infinite precision implementations is presented, emphasizing the case where the input signal samples are correlated. The expressions for the mean square values of all internal variables in a steady state are first derived. These expressions are the key used to determine the dynamic range of the internal signals, and to derive the analytical expressions for the mean square values of the deviations in the output variables of the algorithm in finite wordlength implementations. Previous works address this problem considering the input signal a white noise, a situation not so often encountered in practice. The accuracy of all analytical results are verified through a number of computer simulations.

In this work a variable convergence factor for use in Steiglitz-McBride (SM) based algorithms is proposed. The implementation of the convergence factor requires information available in the algorithm, therefore the additional cost is minimum. Some properties related to the performance of the variable convergence factors for the SM and output error methods are presented. Some simulations are also presented to confirm that the proposed convergence factor leads to fast convergence of the adaptive filter.>

This work develops relations for the mean squared value of internal variables in the fast QRD-RLS. The objective is to derive relations based on known characteristics of input signals that predict the behavior of the internal quantities of the algorithm. It is shown that the fast and conventional QRD-RLS algorithms have some variables in common, and thus previous results of the infinite precision analysis of the conventional algorithm remain valid for the fast version. Conditions for avoiding over flow in fixed-point implementations are presented. Simulation results are also shown.>

An efficient approach for the computation of the optimum convergence factor for the LMS (least mean square)/Newton algorithm applied to a transversal FIR structure is proposed. The approach leads to a variable step size algorithm that results in a dramatic reduction in convergence time. The algorithm is evaluated in systems identification applications.>

A criterion for the absence of zero-input limit cycles in recursive digital filter structures that can be implemented with a single quantizer in the recursive loop is presented. The criterion can be applied with a rounding or with a magnitude truncation quantizer and accounts for the use of error feedback as well. Additionally, a criterion for the elimination of constant-input limit cycles is formulated. The criteria are applied to several well-known filter structures. Since most of the structures have previously been analyzed in a multiquantizer implementation only, new results on their stability properties in the single-quantizer configuration are obtained.>