Deep learning (DL) based resource allocation (RA) has recently gained significant attention due to its performance efficiency. However, most related studies assume an ideal case where the number of users and their utility demands, e.g., data rate constraints, are fixed, and the designed DL-based RA scheme exploits a policy trained only for these fixed parameters. Consequently, computationally complex policy retraining is required whenever these parameters change. In this paper, we introduce a DL-based resource allocator (ALCOR) that allows users to adjust their utility demands freely, such as based on their application layer requirements. ALCOR employs deep neural networks (DNNs) as the policy in a time-sharing problem. The underlying optimization algorithm iteratively optimizes the on-off status of users to satisfy their utility demands in expectation. The policy performs unconstrained RA (URA)–—RA without considering user utility demands–—among active users to maximize the sum utility (SU) at each time instant. Depending on the chosen URA scheme, ALCOR can perform RA in either a centralized or distributed scenario. The derived convergence analyses provide theoretical guarantees for ALCOR’s convergence, and numerical experiments corroborate its effectiveness compared to meta-learning and reinforcement learning approaches.

Two algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) are analysed, namely the generalised echo and interference canceller (GEIC) and the extended multichannel Wiener filter (MWFext). Previously, these algorithms have been examined for linear echo paths, and assuming access to voice activity detectors (VADs) that separately detect desired speech and echo activity. However, algorithms implementing VADs may introduce detection errors. Therefore, in this paper, the previous analyses are extended by 1) modelling general nonlinear echo paths by means of the generalised Bussgang decomposition, and 2) modelling VAD error effects in each specific algorithm, thereby also allowing to model specific VAD assumptions. It is found and verified with simulations that, generally, the MWFextachieves a higher NR performance, while the GEIC achieves a more robust AEC performance.

Cell-free massive multiple-input multiple-output (CFmMIMO) is a paradigm that can improve users’ spectral efficiency (SE) far beyond traditional cellular networks. Increased spatial diversity in CFmMIMO is achieved by spreading the antennas into small access points (APs), which cooperate to serve the users. Sequential fronthaul topologies in CFmMIMO, such as the daisy chain and multi-branch tree topology, have gained considerable attention recently. In such a processing architecture, each AP must store its received signal vector in the memory until it receives the relevant information from the previous AP in the sequence to refine the estimate of the users’ signal vector in the uplink. In this paper, we adopt vector-wise and element-wise compression on the raw or pre-processed received signal vectors to store them in the memory. We investigate the impact of the limited memory capacity in the APs on the optimal number of APs. We show that with no memory constraint, having single-antenna APs is optimal, especially as the number of users grows. However, a limited memory at the APs restricts the depth of the sequential processing pipeline. Furthermore, we investigate the relation between the memory capacity at the APs and the rate of the fronthaul link.

Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i.e., fusion center) for processing. In this contribution, we extend a distributed adaptive algorithm for blind system identification that relies on the estimation of a stacked network-wide consensus vector at each node, the computation of which requires either broadcasting or relaying of node-specific values (i.e., local vector norms) to all other nodes. The extended algorithm employs a distributed-averaging-based scheme to estimate the network-wide consensus norm value by only using the local vector norm provided by neighboring sensor nodes. We introduce an adaptive mixing factor between instantaneous and recursive estimates of these norms for adaptivity in a time-varying system. Simulation results show that the extension provides estimation results close to the optimal fully-connected-network or broadcasting case while reducing inter-node transmission significantly.

This paper presents a centralized cascade multi-channel noise reduction (NR) and acoustic feedback cancellation (AFC) algorithm for speech applications in a wireless acoustic sensor and actuator network (WASAN). The algorithm consists of a multi-channel Wiener filter (MWF) based NR stage, where M microphone and L loud-speaker signals in the network are used to estimate, for each node, the speech component in its reference microphone and loudspeaker signal. For each node then a prediction error method based AFC stage is applied using these estimates to estimate the desired signal. Closed-loop simulations show that the proposed centralized algorithm outperforms a node working in isolation, in terms of the signal-to-interference-plus-noise ratio improvement (∆SINR) and short-time objective intelligibility (STOI) metric. In particular, the performance is improved when for instance one of the nodes has a lower input signal-to-noise ratio (iSNR) than the other nodes. In addition, it is also shown that a single-channel local adaptive filter per node is sufficient in the AFC stage, instead of a K-channel adaptive filter. Based on this and on the availability of a distributed algorithm for the NR stage, the presented algorithm is viewed as a stepping stone towards a fully distributed NR and AFC algorithm.

In this paper, we address the problem of resource allocation (RA) in wireless communication networks, where each user has a dynamic data rate constraint. The objective of RA is to maximize the sum rate (SR) of the users while satisfying the data rate constraints in expectation. For a given set of data rate constraints, a suitable probability distribution for the activation of users is found iteratively with a stochastic gradient descent (SGD) approach to satisfy the data rate constraints in expectation. At each time instant, RA amongst the randomly activated users is performed noniteratively by a centralized deep neural network (DNN). Simulations show that the proposed approach is convergent and not only can consider dynamic data rate constraints accurately, but also that it achieves a SR higher than that of the conventional geometric programming (GP) method. The proposed approach can open up a direction of research for cross-layer RA in the current deep learning-based RA context.

A sound zone control method is proposed, based on the frequency domain variable span trade-off filter (VAST). Existing VAST methods optimizes the sound field at a set of discrete points, while the proposed method uses kernel interpolation to instead optimize the sound field over a continuous region. When the loudspeaker positions are known, the performance can be improved further by applying a directional weighting to the interpolation procedure. The proposed method is evaluated by simulating broadband sound in a reverberant environment, focusing on the case when microphone placement is restricted. The proposed method with directional weighting outperforms the pointwise VAST over the full bandwidth of the signal, and the proposed method without directional weighting outperforms the pointwise VAST at low frequencies.

Acoustic feedback and noise are common problems that corrupt microphone signals and affect the performance of speech and audio signal processing applications and devices. In this paper, a cascade noise reduction (NR) and acoustic feedback cancellation (AFC) algorithm is presented for speech applications where a multi-channel Wiener filter (MWF) based NR is applied first followed by a single-channel prediction-error method (PEM) based adaptive feedback cancellation stage. It is shown that by using a rank-2 estimate of the speech correlation matrix in the NR stage it is possible to obtain a good feedback path estimate for the reference microphone in the AFC stage. Closed-loop simulations with M microphones and 1 loudspeaker are presented using both an M-channel rank-1 and an (M + 1)-channel rank-2 MWF and it is shown that for the considered input signal-to-noise ratios the proposed algorithm increases the added stable gain (ASG) of the system.

In this work we propose a multi-channel active noise equalizer (ANE) that can deal with multi-frequency noise signals and assigns simultaneously different equalization gains to each frequency component at each monitoring sensor. For this purpose, we state a pseudo-error noise signal for each sensor of the ANE, which has to be cancelled out in order to get the desired equalization profiles. Firstly the optimal analytic solution for the ANE filters in the case of single-frequency noise is provided, and an adaptive algorithm based on the Least Mean Squared (LMS) is proposed for the same case. We also show that this adaptive strategy reaches the theoretical solution in steady state. Secondly, we state an equivalent approach for the case of multi-frequency noise based on two alternatives: a common pseudo-error signal at each sensor for all frequencies, and a different pseudo-error signal at each sensor for each frequency. The analytic and adaptive solutions for the ANE control filters have been developed for both pseudo-error alternatives. Finally, the ability of the proposed ANE to achieve simultaneously different user-selected noise profiles in different locations has been validated by their transfer functions and simulations.

The paper presents distributed algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor and actuator network (WASAN) where each node may have multiple microphones and multiple loudspeakers, and where the desired signal is a speech signal. A centralized integrated AEC and NR algorithm, i.e., multichannel Wiener filter (MWF), is used as starting point where echo signals are viewed as background noise signals and loudspeaker signals are used as additional input signals to the algorithm. By including prior knowledge (PK), namely that the loudspeaker signals do not contain any desired signal component, an alternative centralized cascade algorithm (PK-MWF) is obtained with an AEC stage first followed by an MWF-based NR stage which has a lower computational complexity. Distributed algorithms can then be obtained from the MWF and PK-MWF algorithm, i.e., the generalized eigenvalue decomposition (GEVD)-based distributed adaptive node-specific signal estimation (DANSE) and PK-GEVD-DANSE algorithm, respectively. In the former, each node performs a reduced dimensional integrated AEC and NR algorithm and broadcasts only 1 fused signal (instead of all its signals) to the other nodes. In the PK-GEVD-DANSE algorithm, each node performs a reduced dimensional cascade AEC and NR algorithm and broadcasts only 2 fused signals (instead of all its signals) to the other nodes. The distributed algorithms achieve the same performance, upon convergence, as the corresponding centralized integrated (MWF) and centralized cascade (PK-MWF) algorithm. It is observed, however, that the communication cost in the PK-GEVD-DANSE algorithm can also be reduced, where each node then broadcasts only 1 fused signal (instead of 2 signals) to the other nodes. The resulting algorithm, referred to as the pruned PK-GEVD-DANSE (pPK-GEVD-DANSE) algorithm, then effectively combines the lowest possible communication cost (as low as in the GEVD-DANSE algorithm) with a lowest possible computational complexity in each node (further reduced from the PK-GEVD-DANSE computational complexity), within the class of algorithms considered in this paper.

In this paper, a cross-layer resource allocation (RA) scheme based on deep learning is introduced for multi-tone multi-user wired communication systems such as, for instance, digital subscriber line (DSL) systems under the current G.fast standard. The upper layer reports time-varying user demands to the physical layer using proportional priority weights. Unlike the deep neural network (DNN)-based RA schemes available in the literature for wireless communication, here the dynamic part is in adjusting the priority weights which are then fed to a DNN to map these priority weights to an appropriate power allocation, maximizing the weighted sum rate (WSR). The DNN is trained employing an unsupervised strategy to increase its generalization capabilities. Through the simulations, we show that the proposed cross-layer scheme can scale very well, even for real-world systems with thousands of RA variables. Moreover, the proposed cross-layer scheme significantly outperforms the conventional WMMSE method in terms of computation speed, while achieving the same data rates. Hence, the proposed cross-layer scheme is more suitable for cross-layer RA than the conventional methods, as it increases the speed with which the physical layer can react to time-varying upper layer data rate demands.

In cell-free Massive MIMO systems, a large number of distributed wireless access points (AP) are simultaneously serving a number of user equipments (UEs). This setup has the ability to offer a good quality of service, be it that there is still a need for low-complexity signal processing algorithms. In this paper, the problem of optimal uplink receive combining is tackled by providing an efficient distributed MMSE algorithm, with a minimal number of exchanged parameters between the APs and the network center. Scalable versions of this distributed MMSE algorithm are also proposed ensuring that the algorithm can be used in large networks with many UEs.

In multi-microphone speech enhancement, reverberation as well as additive noise and/or interfering speech are commonly suppressed by deconvolution and spatial filtering, e.g., using multi-channel linear prediction (MCLP) on the one hand and beamforming, e.g., a generalized sidelobe canceler (GSC), on the other hand. In this article, we consider several reverberant speech components, whereof some are to be dereverberated and others to be canceled, as well as a diffuse (e.g., babble) noise component to be suppressed. In order to perform both deconvolution and spatial filtering, we integrate MCLP and the GSC into a novel architecture referred to as integrated sidelobe cancellation and linear prediction (ISCLP), where the sidelobe-cancellation (SC) filter and the linear prediction (LP) filter operate in parallel, but on different microphone signal frames. Within ISCLP, we estimate both filters jointly by means of a single Kalman filter. We further propose a spectral Wiener gain post-processor, which is shown to relate to the Kalman filter's posterior state estimate. The presented ISCLP Kalman filter is benchmarked against two state-of-the-art approaches, namely first a pair of alternating Kalman filters respectively performing dereverberation and noise reduction, and second an MCLP+GSC Kalman filter cascade. While the ISCLP Kalman filter is roughly $M^2$ times less expensive than both reference algorithms, where $M$ denotes the number of microphones, it is shown to perform at least similarly as compared to the former, and to outperform the latter. A MATLAB implementation is available.

Multi-channel short-time Fourier transform (STFT) domain-based processing of reverberant microphone signals commonly relies on power-spectral-density (PSD) estimates of early source images, where early refers to reflections contained within the same STFT frame. State-of-the-art approaches to multi-source early PSD estimation, given an estimate of the associated relative early transfer functions (RETFs), conventionally minimize the approximation error defined with respect to the early correlation matrix, requiring non-negative inequality constraints on the PSDs. Instead, we here propose to factorize the early correlation matrix and minimize the approximation error defined with respect to the early-correlation-matrix square root. The proposed minimization problem-constituting a generalization of the so-called orthogonal Procrustes problem-seeks a unitary matrix and the square roots of the early PSDs up to an arbitrary complex argument, whereby non-negative inequality constraints become redundant. A solution is obtained iteratively, requiring one singular value decomposition (SVD) per iteration. The estimated unitary matrix and early PSD square roots further allow to recursively update the RETF estimate, which is not inherently possible in the conventional approach. An estimate of the said early-correlation-matrix square root itself is obtained by means of the generalized eigenvalue decomposition (GEVD), where we further propose to restore non-stationarities by desmoothing the generalized eigenvalues in order to compensate for inevitable recursive averaging. Simulation results indicate fast convergence of the proposed multi-source early PSD estimation approach in only one iteration if initialized appropriately, and better performance as compared to the conventional approach. A MATLAB implementation is available.

This article studies the effects of inter-channel time and level differences in stereophonic reproduction on perceived localization uncertainty, which is defined as how difficult it is for a listener to tell where a sound source is located. Towards this end, a computational model of localization uncertainty is proposed first. The model calculates inter-aural time and level difference cues, and compares them to those associated to free-field point-like sources. The comparison is carried out using a particular distance functional that replicates the increased uncertainty observed experimentally with inconsistent inter-aural time and level difference cues. The model is validated by formal listening tests, achieving a Pearson correlation of 0.99. The model is then used to predict localization uncertainty for stereophonic setups and a listener in central and off-central positions. Results show that amplitude methods achieve a slightly lower localization uncertainty for a listener positioned exactly in the center of the sweet spot. As soon as the listener moves away from that position, the situation reverses, with time-amplitude methods achieving a lower localization uncertainty.

While substantial noise reduction and speech enhancement can be achieved with multiple microphones organized in an array, in some cases, such as when the microphone spacings are quite close, it can also be quite limited. This degradation can, however, be resolved by the introduction of one or more external microphones (XMs) into the same physical space as the local microphone array (LMA). In this paper, three methods of extending an LMA-based generalized sidelobe canceller (GSC-LMA) with multiple XMs are proposed in such a manner that the relative transfer function pertaining to the LMA is treated as a priori knowledge. Two of these methods involve a procedure for completing an extended blocking matrix, whereas the third uses the speech estimate from the GSC-LMA directly with an orthogonalized version of the XM signals to obtain an improved speech estimate via a rank-1 generalized eigenvalue decomposition. All three methods were evaluated with recorded data from an office room and it was found that the third method could offer the most improvement. It was also shown that in using this method, the speech estimate from the GSC-LMA was not compromised and would be available to the listener if so desired, along with the improved speech estimate that uses both the LMA and XMs.

Conventionally, the single constraint of the minimum variance distortionless response (MVDR) beamformer for speech enhancement has been defined using one of two approaches. Either it is based on a priori assumptions such as microphone characteristics, position, speech source location, and room acoustics, or on a relative transfer function (RTF) vector estimate using a data dependent method. Each approach has its respective merits and drawbacks and a decision usually has to be made between one of the approaches. In this paper, an alternative approach of using an integrated MVDR beamformer is investigated, where both the hard constraints from the two conventional approaches are softened to yield two tuning parameters. It will be shown that this integrated MVDR beamformer can be expressed as a convex combination of the conventional MVDR beamformers, a linearly constrained minimum variance (LCMV) beamformer, and an all-zero vector, with real, positive-valued coefficients. By analysing how the tuning parameters affect these coefficients, two tuning rules for a practical implementation of the integrated MVDR are subsequently proposed. An evaluation with simulated and recorded data demonstrates that the integrated MVDR beamformer can be beneficial as opposed to relying on either of the conventional MVDR beamformers.

Acoustic source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists of the approximation of the sound field measured by a set of microphones. The recorded sound pressure is matched with that of a particular acoustic model based on a collection of plane waves arriving from different directions at the microphone positions. In order to achieve meaningful results, spatial and spatio-spectral sparsity can be promoted in the weight signals controlling the plane waves. The large-scale optimization problem resulting from the inverse problem formulation is solved using a first order optimization algorithm combined with a weighted overlap-add procedure. It is shown that once the weight signals capable of effectively approximating the sound field are obtained, they can be readily used to localize a moving sound source in terms of direction of arrival (DOA) and to perform dereverberation in a highly reverberant environment. Results from simulation experiments and from real measurements show that the proposed algorithm is robust against both localized and diffuse noise exhibiting a noise reduction in the dereverberated signals.

For blind speech dereverberation, two frameworks are commonly used: on the one hand, the multi-channel linear prediction (MCLP) framework, and on the other hand, data-dependent beamforming, e.g., the generalized sidelobe canceler (GSC) framework. The MCLP framework is designed to perform deconvolution and hence has gained increased prominence in blind speech dereverberation. The GSC framework is commonly used for noise reduction, but may be applied for dereverberation as well. In previous work, we have shown that for the noiseless case, MCLP and the GSC yield in theory mathematically equivalent results in terms of dereverberation. In this paper, we assume additional coherent as well as incoherent-noise components and formally analyze and compare both frameworks in terms of dereverberation and noise reduction performance. Both the theoretical analysis and time domain simulation results demonstrate that unlike the GSC, MCLP expectably shows limited performance in terms of noise reduction, while both perform equally well in terms of dereverberation, provided that the GSC blocking matrix achieves complete blocking of the early reverberant-speech component and sufficiently many microphones are available. In case of incomplete blocking, however, the GSC performs inferior to MCLP in terms of dereverberation, as shown in short-time Fourier transform domain simulations.

In next-generation digital subscriber line networks such as G.fast, employing discrete multi-tone transmission in high frequencies up to 212 MHz, the crosstalk among lines reaches very high levels. To precompensate the crosstalk in downstream transmission, QRD-based precoding has been proposed as a near-optimal dynamic spectrum management (DSM) technique. However, the performance of QRD-based precoding is greatly affected by the user encoding ordering (UEO). Since current multi-tone UEO methods are rather heuristic in the way they approach fairness, we develop, in this paper, a set of novel DSM algorithms for joint power allocation and UEO that enforce a generalized α -fairness policy. Since finding the globally optimal UEO entails a combinatorial optimization problem with excessive computational complexity, an iterative algorithm is proposed which uses per-tone exhaustive searches (PTESs) and provides near-optimal approximate solutions. To further reduce the computational complexity, two suboptimal methods are suggested to replace the expensive PTESs, leading to two additional α -fair DSM algorithms that are tractable for large scenarios against little performance loss. Simulations of a G.fast cable binder show that the α -fair DSM algorithms achieve an efficient trade-off between fairness and performance in contrast to current UEO methods.

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.

The use of an external microphone in conjunction with an existing local microphone array can be particularly beneficial for noise reduction tasks that are critical for hearing devices, such as cochlear implants and hearing aids. Recent work has already demonstrated how an external microphone signal can be effectively incorporated into the common noise reduction technique of using a Minimum Variance Distortionless Response (MVDR) beamformer. In this paper, we provide a further extension, whereby an external microphone signal can be incorporated into an existing framework of a Generalised Sidelobe Canceller (GSC) that has been designed for a local microphone signal array. It will be shown that the resulting GSC with an external microphone results in an easily implementable addition to the existing GSC framework for a local microphone array, and can exhibit an improved noise reduction performance.

In this paper, source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists in the interpolation of the sound field measured by a set of microphones by matching the recorded sound pressure with that of a particular acoustic model. This model is based on a collection of equivalent sources creating either spherical or plane waves. In order to achieve meaningful results, spatial, spatio-temporal and spatio-spectral sparsity can be promoted in the signals originating from the equivalent sources. The inverse problem consists of a large-scale optimization problem that is solved using a first order matrix-free optimization algorithm. It is shown that once the equivalent source signals capable of effectively interpolating the sound field are obtained, they can be readily used to localize a speech sound source in terms of Direction of Arrival (DOA) and to perform dereverberation in a highly reverberant environment.

In this paper, speech-distortion weighted (SDW) interframe Wiener filters (IFWFs) are investigated for single-channel noise reduction in a filter bank structure. The filters utilize a parameter μ that explicitly sets a tradeoff between noise reduction and speech distortion and have traditionally been used in multichannel applications under the term SDW multichannel Wiener filter. The application of these SDW-IFWFs relies on the estimation of interframe correlation (IFC) coefficients, and it is shown that the IFC coefficients can be more robustly estimated using a secondary higher resolution filter bank (HRFB). It is then shown how real-valued scalar gains, which are optimal in the primary filter bank, can be applied directly in the HRFB instead of the interframe filtering in the primary filter bank, which leads to a more robust noise reduction performance for any value of μ. Computing these gains is also cheaper since matrix inversions are avoided and the primary filter bank is not needed in the actual implementation. Experimental results are given that support the claims, where the proposed methods are compared to relevant reference methods using measures such as the segmental SNR and the objective PESQ.

Objective measures are widely used for the perceptual sound-quality evaluation of audio signal processing algorithms. Nevertheless, the use of subjective-evaluation measures remains relevant, in particular when application-specific objective measures are lacking. In this paper, we present a perceptual sound-quality evaluation of different algorithms for adaptive feedback cancellation (AFC), with both speech and music signals. Three algorithms are compared: the block normalized least mean squares algorithm, the prediction-error method (PEM) based frequency-domain adaptive filter, and the PEM-based frequency-domain Kalman filter (PEM-FDKF). The subjective evaluation results for the tested algorithms suggest that there is a large difference in statistical significance, and a corresponding large effect size, between the PEM-FDKF and the other algorithms, when using speech signals. A smaller statistical significance, and a lower effect size, is reported when using music signals. The subjective evaluation results are then compared with the results obtained with several objective measures. The correlation between subjective and objective scores shows that objective measures can be effectively used to predict the sound-quality degradation caused by acoustic feedback and AFC artifacts.

We consider nonlinear zero-forcing (ZF) precoding design to improve the downstream G.fast peak-rates when only a few users in the cable binder are active. In order to compute the optimal nonlinear ZF precoder under per-line power constraints (PLPCs), we present a novel low-complexity dual decomposition algorithm, in which the key is the use of Lagrange multiplier based virtual precoders to transform the PLPCs into an easier virtual sum-power constraint (SPC), such that the SPC-optimality of the QR decomposition-based precoder may be exploited. We show a reduced computational complexity of this algorithm over the state-of-the-art SVD-block-diagonalization-based dual decomposition algorithm. We present simulations of a 10-line cable binder that demonstrate substantial peak-rate gains over standard QR decomposition-based ZF precoding in DSL, due to the increasingly stronger crosstalk channels in the G.fast frequency range (up to 212 MHz). Furthermore, we show that the proposed algorithm naturally extends to the scenario with multiple lines terminating at the customer premise equipments.

The growing demand for wireless connectivity has turned bandwidth into a scarce resource that has to be carefully managed and fairly distributed to users. However, the variability of the wireless channel can severely degrade the service received by each user. The Double Relay Communication Protocol (DRCP) [1] is a transmission scheme that addresses these problems by exploiting spatial diversity to enhance the fairness of the system without requiring any additional infrastructure (i.e relay nodes or a backhaul connection). Although DRCP has originally been proposed to work without channel state information at the transmitter (CSIT), in this paper we study how the performance of DRCP can be further improved through power control when CSIT is available. Our approach provides the highest fairness and the largest minimum spectral efficiency for most conditions compared to other studied baseline approaches.

In this demonstration, we aim at presenting our recent implementation results and provide an evaluation testbed through which users can experiment and compare the outputs of the distributed speech enhancement algorithms in [1-3]. The system allows a user to assess the merits of these algorithms in any acoustic setup. The multi-channel Wiener filter (MWF) is a well-known noise reduction algorithm for multi-microphone speech processing applications. In general, the noise reduction improves as the number of available microphones increases, since a better spatial sampling or diversity can be exploited. Motivated by this, wireless acoustic sensor networks (WASNs), consisting of a multitude of collaborating nodes with an embedded signal processing unit and microphone array, have been proposed to increase the spatial diversity of multi-microphone systems. However, due to the limited per-node computational power and communication bandwidth, reduced-bandwidth distributed processing is more favorable than a centralized processing where all the microphone signals are transmitted to a fusion center. In this demo, we evaluate the so-called distributed adaptive node-specific signal estimation (DANSE) algorithm [1] which is essentially a distributed realization of the MWFs of the individual nodes of a WASN and allows the nodes to cooperate by exchanging pre-filtered and compressed signals, while eventually converging to the same centralized MWF solutions as if each node would have access to all the microphone signals in theWASN [1,2]. In the original version of DANSE in [1], the required speech correlation matrices are estimated using a straightforward subtraction-based method. This method, however, has been shown to deliver an unsatisfying performance in the presence of second-order statistics error (e.g., due to low-SNR conditions, highly non-stationary noise or erroneous voice activity detections (VADs)) [4]. An alternative version of DANSE, called generalized eigenvalue decomposition (GEVD)-based DANSE, has been developed in [3] in which each node incorporates a GEVD-based low-rank approximation of the speech correlation matrix in its local MWF. An in-depth theoretical study of the underlying principals of the GEVD-based DANSE algorithm has been presented in [3]. In order to also evaluate the merits of the GEVD-based DANSE algortihm in a practical realistic environment, a real-time experimental setup has been developed which will be explained in the next section.

In the G.fast frequency range with strong levels of crosstalk, nonlinear precoding (NLP) is proposed as a near-optimal technique for crosstalk precompensation in downstream transmission. While existing methods for multi-tone NLP user encoding ordering (UEO) are rather heuristic in how they approach fairness and suffer from substantial suboptimality, we develop a novel algorithm for joint dynamic spectrum management (DSM) and UEO that enforces a generalized alpha-fairness policy. Since finding the optimal UEO is a combinatorial optimization problem with excessive computational complexity, the proposed algorithm uses a low-complexity iterative method which provides near-optimal approximate solutions. Simulations demonstrate that the novel algorithm achieves a trade-off between fairness and performance that outperforms current UEO methods.

The static resource allocation which is usually assumed for the DSL physical layer leaves unused a significant portion of the achievable rate region. An alternative approach is to divide time into slots of short duration, and to change the resource allocation from each time slot to the next. A crosslayer scheduler then chooses a different resource allocation setting for each time slot by defining a utility function for each user n, and solving the corresponding network utility maximization (NUM) problem. For spectrum coordination, this NUM problem is non-convex and solving it is NP-Hard. This paper therefore introduces a fast algorithm, referred to as NUM-DSB, which converges to a local solution of the NUM problem. NUM-DSB can be applied to any NUM problem, regardless of the considered utility functions's characteristics. Simulation results show that NUM-DSB can compete with the state of the art algorithm for smooth non-convex network utility maximization.

The quality of experience of many modern network services depends on the delay performance of the underlying communications network. In DSL networks, crosstalk introduces competition for bandwidth among users. In such a competitive environment, delay performance is largely determined by the manner in which the cross-layer scheduler assigns bandwidth to the different users. Existing cross-layer schedulers optimize a simple metric, and do not consider important information that is contained within a queue's packets. In this paper, we present a new cross-layer scheduler, referred to as the minimal delay violation (MDV) scheduler, which optimizes a more elaborate metric that closely resembles the quality of experience of the users. Through simulations, it is shown that the new scheduler outperforms the state of the art in cross-layer scheduling algorithms.

Full-duplex (FDX) transmission is a promising technique emerging in DSL networks that theoretically may double the spectral efficiency by simultaneously transmitting in the downstream (DS) and upstream (US) on the same frequency band. Unfortunately, this may lead to severe near-end crosstalk (NEXT) interference in addition to the usual far-end crosstalk (FEXT) among the lines within a cable binder. To limit the NEXT impact by balancing the user transmit powers, tailored vectoring-based dynamic spectrum management (DSM) techniques are vital. In this paper, we develop a DSM algorithm for the specific case of perfect NEXT cancellation at the access node. This assumption in combination with US-DS duality theory allows to reformulate the DS-US structure of the non-convex weighted sum-rate maximization problem into an easier US-US structure, which can be solved with low-complexity iterative fixed-point power updates. Simulations of G.fast multi-user FDX transmission demonstrate significant improvements over time division duplex transmission.

Massive MIMO has been proposed as a means of achieving huge spectral efficiency in cellular networks. However, its implementation is usually based on linear precoding schemes using Channel State Information (CSI). For this reason, the use of the Time Division Duplex (TDD) mode is typically assumed since it removes the dependence on the number of antennas at the transmitter to acquire CSI. Unfortunately, most of the cellular network operate in the Frequency Division Duplex (FDD) mode in which the acquisition of CSI depends proportionally to the number of transmit antennas. For this reason, the implementation of Massive MIMO is handicapped to few transmit antennas for the current cellular networks. In this paper we study the use of reconfigurable antennas that can switch among a set of preset modes, i.e., radiation patterns, through Blind Interference Alignment schemes. We show that it is a solution to exploit a very large number of antennas at the transmit side, overcoming the bottleneck that today constitutes the acquisition of CSI in Massive MIMO systems operating in FDD mode.

Room Impulse Responses (RIRs) are typically measured using a set of microphones and a loudspeaker. When RIRs spanning a large volume are needed, many microphone measurements must be used to spatially sample the sound field. In order to reduce the number of microphone measurements, RIRs can be spatially interpolated. In the present study, RIR interpolation is formulated as an inverse problem. This inverse problem relies on a particular acoustic model capable of representing the measurements. Two different acoustic models are compared: the plane wave decomposition model and a novel time-domain model, which consists of a collection of equivalent sources creating spherical waves. These acoustic models can both approximate any reverberant sound field created by a far-field sound source. In order to produce an accurate RIR interpolation, sparsity regularization is employed when solving the inverse problem. In particular, by combining different acoustic models with different sparsity promoting regularizations, spatial sparsity, spatio-spectral sparsity, and spatio-temporal sparsity are compared. The inverse problem is solved using a matrix-free large-scale optimization algorithm. Simulations show that the best RIR interpolation is obtained when combining the novel time-domain acoustic model with the spatio-temporal sparsity regularization, outperforming the results of the plane wave decomposition model even when far fewer microphone measurements are available.

Microphone arrays allow to exploit the spatial coherence between simultaneously recorded microphone signals, e.g., to perform speech enhancement, i.e., to extract a speech signal and reduce background noise. However, in systems where the microphones are not sampled in a synchronous fashion, as it is often the case in wireless acoustic sensor networks, a sampling rate offset (SRO) exists between signals recorded in different nodes, which severely affects the speech enhancement performance. To avoid this performance reduction, the SRO should be estimated and compensated for. In this paper, we propose a new approach to blind SRO estimation for an asynchronous wireless acoustic sensor network, which exploits the phase drift of the coherence between the asynchronous microphones signals. We utilize the fact that the SRO causes a linearly increasing time delay between two signals and hence a linearly increasing phase-shift in the short-time Fourier transform domain. The increasing phase shift, observed as a phase drift of the coherence between the signals, is used in a weighted least-squares framework to estimate the SRO. This method is referred to as least-squares coherence drift (LCD). Experimental results in different real-world recording and simulated scenarios show the effectiveness of LCD compared to different benchmark methods. The LCD is effective even for short signal segments. We finally demonstrate that the use of the LCD within a conventional compensation approach eliminates the performance loss due to SRO in a speech enhancement algorithm based on the multichannel Wiener filter.

Adaptive filtering based feedback cancellation is a widespread approach to acoustic feedback control. However, traditional adaptive filtering algorithms have to be modified in order to work satisfactorily in a closed-loop scenario. In particular, the undesired signal correlation between the loudspeaker signal and the source signal in a closed-loop scenario is one of the major problems to address when using adaptive filters for feedback cancellation. Slow convergence speed and limited tracking capabilities are other important limitations to be considered. Additionally, computationally expensive algorithms as well as long delays should be avoided, for instance, in hearing aid applications, because of power constraints, important to extend battery life, and real-time implementations requirements, respectively. We present an algorithm combining good decorrelation properties, by means of the prediction-error method based signal prewhitening, fast convergence, good tracking behavior, and low computational complexity by means of the frequency-domain Kalman filter, and low delay by means of a partitioned-block implementation.

Parametric modeling of room acoustics aims at representing room transfer functions by means of digital filters and finds application in many acoustic signal enhancement algorithms. In previous work by other authors, the use of orthonormal basis functions (OBFs) for modeling room acoustics has been proposed. Some advantages of OBF models over all-zero and pole-zero models have been illustrated, mainly focusing on the fact that OBF models typically require less model parameters to provide the same model accuracy. In this paper, it is shown that the orthogonality of the OBF model brings several additional advantages, which can be exploited if a suitable algorithm for identifying the OBF model parameters is applied. Specifically, the orthogonality of OBF models does not only lead to improved model efficiency (as pointed out in previous work), but also leads to improved model scalability and model stability. Its appealing scalability property derives from a previously unexplored interpretation of the OBF model as an approximation to a solution of the inhomogeneous acoustic wave equation. Following this interpretation, a novel identification algorithm is proposed that takes advantage of the OBF model orthogonality to deliver efficient, scalable, and stable OBF model estimates, which is not necessarily the case for nonlinear estimation techniques that are normally applied.

In the G.fast digital subscriber line frequency range (up to 106 or 212 MHz), where crosstalk channels may even become larger than direct channels, linear zero-forcing (ZF) precoding is no longer near-optimal for downstream (DS) vectored transmission. To improve performance, we develop a novel low-complexity algorithm for both linear and nonlinear precoding-based dynamic spectrum management that maximizes the weighted sum-rate under realistic per-line total power and per-tone spectral mask constraints. It applies to DS scenarios with a single copper line at each customer site [i.e., broadcast channel (BC) scenarios], as well as to DS scenarios with multiple copper lines at some or all customer sites (i.e., the so-called multiple-input-multiple-output-BC scenarios). The algorithm alternates between precoder and equalizer optimization, where the former relies on a Lagrange multiplier based transformation of the DS dual decomposition approach formulation into its dual upstream (US) formulation, together with a low-complexity iterative fixed-point formula to solve the resulting US problem. Simulations with measured G.fast channel data of a very high crosstalk cable binder are provided revealing a significantly improved performance of this algorithm over ZF techniques for various scenarios, and in addition, a faster convergence rate compared with the state-of-the-art WMMSE algorithm.

Speech enhancement in wireless acoustic sensor networks requires the exchange of audio signals. Since the wireless communication often dominates the nodes’ energy budget, techniques for data exchange reduction are crucial. Adaptive quantization aims to optimize the bit depth of each exchanged signal according to its contribution to the speech enhancement performance. This enables the network to scale its energy and communication bandwidth requirements according to the current operating environment. The impact metric was previously proposed to predict the effect of quantization in linear minimum mean squared error (MMSE) estimation. We provide new insights into greedy adaptive quantization based on this impact metric. We achieve this by expanding the mathematical framework to include a new metric based on the gradient of the MMSE as a function of the quantization noise power. Using these tools, we show how the MMSE gradient naturally leads to a greedy algorithm and how the impact metric is a generalization of the gradient metric and a previously proposed metric. Besides, we validate the impact metric for adaptive quantization both in a simulated and in a real wireless acoustic sensor network deployed in a home environment, showing the energy savings achievable through greedy adaptive quantization.

The linearly constrained minimum variance (LCMV) beamformer has been widely employed to extract (a mixture of) multiple desired speech signals from a collection of microphone signals, which are also polluted by other interfering speech signals and noise components. In many practical applications, the LCMV beamformer requires that the subspace corresponding to the desired and interferer signals is either known, or estimated by means of a data-driven procedure, e.g., using a generalized eigenvalue decomposition (GEVD). In practice, however, it often occurs that insufficient relevant samples are available to accurately estimate these subspaces, leading to a beamformer with poor output performance. In this paper we propose a subspace projection-based approach to improve the performance of the LCMV beamformer by exploiting the available data more efficiently. The improved performance achieved by this approach is demonstrated by means of simulation results.

We study a distributed node-specific parameter estimation problem where each node in a wireless sensor network is interested in the simultaneous estimation of different vectors of parameters that can be of local interest, of common interest to a subset of nodes, or of global interest to the whole network. We assume a setting where the nodes do not know which other nodes share the same estimation interests. First, we conduct a theoretical analysis on the asymptotic bias that results in case the nodes blindly process all the local estimates of all their neighbors to solve their own node-specific parameter estimation problem. Next, we propose an unsupervised diffusion-based LMS algorithm that allows each node to obtain unbiased estimates of its node-specific vector of parameters by continuously identifying which of the neighboring local estimates correspond to each of its own estimation tasks. Finally, simulation experiments illustrate the efficiency of the proposed strategy.

Many acoustic signal enhancement applications require adaptive filters with a long impulse response, but with a small number of filter parameters. Fixed-poles infinite impulse response (IIR) adaptive filters based on orthonormal basis functions (OBFs) present advantages over finite impulse response filters and other IIR filters, assuring stability and fast global convergence in the adaptation of the filter parameters. A scalable algorithm is introduced for the estimation of the poles of an adaptive OBF filter from multichannel input-output data. The set of poles, common to all the acoustic channels considered, is estimated in parallel to the adaptation of the linear filter parameters. It will be shown that the result of the identification with common poles is quite robust to variations in the room transfer function, suggesting the possibility that poles may be kept fixed after estimation.

The ability to efficiently assess and track the utility of each sensor signal is crucial to reduce the energy consumption in a wireless sensor network (WSN), e.g., by putting the sensors with low utility to sleep. Methods to track the sensor signal utility have been described for several multichannel signal estimation methods. For linear minimum mean squared error (LMMSE) estimation, the utility of a sensor signal is defined as the predicted increase in the minimum mean squared error when the sensor would be shut down. However, rather than making such a binary decision, more flexible energy-saving methods could be considered where a sensor changes internal parameters such as, e.g., the number of bits per sample, which results in noise injection in the transmitted sensor signal. We propose a generalization of the original definition of sensor signal utility to include this effect, and we show that it can be efficiently computed and tracked at hardly any computational cost compared to the already available LMMSE estimator. In addition, we illustrate how it can be used to assign a number of bits to each sensor with a greedy approach. Simulation results show that a greedy assignment based on the proposed generalized utility leads to improved results compared to the original utility measure.

In this paper, an adaptive time-frequency analysis scheme is proposed along with a synthesis scheme using an asymmetric window. The proposed scheme is suitable for audio noise reduction with a low delay in the range of 0 to 4 ms. The main novelty of the paper is the adaptive analysis scheme that can adapt to the incoming signal independently in both time and frequency by employing a complex filter on a DFT modulated filter bank. A number of adaptive time-frequency schemes are described that are suitable for low delay and low computational complexity. The adaptive time-frequency scheme is used for the computation of noise reduction gain factors, which are then adopted in a nonadaptive analysis/synthesis scheme. The synthesis scheme uses an asymmetric window to achieve a good tradeoff between low delay and a sharp frequency response. Examples are given of the adaptive analysis and measurements of the synthesis scheme are given to show that the filter bank has a gain dependent nonlinear phase response. Finally, a noise reduction task is performed that shows good performance compared to reference implementations in terms of segmental SNR and PESQ.

A general binaural noise reduction system is considered that employs the multichannel Wiener filter with partial noise estimation (MWFη) allowing for an explicit tradeoff between noise reduction and binaural noise cue preservation. In this paper, it is assumed that along with the general binaural system, a remote microphone signal with a high input signal-to-noise ratio (SNR) is available for inclusion in the MWFη. The use of this remote microphone signal with a high input SNR allows for a simultaneous increase in both noise reduction performance and preservation of the binaural noise cues. To further increase the performance, a modification to the partial noise estimation (PNE) variable, η, is proposed which relies on exploiting the aforementioned trade-off by either constraining the output SNR or binaural noise cues to the same level before and after the addition of the remote microphone signal. The validity of the theoretical results are supplemented via simulations using a binaural setup with a single speech and noise source.

In the G.fast frequency range (up to 212 MHz), the diagonal dominance structure of the channel matrix is no longer valid at the higher frequencies. As a result, the linear Zero Forcing (ZF) precoder in combination with dynamic spectrum management (DSM) is no longer near-optimal for downstream vectored G.fast transmission. To boost performance, we develop a novel low-complexity algorithm for both linear and non-linear precoding based DSM that maximizes the weighted line sum-rate under realistic per-line total power and per-tone spectral mask constraints. The algorithm relies on a Lagrange multiplier based transformation of the downstream dual decomposition approach formulation into its dual upstream formulation, together with a low-complexity iterative fixed-point formula to solve the resulting upstream problems. Simulations with measured G.fast channel data up to both 106 and 212 MHz are provided revealing a significantly increased performance of this algorithm over linear ZF precoding.

In this paper, a delayless speech enhancement scheme with zero phase distortion is proposed. It is based on a cascade of adaptive filters that predicts periodic components with a significant auto-correlation for lags larger than a value D. The adaptive filter is positioned at the output of a speech enhancement algorithm, to adjust the phase of the periodic components to the noisy signal, and to remove stochastic signal components with a significant auto-correlation only for lags smaller than D. The stochastic components are enhanced in a separate channel and mixed back together with the periodic components to give an output with no delay or phase distortion compared to the input signal. Such a scheme is useful for low-delay processing where the phase of the signal must be preserved, for instance as a front-end for spatial filtering or when the output is mixed with another source, such as the direct transmitted sound through the vent in an open hearing aid fitting.

Using periodic auditory stimuli, it is possible to evoke so-called auditory steady-state responses (ASSRs) in the brain, which can be measured using electroencephalography (EEG). They can be used to objectively estimate frequency-specific hearing thresholds, which is especially useful for early hearing assessment in newborns. The main problem is the extremely low signal-to-noise ratio (SNR), necessitating long measurements of up to an hour for a full audiometric assessment. To speed up the detection, we apply a linear spatial filter to the multi-channel EEG measurements, resulting in a new 'virtual' channel with optimal SNR. To ensure robustness, we then consider a hybrid ASSR detection method in which the original EEG channels are complemented with this virtual channel. The addition of this virtual channel successfully speeds up the detection of ASSRs by over 15 %. Furthermore our method not only speeds up the detection, but also greatly improves its sensitivity, in particular in the (clinically most relevant) lowest SNR scenarios. This could help reduce the gap that still exists between behaviourally and objectively obtained hearing thresholds.

In this paper, we consider the problem of distributed estimation of node-specific signals in a fully-connected wireless sensor network with multi-sensor nodes. The estimation relies on a data-driven design of a spatial filter, referred to as the generalized eigenvalue decomposition (GEVD)-based multi-channel Wiener filter (MWF). In non-stationary or low-SNR conditions, this GEVD-based MWF has been demonstrated to be more robust than the original MWF due to an inherent GEVD-based low-rank approximation of the sensor signal correlation matrix. In a centralized realization where a fusion center has access to all the nodes' sensor signal observations, the network-wide sensor signal correlation matrix and its low-rank approximation can be directly estimated from the sensor signals. However, in this paper we aim to avoid centralizing the sensor signal observations, in which case this network-wide correlation matrix cannot be estimated. We introduce a distributed algorithm which is able to significantly compress the broadcast signals while still converging to the centralized GEVD-based MWF as if each node would have access to all sensor signal observations.

We study a distributed node-specific signal estimation problem where the node-specific desired signals and/or the sensor observations can have partially-overlapping latent signal subspaces. First, we provide the minimum number of linear combinations of observed sensor signals that each node can broadcast to still let all other nodes achieve the network-wide Linear Minimum Mean-Square Error (LMMSE) estimate of their node-specific desired signals. Later, for a fully-connected wireless sensor network, we derive a distributed algorithm that, under some settings, allows each node to achieve the LMMSE estimate of its node-specific desired signals by broadcasting the smallest number of signals. Unlike the existing algorithms, the proposed algorithm deals with the problem of partially-overlapping node-specific interests and incomplete observability of all latent sources at the nodes. Finally, the effectiveness of the proposed technique is shown through numerical simulations.

This paper is concerned with an acoustical phenomenon called sweeping echo, which manifests itself in a room impulse response as a distinctive, continuous pitch increase. In this paper, it is shown that sweeping echoes are present (although to greatly varying degrees) in all perfectly rectangular rooms. The theoretical analysis is based on the rigid-wall image solution of the wave equation. Sweeping echoes are found to be caused by the orderly time-alignment of high-order reflections arriving from directions close to the three axial directions. While sweeping echoes have been previously observed in real rooms with a geometry very similar to the rectangular model (e.g., a squash court), they are not perceived in commonly encountered rooms. Room acoustic simulators such as the image method (IM) and finite difference time-domain (FDTD) correctly predict the presence of this phenomenon, which means that rectangular geometries should be used with caution when the objective is to model commonly encountered rooms. Small out-of-square asymmetries in the room geometry are shown to reduce the phenomenon significantly. Randomization of the image sources' position is shown to remove sweeping echoes without the need to model an asymmetrical geometry explicitly. Finally, the performance of three speech and audio processing algorithms is shown to be sensitive to strong sweeping echoes, thus highlighting the need to avoid their occurrence.

An adaptive time-frequency analysis scheme is proposed for improved real-time speech enhancement. The proposed scheme uses a filtering of the short-time Fourier transform (STFT) to obtain an adaptive resolution and is computationally efficient, since it uses fast Fourier transforms (FFTs) in the analysis and synthesis filters. Unlike previously suggested methods, the proposed method allows the time-frequency resolution to be chosen independently for each frequency bin. Perfect reconstruction is achieved by calculating the speech enhancement gains based on the adaptive analysis and then applying these to a STFT which is then filtered to allow the causal part of the gains to pass through the synthesis filter. The proposed method is shown to have superior performance compared to a fixed resolution STFT scheme with an equal time delay of 10 ms.

In this paper, we present a distributed algorithm to remove eye blink artifacts from electroencephalography (EEG) signals recorded in a modular high-density EEG system, referred to as a wireless EEG sensor network (WESN). A WESN is a particular instance of a wireless body area network for long-term non-invasive neuromonitoring, which is amenable to extreme miniaturization and low-power system design. We first propose a centralized algorithm for eye blink artifact removal (EBAR) based on the multi-channel Wiener filter (MWF). We then show how this MWF-based EBAR algorithm can be implemented in a distributed fashion to remove the eye blink artifacts in each EEG node without centralizing all the raw EEG data. Instead, the EEG nodes share fused EEG signals with each other. Nevertheless, it can be shown that the estimation performance of the distributed algorithm is equivalent to the performance of the centralized MWF, as if each EEG node had access to all the EEG channels of the WESN. This is also experimentally validated by means of recorded EEG data.

We envisage a wireless sensor network (WSN) where each node is tasked with estimating a set of node-specific desired signals that has been corrupted by additive noise. The nodes accomplish this estimation by means of the distributed adaptive node-specific estimation (DANSE) algorithm in a tree topology (T-DANSE). In this paper, we consider a network where there is at least one node with a large (virtually infinite) energy budget, which we select as the root node. We propose a modification to the signal flow of the T-DANSE algorithm where instead of each node having two-way signal communication, there is a single signal flow toward the root node of the tree topology which then broadcasts a single signal to all other nodes. We demonstrate that the modified algorithm is equivalent to the original T-DANSE algorithm in terms of the signal estimation performance, shifts a large part of the communication burden toward the highpower root node to reduce the energy consumption in the low-power nodes and reduces the input-output delay.

Binaural hearing aids allow for a wireless exchange of microphone signals between a left and a right device. A significant noise reduction performance improvement can be achieved compared to a monaural configuration (a single device) or a bilateral configuration (in which the devices work independently). In addition, the binaural localization cues, i.e. the Interaural Time Differences and Interaural Level Differences , can also be better preserved in a binaural procedure. It was previously proven that a binaural noise reduction procedure based on the Speech Distortion Weighted Multi-channel Wiener Filter (SDW-MWF) indeed preserves the speech localization cues, if all microphone signals can be exchanged. However, in practice, it may not be feasible to exchange all microphone signals between the devices, so that reduced-bandwidth SDW-MWF schemes (where only filtered combinations of microphone signals are exchanged) have to be utilized. This paper demonstrates that a straightforward reduced-bandwidth SDW-MWF scheme still preserves the speech ITD cues, but distorts the speech ILD cues, in a single speech source scenario. Novel reduced-bandwidth SDW-MWF schemes, which make use of a common spectral postfilter, are therefore introduced. Experiments in a reverberant environment demonstrate that the novel schemes reduce the ILD distortion, without severely degrading the noise reduction performance.

Iterative power difference balancing (IPDB) has recently been proposed as a first real-time dynamic spectrum management (RT-DSM) algorithm. It consists of a primal coordinate ascent approach where each coordinate step is performed using an exhaustive discrete grid line search. In this paper we present an iterative convex approximation based approach to perform the coordinate ascent search so as to reduce its computational complexity. By exploiting the problem structure, a closed-form solution is derived for the convex approximations. The resulting RT-DSM algorithm is referred to as fast IPDB (F-IPDB). Compared to IPDB, F-IPDB exhibits similar data rate performance with significantly reduced computational complexity, while also providing smoother final transmit spectra.

This paper presents an embedded-optimization-based loudspeaker precompensation algorithm using a Hammerstein loudspeaker model, i.e. a cascade of a memoryless nonlinearity and a linear finite impulse response filter. The loudspeaker precompensation consists in a per-frame signal optimization. In order to minimize the perceptible distortion incurred in the loudspeaker, a psychoacoustically motivated optimization criterion is proposed. The resulting per-frame signal optimization problems are solved efficiently using first-order optimization methods. Depending on the invertibility and the smoothness of the memoryless nonlinearity, different first-order optimization methods are proposed and their convergence properties are analyzed. Objective evaluation experiments using synthetic loudspeaker models and real loudspeakers show that the proposed loudspeaker precompensation algorithm provides a significant audio quality improvement, especially so at high playback levels.

In linear prediction of speech, the 1-norm error minimization criterion has been shown to provide a valid alternative to the 2-norm minimization criterion. However, unlike 2-norm minimization, 1-norm minimization does not guarantee the stability of the corresponding all-pole filter and can generate saturations when this is used to synthesize speech. In this paper, we introduce two new methods to obtain intrinsically stable predictors with the 1-norm minimization. The first method is based on constraining the roots of the predictor to lie within the unit circle by reducing the numerical range of the shift operator associated with the particular prediction problem considered. The second method uses the alternative Cauchy bound to impose a convex constraint on the predictor in the 1-norm error minimization. These methods are compared with two existing methods: the Burg method, based on the 1-norm minimization of the forward and backward prediction error, and the iteratively reweighted 2-norm minimization known to converge to the 1-norm minimization with an appropriate selection of weights. The evaluation gives proof of the effectiveness of the new methods, performing as well as unconstrained 1-norm based linear prediction for modeling and coding of speech.

In this paper, we propose a new framework to tackle the double-talk (DT) problem in acoustic echo cancellation (AEC). It is based on a frequency-domain adaptive filter (FDAF) implementation of the so-called prediction error method adaptive filtering using row operations (PEM-AFROW) leading to the FDAF-PEM-AFROW algorithm. We show that FDAF-PEM-AFROW is by construction related to the best linear unbiased estimate (BLUE) of the echo path. We depart from this framework to show an improvement in performance with respect to other adaptive filters minimizing the BLUE criterion, namely the PEM-AFROW and the FDAF-NLMS with near-end signal normalization. One of the contributions is to propose the instantaneous pseudo-correlation (IPC) measure between the near-end signal and the loudspeaker signal. The IPC measure serves as an indication of the effect of a DT situation occurring during adaptation. We motivate the choice of FDAF-PEM-AFROW over PEM-AFROW and FDAF-NLMS with near-end signal normalization, based on performance, computational complexity and related IPC measure values. Moreover, we use the FDAF-PEM-AFROW framework to improve several state-of-the-art variable step-size (VSS) and variable regularization (VR) algorithms. The FDAF-PEM-AFROW versions significantly outperform the original versions in every simulation. In terms of computational complexity, the FDAF-PEM-AFROW versions are themselves about two orders of magnitude cheaper than the original versions.

This paper presents low-rank approximation based multichannel Wiener filter algorithms for noise reduction in speech plus noise scenarios, with application in cochlear implants. In a single speech source scenario, the frequency-domain autocorrelation matrix of the speech signal is often assumed to be a rank-1 matrix, which then allows to derive different rank-1 approximation based noise reduction filters. In practice, however, the rank of the autocorrelation matrix of the speech signal is usually greater than one. Firstly, the link between the different rank-1 approximation based noise reduction filters and the original speech distortion weighted multichannel Wiener filter is investigated when the rank of the autocorrelation matrix of the speech signal is indeed greater than one. Secondly, in low input signal-to-noise-ratio scenarios, due to noise non-stationarity, the estimation of the autocorrelation matrix of the speech signal can be problematic and the noise reduction filters can deliver unpredictable noise reduction performance. An eigenvalue decomposition based filter and a generalized eigenvalue decomposition based filter are introduced that include a more robust rank-1, or more generally rank-R, approximation of the autocorrelation matrix of the speech signal. These noise reduction filters are demonstrated to deliver a better noise reduction performance especially in low input signal-to-noise-ratio scenarios. The filters are especially useful in cochlear implants, where more speech distortion and hence a more aggressive noise reduction can be tolerated.

There are two types of crosstalk in digital subscriber line (DSL) systems, namely near-end crosstalk (NEXT) and far-end crosstalk (FEXT). NEXT is usually much stronger than FEXT. Therefore, high-speed DSL systems transmit in preplanned disjoint downstream (DS) and upstream (US) frequency bands to avoid NEXT. Although easy for implementation, such a fixed bandplan can lead to inefficient bandwidth usage, depending on the DS and US bit rate requirements and the loop topology, particularly in so-called vectored DSL systems, which include signal coordination for FEXT cancellation. In dynamic bandplanning (DBP), each frequency band is allocated to either DS, US, or to both directions depending on the aforementioned parameters. In this paper, we consider optimal DBP for vectored DSL with linear as well as with nonlinear transmitter/receiver structures. We propose an optimal DBP algorithm for systems with disjoint DS and US bands. For systems with overlapping bands, the problem of finding the optimal transmitter/receiver filters is nonconvex and we propose two iterative algorithms based on recent optimum spectrum balancing schemes from the literature. We also study the effect of echo and US NEXT cancellation on the system performance. Finally, simulation results are provided to demonstrate that our algorithms can indeed significantly increase the achievable bit rates.

In this paper, we treat the combined signal and spectrum coordination problem in digital subscriber line (DSL) networks with linear design for transmitters and receivers. The transmission is modeled as a multitone MIMO system where each user has a number of transceivers and there is coordination between sets of users on the transmitter and on the receiver sides. We consider the possibility of an asynchronous transmission, i.e. when the transmission of DMT blocks for different users is not aligned in time. This gives rise to inter-carrier interference. Our objective is the maximization of the weighted sum of users' data rates subject to power constraints. Although this problem is well known in the literature, previous works have always based their designs on strong assumptions about the network infrastructure. In this paper, we propose a general framework and algorithm that apply for any infrastructure, including any number of users, any number of transceivers, any number of tones, any kind of coordination on both the transmitter and on the receiver sides, and synchronous or asynchronous transmission. We also do not assume any special structure of the channel matrix. Our algorithm is seen to perform very well and is polynomial time solvable.

Dynamic spectrum management is recognized as a key technique to tackle interference in multi-user multi-carrier communication systems and networks. However existing dynamic spectrum management algorithms may not be suitable when the available computation time and compute power are limited, i.e., when a very fast responsiveness is required. In this paper, we present a new paradigm, theory and algorithm for real-time dynamic spectrum management (RT-DSM). Specifically, a RT-DSM algorithm is real-time in the sense that it can be stopped at any point in time while guaranteeing a feasible and improved solution. This is enabled by the introduction of a novel difference-of-variables (DoV) transformation and problem reformulation, for which a primal coordinate ascent approach is proposed with exact line search via a logarithmically-scaled grid search. The proposed algorithm is referred to as iterative power difference balancing (IPDB). Simulations for different realistic wireline and wireless interference-limited systems demonstrate its good performance, low complexity and wide applicability under different configurations.

In this paper, we describe a distributed adaptive (time-recursive) algorithm to estimate and track the eigenvectors corresponding to the Q largest or smallest eigenvalues of the global sensor signal covariance matrix in a wireless sensor network (WSN). We only address the case of fully connected (broadcast) networks, in which the nodes broadcast compressed Q-dimensional sensor observations. It can be shown that the algorithm converges to the desired eigenvectors without explicitely constructing the global covariance matrix that actually defines them, i.e., without the need to centralize all the raw sensor observations. The algorithm allows each node to estimate (a) the node-specific entries of the global covariance matrix eigenvectors, and (b) Q-dimensional observations of the full set of sensor observations projected onto the Q estimated eigenvectors. The theoretical results are validated by means of numerical simulations.

Employing sparsity criteria in linear prediction of speech has been proven successful for several analysis and coding purposes. However, sparse linear prediction comes at the expenses of a much higher computational burden and numerical sensitivity compared to the traditional minimum variance approach. This makes sparse linear prediction difficult to deploy in real-time systems. In this paper, we present a step towards real-time implementation of the sparse linear prediction problem using hand-tailored interior-point methods. Using compiled implementations the sparse linear prediction problems corresponding to a frame size of 20ms can be solved on a standard PC in approximately 2ms and orders faster than with general purpose software.

We focus on the effects of intercarrier interference (ICI) in digital subscriber line (DSL) systems due to asynchronous discrete multitone (DMT) transmission and its impact on dynamic spectrum management (DSM). ICI arises when the DMT blocks of interfering users in the network are not aligned in time and it may significantly impact the system performance. Our contribution is the derivation of a simple and accurate model for the effect of the ICI. We propose both an ICI model based on the particular delay between two users and an ICI model averaged over the delays between two users. Simulation results show that an accurate characterization of the ICI positively impacts the performance of DSM solutions.

This paper presents multichannel Wiener filtering-based algorithms for noise reduction in cochlear implants. In a single speech scenario, the autocorrelation matrix of the speech signal can be approximated by a rank-1 matrix. It is then possible to derive noise reduction filters that deliver improved signal-to-noise ratio performance. The link between these different filters is investigated here and an eigenvalue decomposition based algorithm is demonstrated to be more stable at low input signal-to-noise ratio compared to previous algorithms.

A wireless acoustic sensor network is envisaged that is composed of distributed nodes each with several microphones. The goal of each node is to perform signal enhancement, by means of a multi-channel Wiener filter (MWF), in particular to produce an estimate of a desired speech signal. In order to reduce the number of broadcast signals between the nodes, the distributed adaptive node-specific signal estimation (DANSE) algorithm is employed. When each node broadcasts only linearly compressed versions of its microphone signals, the DANSE algorithm still converges as if all uncompressed microphone signals were broadcast. Due to the iterative and statistical nature of the DANSE algorithm several blocks of data are needed before a node can update its node-specific parameters leading to poor tracking performance. In this paper a sub-layer algorithm is presented, that operates under the primary layer DANSE algorithm, which allows nodes to update their parameters during every new block of data and is shown to improve the tracking performance in time-varying environments.

The use of a cyclic prefix (CP) in orthogonal frequency-division multiplexing (OFDM) allows to avoid inter-carrier interference (ICI), but also diminishes the efficiency of the modulation scheme. We study the problem of jointly optimizing the CP length and the discrete bit and power-loading under ICI in OFDM, exemplified by its application to power line communication (PLC). We derive a low-complexity linear programming (LP) based performance upper-bound which is applicable to analyze the suboptimality of bit-loading heuristics under varying CP lengths. Furthermore, a branch-and-bound heuristic is proposed for low-complexity CP-length selection. While we do not expect the LP bound to be tight in all possible interference scenarios, our simulation results support the hypothesis that it is sufficiently tight in the range of near-optimal CP lengths in order to provide guidelines for optimal as well as heuristic CP-length selection.

Time domain synchronous OFDM (TDS-OFDM) can achieve a higher spectrum efficiency than standard cyclic prefix OFDM (CP-OFDM). Currently, it can support constellations up to 64QAM, but cannot support higher-order constellations like 256QAM due to the residual mutual interferences between the pseudorandom noise (PN) guard interval and the OFDM data block. To solve this problem, we break the traditional approach of iterative interference cancellation and propose the idea of using multiple inter-block-interference (IBI)-free regions of very small size to realize simultaneous multi-channel reconstruction under the framework of structured compressive sensing, whereby the sparsity nature of wireless channels as well as the characteristic that path delays vary much slower than path gains are jointly exploited. In this way, the mutually conditional time-domain channel estimation and frequency-domain data demodulation in TDS-OFDM can be decoupled without the use of IBI removal. We then propose the adaptive simultaneous orthogonal matching pursuit (A-SOMP) algorithm with low complexity to realize accurate multi-channel reconstruction, whose performance is close to the Cramér-Rao lower bound (CRLB). Simulation results confirm that the proposed scheme can support 256QAM without changing the current signal structure, so the spectrum efficiency can be increased by about 30%.

This paper deals with the problem of power allocation in a multi-user, multicarrier communication system. This problem arises, for example, in wireless OFDM or digital subscriber line networks. Users transmitting concurrently on the same sub-carriers cause interference to each other, and this interference is a serious limitation for system performance. We consider the problem of maximizing the weighted sum of users' data rates. The optimization variables are vectors containing the powers for all users on each tone. The problem is intrinsically difficult due to the non-concavity of the utility function. We propose to change the (cartesian) power vector by its polar coordinates vector equivalent. The main contribution of this paper is to show that at least for one dimension of the polar coordinates vector, the radius, the problem is concave and thus easy to solve. We develop an algorithm based in such a polar coordinates formulation to exploit such concavity. It is demonstrated that the algorithm we propose saves considerably on computational cost compared to previous algorithms.

Vectoring is recognized as a very efficient technique to tackle crosstalk interference in multi-user DSL systems. In this paper we focus on sparse linear zero-forcing based vectoring schemes with a shared resource pool, which are known to obtain a good trade-off between online computational complexity and near-optimal performance. We propose a new system setup with a controller that dynamically (in time) optimizes and allocates computational complexity resources taking into account variable data rate demands with real-time constraints. The controller is designed to minimize the online computational complexity over time and allows for flexible fixed and variable data rate allocations. Simulation results with variable real-time constrained video streams for a realistic downstream VDSL scenario demonstrate that this novel controller-based approach allows to significantly reduce the average as well as peak online computational complexity.

Widely linear (WL) filtering has been shown to improve performance compared to linear filtering due to its ability to incorporate the non-circularity of the signal statistics. However there has been some inconsistency in its application, specifically when constructing complex signals from real signals, which has recently been considered in the context of speech enhancement in binaural or stereo systems. This letter shows that the corresponding WL filtered output contains exactly the same information as the linear filter output while increasing the computational complexity and memory requirements.

The restoration of clipped audio signals, commonly known as declipping, is important to achieve an improved level of audio quality in many audio applications. In this paper, a novel declipping algorithm is presented, jointly based on the theory of compressed sensing (CS) and on well-established properties of human auditory perception. Declipping is formulated as a sparse signal recovery problem using the CS framework. By additionally exploiting knowledge of human auditory perception, a novel perceptual compressed sensing (PCS) framework is devised. A PCS-based declipping algorithm is proposed which uses ℓ1-norm type reconstruction. Comparative objective and subjective evaluation experiments reveal a significant audio quality increase for the proposed PCS-based declipping algorithm compared to CS-based declipping algorithms.

Acoustic echo cancellation (AEC) is used in speech communication systems where the existence of echoes degrades the speech intelligibility. Standard approaches to AEC rely on the assumption that the echo path to be identified can be modeled by a linear filter. However, some elements introduce nonlinear distortion and must be modeled as nonlinear systems. Several nonlinear models have been used with more or less success. The kernel affine projection algorithm (KAPA) has been successfully applied to many areas in signal processing but not yet to nonlinear AEC (NLAEC). The contribution of this paper is three-fold: (1) to apply KAPA to the NLAEC problem, (2) to develop a sliding-window leaky KAPA (SWL-KAPA) that is well suited for NLAEC applications, and (3) to propose a kernel function, consisting of a weighted sum of a linear and a Gaussian kernel. In our experiment set-up, the proposed SWL-KAPA for NLAEC consistently outperforms the linear APA, resulting in up to 12 dB of improvement in ERLE at a computational cost that is only 4.6 times higher. Moreover, it is shown that the SWL-KAPA outperforms, by 4-6 dB, a Volterra-based NLAEC, which itself has a much higher 413 times computational cost than the linear APA.

This paper presents a binaural approach to integrated active noise control and noise reduction in hearing aids and aims at demonstrating that a binaural setup indeed provides significant advantages in terms of the number of noise sources that can be compensated for and in terms of the causality margins.

Different precoder and equalizer structures have been proposed in literature for vectored digital subscriber lines (DSLs). In this paper, we assess the relative merit of advanced complex precoders and equalizers in practical DSL scenarios. Using extensive computer simulations, the achievable bit rates are investigated in the presence of alien crosstalkers, assuming a beta distribution model for crosstalk couplings. Vectoring in conventional as well as phantom mode (PM) VDSL2 transmission is considered. Vectoring techniques at the access-node (AN) are studied such as the zero-forcing equalizer (ZFE), ZF generalized decision feedback equalizer (ZF-GDFE), linear minimum mean squared error (MMSE), and MMSE-GDFE for upstream (US) scenarios and zero-forcing precoder (ZFP) for downstream (DS) scenarios. Vectoring is also considered at the customer premises (CP) in PM transmission. The results show that when there are no alien crosstalkers, the low-complexity ZFE allows reaching crosstalk-free rates in US direction. However using an MMSE-GDFE and if the noise spatial covariance information is assumed static, considerably higher bit rates can be achieved for long loops in the presence of alien crosstalkers. This rate increase comes at the expense of increased delay. In DS direction, the ZFP can achieve crosstalk-free rates in the absence of alien crosstalkers. The results also show that the use of vectoring techniques at the CP in PM transmission does not increase the achievable bit rates in US or DS direction when vectoring is already employed at the AN.

In this paper, we revisit the distributed total least squares (D-TLS) algorithm, which operates in an ad hoc sensor network where each node has access to a subset of the equations of an overdetermined set of linear equations. The D-TLS algorithm computes the total least squares (TLS) solution of the full set of equations in a fully distributed fashion (without fusion center). We modify the D-TLS algorithm to eliminate the large computational complexity due to an eigenvalue decomposition (EVD) at every node and in each iteration. In the modified algorithm, a single power iteration (PI) is performed instead of a full EVD computation, which significantly reduces the computational complexity. Since the nodes then do not exchange their true eigenvectors, the theoretical convergence results of the original D-TLS algorithm do not hold anymore. Nevertheless, we find that this PI-based D-TLS algorithm still converges to the network-wide TLS solution, under certain assumptions, which are often satisfied in practice. We provide simulation results to demonstrate the convergence of the algorithm, even when some of these assumptions are not satisfied.

The aim of this paper is to improve the performance of existing speech distortion weighted multi-channel Wiener filter (SDW-MWFμ) based noise reduction (NR) algorithms. It is well known that for the SDW-MWFμthe improved NR performance comes at the cost of higher speech distortion when a fixed speech distortion weighting factor is used. In this paper we propose two psychoacoustically motivated weighting factor selection strategies, devised to exploit masking properties of the human ear. Experimental results based on PESQ scores, SNR improvement, and signal distortion confirm that both proposed psychoacoustically motivated weighting factor selection strategies do improve the NR performance compared to using a fixed weighting factor. In some of the analyzed scenarios, the fixed weighting factor approach is even seen to degrade the PESQ scores, while the psychoacoustically motivated approaches are seen to significantly improve the PESQ scores in all of the analyzed scenarios.

In acoustic echo cancellation (AEC) applications, oftentimes an acoustic path from a loudspeaker to a microphone is estimated by means of a linear adaptive filter. However, loudspeakers introduce nonlinear distortions which may strongly degrade the adaptive filter performance, thus nonlinear filters have to be considered. This paper proposes two adaptive algorithms namely the parallel and cascade sliding-window kernel based affine projection algorithm (PSW-KAPA and CSW-KAPA) to solve the problem of nonlinear AEC (NLAEC) while keeping the computational complexity low. They are based on a leaky KAPA which employs the theory and algorithms of kernel methods. The basic concept is to perform adaptive filtering in a linear space that is nonlinearly related to the original input space. A kernel specifically designed for acoustic applications is proposed, which consists in a weighted sum of the linear and the Gaussian kernels. The motivation is basically to separate the problem into linear and nonlinear subproblems. The weights in the kernel also impose different forgetting mechanisms in the sliding window which in turn translates to a more flexible regularization. Simulation results show that PSW-KAPA and CSW-KAPA consistently outperform the linear NLMS, and generalize well both in high and low linear to nonlinear ratio (LNLR).

A wireless acoustic sensor network is considered with spatially distributed microphones which observe a desired speech signal that has been corrupted by noise. In order to reduce the noise the signals are sent to a fusion center where they are processed with a centralized rank-1 multi-channel Wiener filter (R1-MWF). The goal of this work is to efficiently compute an assessment of the contribution of each individual microphone with respect to either signal-to-noise ratio (SNR), signal-to-distortion ratio (SDR) or the minimized cost function referred to as the utility. These performance measures are derived by exploiting unique properties of the R1-MWF which can be computed efficiently from values that are known from the current signal estimation process. The performance measures may be used in unison or individually to determine the contributions of each microphone and help facilitate in selecting only a subset of the available signals in order to meet the bandwidth and power constraints of the system.

In this paper, we consider optimal dynamic spectrum management (DSM) for a downstream (DS) DSL scenario in which users are divided into a few separate groups, where vector encoding based signal coordination can be applied in each group and spectrum coordination is possible for all users. This can be seen as a mixed interference/broadcast channel (IF/BC) scenario. We investigate several candidates for vector encoding the signals inside the groups, including the linear zero-forcing (ZF) pre-compensator, ZF Tomlinson-Harashima pre-coder (THP), optimal linear pre-compensator (OLP), and THP with optimal transmit filters. The calculation of the optimal transmit filters (for both the linear pre-compensator and THP) is a non-convex problem. To resolve this problem, we develop a generalized duality between the IF/BC and IF/multiple-access channel (MAC). In order to achieve the highest data rates, optimal spectrum balancing (OSB) is applied to all users on top of the vector encoding inside the groups. Simulation results show that the grouped THP with optimal transmit filters achieves considerably higher bit rates than the other schemes. The resulting algorithm is referred to as the IF/BC-OSB algorithm, and encompasses the OSB (i.e., IF-OSB) and the earlier developed BC-OSB algorithm as special cases.

Clipping is an essential signal processing operation in many real-time audio applications, yet the use of existing clipping techniques generally has a detrimental effect on the perceived audio signal quality. In this paper, we present a novel multidisciplinary approach to clipping which aims to explicitly minimize the perceptible clipping-induced distortion by embedding a convex optimization criterion and a psychoacoustic model into a frame-based algorithm. The core of this perception-based clipping algorithm consists in solving a convex optimization problem for each time frame in a fast and reliable way. To this end, three different structure-exploiting optimization methods are derived in the common mathematical framework of convex optimization, and corresponding theoretical complexity bounds are provided. From comparative audio quality evaluation experiments, it is concluded that the perception-based clipping algorithm results in significantly higher objective audio quality scores than existing clipping techniques. Moreover, the algorithm is shown to be capable to adhere to real-time deadlines without making a sacrifice in terms of audio quality.

The aim of this paper is to provide an overview of Sparse Linear Prediction, a set of speech processing tools created by introducing sparsity constraints into the linear prediction framework. These tools have shown to be effective in several issues related to modeling and coding of speech signals. For speech analysis, we provide predictors that are accurate in modeling the speech production process and overcome problems related to traditional linear prediction. In particular, the predictors obtained offer a more effective decoupling of the vocal tract transfer function and its underlying excitation, making it a very efficient method for the analysis of voiced speech. For speech coding, we provide predictors that shape the residual according to the characteristics of the sparse encoding techniques resulting in more straightforward coding strategies. Furthermore, encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application to sparse linear predictive coding. The proposed estimators are all solutions to convex optimization problems, which can be solved efficiently and reliably using, e.g., interior-point methods. Extensive experimental results are provided to support the effectiveness of the proposed methods, showing the improvements over traditional linear prediction in both speech analysis and coding.

This paper focuses on speech enhancement in hearing aids and presents an integrated approach to active noise control and noise reduction which is based on an optimization over a zone-of-quiet generated by the active noise control. A basic integrated active noise control and noise reduction scheme has been introduced previously to tackle secondary path effects and effects of noise leakage through an open fitting. This scheme however, only takes the sound pressure at the ear canal microphone into account. For an integrated active noise control and noise reduction scheme to be efficient, it is desired to achieve active noise control at the eardrum which in practice is away from the ear canal microphone. In some cases, it can also be desired to achieve noise control over a zone not limited to a single point. Two different schemes are presented. The first scheme is based on a mean squared error criterion expressed at a remote point (RP) away from the ear canal microphone and the second scheme is based on an average mean squared error criterion over a desired zone-of-quiet. They are both compared experimentally with the original scheme for both active noise control and integrated active noise control and noise reduction, respectively. The remote-point approach then allows to restore the performance of the original scheme at the desired remote point while the zone-of-quiet approach allows to increase performance up to 3 dB on the desired zone-of-quiet.

In this paper, we investigate joint level 2 and 3 dynamic spectrum management (joint DSM 2/3) for downstream (DS) DSL. We consider a DS scenario in which users are divided into a few separate groups, where vector encoding based signal coordination can be applied in each group and spectrum coordination is possible for all users. This can be seen as a mixed interference/broadcast channel (IF/BC) scenario. In order to obtain the optimal transmitter structure, we develop a generalized duality between the vector broadcast and multiple-access channel (MAC) for scenarios in which partial signal coordination is available among users. This theory together with optimal spectrum balancing (OSB) is exploited to calculate the jointly optimal filters and transmit powers for non-linear vector dirty paper coding structures (in the form of Tomlinson-Harashima precoder (THP)) in the groups. The proposed scheme is compared to several other joint DSM 2/3 algorithms for DS DSL. Simulation results show that the proposed scheme (referred to as the IF/BC-OSB algorithm) achieves considerably higher bit rates than the other schemes. IF/BC-OSB encompasses the earlier developed BC-OSB algorithm as a special case. A simplified version of IF/BC-OSB avoiding exhaustive search with near-optimal performance is also proposed.

DSL broadband access suffers from crosstalk among different lines within the same cable bundle. Dynamic spectrum management (DSM) refers to a set of techniques to mitigate the impact of crosstalk leading to spectacular performance gains. DSM research has mainly aimed at physical layer performance metrics, such as data rates and transmit powers. However, for many applications higher-layer performance metrics, such as throughput and delay, may be much more important to improve user satisfaction. In this paper, we provide a cross-layer DSM framework to study throughput and delay performance by looking at scheduling and DSM together. We show how optimal scheduling can be combined with both optimal and suboptimal DSM and provide throughput-optimal scheduling algorithms which require only polynomial complexity. We analytically study the impact on delay performance of achieving throughput-optimality with suboptimal DSM compared to optimal DSM. We then present extensions that significantly improve delay performance by exploiting the specific structure of the problem, such as the temporal-spectral correlation property. Furthermore, we propose a second cross-layer DSM framework that achieves throughput-optimal scheduling with suboptimal DSM, but in addition also significantly reduces overall power consumption. Finally, we analyze and quantify the tradeoff between throughput, delay and power consumption for concrete DSL scenarios.

We consider distributed linearly constrained minimum variance (LCMV) beamforming in a wireless sensor network. Each node computes an LCMV beamformer with node-specific constraints, based on all sensor signals available in the network. A node has a local sensor array, and compresses its sensor signals to a signal with fewer channels, which is then shared with other nodes in the network. The compression rate depends inversely on the total number of linear constraints. Even though a significant compression is obtained, each node is able to generate the same outputs as a centralized LCMV beamformer, as if all sensor signals are available to every node. Since the distributed LCMV algorithm exploits a similar parametrization as previously developed distributed unconstrained MMSE signal estimation algorithms, it has similar dynamics and convergence properties. We provide simulation results to demonstrate the optimality and convergence of the algorithm.

The Speech Distortion Weighted Multichannel Wiener Filter (SDW-MWF) is a promising multi-microphone noise reduction technique, in particular for hearing aid applications. Its benefit over other techniques has been shown in several theoretical and experimental contributions. In theoretical studies, a single target speech source is commonly assumed, as this facilitates the analysis. In this contribution, we first prove that an algorithm, that implicitly assumes a single target speech source, is also more robust against estimation errors in the speech second order statistics, compared to a standard SDW-MWF algorithm. Secondly, as any SDW-MWF algorithm relies on a voice activity detector (VAD), a novel VAD-robust extension is also proposed. It is shown theoretically and through experiments with a realistic VAD that the new algorithm indeed achieves a good performance, even at low input SNR's where the VAD error rate is high.

Clipping is a necessary signal processing operation in many real time audio applications, yet it often reduces the sound quality of the signal. The recently proposed perception-based clipping algorithm has been shown to significantly outperform other clipping techniques in terms of objective sound quality scores. However, the real-time solution of the optimization problems that form the core of this algorithm, poses a challenge. In this paper, a fast gradient projection optimization method is proposed and incorporated into the perception based clipping algorithm. The optimization method will be shown to have an extremely low computational complexity per iteration, allowing the perception-based clipping algorithm to be applied in real-time for a broad range of clipping factors.

Dynamic spectrum management (DSM) aims to optimally al locate per-user transmit spectra so that the effect of multiuser interference (crosstalk) is minimized and the capabilities of the network are maximized. Work on DSM has progressed considerably over the past decade. Most of this work, however, deals with the idealized situation of perfect synchronization amongst the multiple users. This work deals with a more realistic albeit more difficult situation, i.e we consider the DSM problem with asynchronous crosstalk. We propose a novel solution for this problem, one that is mainly based on the approximate summability of the crosstalk damage ratio (CDR). Performance of the algorithm is assessed through numerical experiments and is shown to be better than the performance of state-of-the-art methods.

In this paper, a multi-channel noise reduction algorithm is presented based on a Speech Distortion Weighted Multi-channel Wiener Filter (SDW-MWF) approach that incorporates a flexible weighting factor. A typical SDW-MWF uses a fixed weighting factor to trade-off between noise reduction and speech distortion without taking speech presence or speech absence into account. Consequently, the improvement in noise reduction comes at the cost of a higher speech distortion since the speech dominant segments and the noise dominant segments are weighted equally. Based on a two-state speech model with a noise-only and a speech+noise state, a solution is introduced that allows for a more flexible trade-off between noise reduction and speech distortion. Experimental results with hearing aid scenarios demonstrate that the proposed SDW-MWF incorporating the flexible weighting factor improves the signal-to-noise-ratio with lower speech distortion compared to a typical SDW-MWF and the SDW-MWF incorporating the conditional speech presence probability (SPP).

Lagrange dual optimization technique (LDO) is a powerful tool for solving constrained optimization problems in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing systems (OFDM) and is generally considered to be optimal in the literature. LDO relaxes a constrained problem into an unconstrained dual problem using Lagrange multipliers. To solve the dual problem, the optimal value of the Lagrange multipliers should be found. The Lagrange multipliers are usually determined in an iterative process and reducing the number of iterations is of crucial importance to obtain systems with manageable computational complexity. In this paper, we show that for the LDO to be optimal in optimal spectrum balancing of DSL, the joint rate and power region (JRPR) should be strictly convex. Moreover, we propose a new LDO based algorithm with two basic advantages. Firstly, the computational complexity of the algorithm is logarithmic in the desired precision. Secondly, the algorithm can be used to find the optimal solution even for the cases with non-strictly convex JRPR. Our results can potentially be generalized to a wider range of optimization problems in the context of MIMO-OFDM and other complex separable systems.

An essential part of the bachelor program in Electrical Engineering at the Katholieke Universiteit Leuven since many years is a number of design projects that teach the basics of electronics design engineering to our students. The major project consists of an academic-year-long design task that is carried out by a group of about 20 students. These students are trained to operate as a multidisciplinary team based of sub- teams that handle the different design problems in a multidisciplinary way. Teaching assistants are added to the team to serve either as expert designer or as project leader of a team. In this way the students learn a lot about the circuits, systems and software they conceive but they also develop their teamwork, leadership and presentation skills. In this paper we will give an overview of the technical content of this project and describe the way how it is organised. Taking this project as an example, the rationale behind this kind of interdisciplinary design projects is depicted.

The acoustic feedback problem has intrigued researchers over the past five decades, and a multitude of solutions has been proposed. In this survey paper, we aim to provide an overview of the state of the art in acoustic feedback control, to report results of a comparative evaluation with a selection of existing methods, and to cast a glance at the challenges for future research.

The speech distortion weighted multichannel Wiener filter (SDW-MWF) is a promising multi-microphone noise reduction technique, in particular for hearing aid applications. Its benefit over other single- and multi-microphone techniques has been shown in several previous contributions, theoretically as well as experimentally. In theoretical studies, it is usually assumed that there is a single target speech source. The filter can then be decomposed into a conceptually interesting structure, i.e., into a spatial filter (related to other known techniques) and a single-channel postfilter, which then also allows for a performance analysis. Unfortunately, it is not straightforward to make a robust practical implementation based on this decomposition. Instead, a general SDW-MWF implementation, which only requires a (relatively easy) estimation of speech and noise correlation matrices, is mostly used in practice. This paper features a theoretical study and experimental validation on a binaural hearing aid setup of this standard SDW-MWF implementation, where the effect of estimation errors in the second-order statistics is analyzed. In this case, and for a single target speech source, the standard SDW-MWF implementation is found not to behave as predicted theoretically. Second, two recently introduced alternative filters, namely the rank-one SDW-MWF and the spatial prediction SDW-MWF, are also studied in the presence of estimation errors in the second-order statistics. These filters implicitly assume a single target speech source, but still only rely on the speech and noise correlation matrices. It is proven theoretically and illustrated through experiments that these alternative SDW-MWF implementations behave close to the theoretical optimum, and hence outperform the standard SDW-MWF implementation.

Orthogonal spreading codes play an essential role in code-division multiple-access (CDMA) systems by eliminating multiple-access interference (MAI). In this paper, a novel orthogonal spreading code has been proposed for spectrally-encoded (SE) CDMA, a.k.a., spread-time (ST) CDMA with arbitrary pulse shape. It has been shown that it is possible to retain the orthogonality of the code in the presence of tail truncation by time windowing and in a general multipath fading channel in which users experience different frequency selectivity just by modifying the user codewords. Simulation results show that the proposed codes can achieve single user performance when the code length is twice the number of users.

Dynamic spectrum management (DSM) refers to a wide range of techniques for counteracting crosstalk in digital subscriber line (DSL) networks. DSM is categorized into three levels based on the degree of coordination among users. In this article, we investigate optimal joint level 2 and 3 DSM for upstream DSL. We will discuss the difficulties of finding the universally optimal solution and we propose an optimal algorithm, referred to as IF/MAC-OSB, under some practical and implementation assumptions for this problem. Using computer simulations, we show that IF/MAC-OSB is capable of increasing the user bit rates considerably compared to several other DSM techniques. The proposed algorithm involves using the minimum mean squared error (MMSE)-generalized decision feedback equalizer (GDFE) together with Lagrange dual optimization. We address several aspects of the problem including the optimal decoding order in the GDFE receiver, GDFE error propagation, and the computational complexity of the algorithm. We also study effects of channel model randomness and upstream power back-off utilization on the performance of the algorithm.

The design of crosstalk mitigation techniques for DSL broadband access systems has mainly focused on physical layer transmit rate maximization. However, for certain applications, upper-layer performance metrics like network throughput, stability and delay performance may be more relevant. In this paper, we present a number of dynamic resource allocation based algorithms for partial crosstalk cancellation (PCC) that focus on these upper-layer metrics. A first algorithm focuses on preserving transmission queueing stability while maximizing the transmit rate. This is then extended towards budget adaptive algorithms, which dynamically adapt the PCC so as to improve the resource efficiency and to obtain a desirable trade-off between delay performance and resource consumption. Simulation results demonstrate the improved stability of the proposed algorithms and the obtained trade-off between delay performance and resource consumption.

In this paper, we propose an energy-based technique to track the power of multiple simultaneous speakers using an ad hoc microphone array with unknown microphone positions. By considering the short-term power of the microphone signals, the problem can be converted into a non-negative blind source separation (NBSS) problem. By exploiting the prior knowledge that the source signals are non-negative and well-grounded, very efficient algorithms can be used to solve this NBSS problem, based only on second order statistics. We provide simulation results that demonstrate the effectiveness of the presented algorithm.

Linear prediction of speech based on 1-norm minimization has already proved to be an interesting alternative to 2-norm minimization. In particular, choosing the 1-norm as a convex relaxation of the 0-norm, the corresponding linear prediction model offers a sparser residual better suited for coding applications. In this paper, we propose a new speech modeling technique based on reweighted 1-norm minimization. The purpose of the reweighted scheme is to overcome the mismatch between 0-norm minimization and 1-norm minimization while keeping the problem solvable with convex estimation tools. Experimental results prove the effectiveness of the reweighted 1-norm minimization, offering better coding properties compared to 1-norm minimization.

In this paper, we describe a new approach to cope with packet loss in speech coders. The idea is to split the information present in each speech packet into two components, one to independently decode the given speech frame and one to enhance it by exploiting inter-frame dependencies. The scheme is based on sparse linear prediction and a redefinition of the analysis-by-synthesis process. We present Mean Opinion Scores for the presented coder with different degrees of packet loss and show that it performs similarly to frame dependent coders for low packet loss probability and similarly to frame independent coders for high packet loss probability. We also present ideas on how to make the coder work synergistically with the channel loss estimate.

Acoustic feedback is a well-known problem in hearing aids, which is caused by the undesired acoustic coupling between the loudspeaker and the microphone. Acoustic feedback limits the maximum amplification that can be used in the hearing aid without making it unstable. The goal of adaptive feedback cancellation (AFC) is to adaptively model the feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the feedback path model is the correlation between the near-end signal and the loudspeaker signal, which is caused by the closed signal loop. A possible solution to this problem is to use the prediction error method (PEM)-based AFC with a linear prediction (LP) model for the near-end signal. In this paper, a modification to the PEM-based AFC is presented where the LP model is replaced by a sinusoidal near-end signal model. More specifically, it is shown that using frequency estimation techniques to estimate the sinusoidal near-end signal model improves the performance of the PEM-based AFC compared to using a LP model. Simulation results for a hearing aid scenario indicate a significant improvement in terms of misadjustment and maximum stable gain increase.

In current DSL systems, crosstalk is a major source of performance degradation. In the case of in-domain crosstalk and AWGN, it has been shown that the crosstalk can be effectively mitigated using a linear zero-forcing canceler. Furthermore, the complexity can be reduced by only canceling the crosstalk from major crosstalkers on each tone, which is reffered to as partial cancellation. However, such approach does not work e.g. in the case of out-of-domain (alien) crosstalk. As alien crosstalk is spatially correlated, the zero-forcing canceler performs very poorly, hence an alternative linear canceler has to be examined. In this paper, we demonstrate that an MMSE crosstalk canceler provides improved performance compared to the zero-forcing canceler in this scenario and additionally, we also present an efficient algorithm to perform MMSE partial cancellation.

Encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application in the context of speech coding based on sparse linear prediction. In particular, a compressed sensing method can be devised to compute a sparse approximation of speech in the residual domain when sparse linear prediction is involved. We compare the method of computing a sparse prediction residual with the optimal technique based on an exhaustive search of the possible nonzero locations and the well known Multi-Pulse Excitation, the first encoding technique to introduce the sparsity concept in speech coding. Experimental results demonstrate the potential of compressed sensing in speech coding techniques, offering high perceptual quality with a very sparse approximated prediction residual.

In this letter, analytical expressions are derived for the power spectral density (PSD) of orthogonal frequency division multiplex (OFDM) signals employing a cyclic prefix (CP-OFDM) or zero padding (ZP-OFDM) time guard interval. Under the relatively weak assumptions that (i) the data are independent and identically distributed on all OFDM subcarriers and (ii) the OFDM pulse shape is sufficiently localized in time, simple closed-form PSD expressions can be obtained. These expressions are then compared to existing OFDM PSD expressions and validated by inspecting the power spectra of some standardized OFDM signals.

Binaural hearing aids use microphone signals from both left and right hearing aid to generate an output signal for each ear. The microphone signals can be processed by a procedure based on speech distortion weighted multichannel Wiener filtering (SDW-MWF) to achieve significant noise reduction in a speech + noise scenario. In binaural procedures, it is also desirable to preserve binaural cues, in particular the interaural time difference (ITD) and interaural level difference (ILD), which are used to localize sounds. It has been shown in previous work that the binaural SDW-MWF procedure only preserves these binaural cues for the desired speech source, but distorts the noise binaural cues. Two extensions of the binaural SDW-MWF have therefore been proposed to improve the binaural cue preservation, namely the MWF with partial noise estimation (MWF-eta) and MWF with interaural transfer function extension (MWF-ITF). In this paper, the binaural cue preservation of these extensions is analyzed theoretically and tested based on objective performance measures. Both extensions are able to preserve binaural cues for the speech and noise sources, while still achieving significant noise reduction performance.

This paper presents combined active noise control and noise reduction schemes for hearing aids to tackle secondary path effects and effects of noise leakage through an open fitting. While such leakage contributions and the secondary acoustic path from the loudspeaker to the tympanic membrane are usually not taken into account in standard noise reduction systems, they appear to have a non-negligible impact on the final signal-to-noise ratio. Using a noise-reduction algorithm and an active noise control system in cascade may be efficient as long as the causality margin of the system is large enough. Putting the two functional blocks in parallel and then integrating them is found to lead to a more robust algorithm. A Filtered-x Multichannel Wiener Filter is presented and applied to integrate noise reduction and active noise control. The cascaded scheme and the integrated scheme are compared experimentally with a Multichannel Wiener Filter in a classic noise reduction framework without active noise control, where the integrated scheme is found to provide the best performance.

This paper presents a method for high-resolution fundamental frequency$(F_{0})$estimation based on subspaces decomposed from a frequency-selective data model, by effectively splitting the signal into a number of subbands. The resulting estimator is termed frequency-selective harmonic MUSIC (F-HMUSIC). The subband-based approach is expected to ensure computational savings and robustness. Additionally, a method for automatic subband signal activity detection is proposed, which is based on information-theoretic criterion where no subjective judgment is needed. The F-HMUSIC algorithm exhibits good statistical performance when evaluated with synthetic signals for both white and colored noises, while its evaluation on real-life audio signal shows the algorithm to be competitive with other estimators. Finally, F-HMUSIC is found to be computationally more efficient and robust than other subspace-based$F_{0}$estimators, besides being robust against recorded data with inharmonicities.

To gradually expand their networks, operators deploy new xDSL technologies from remote terminals (RT's) closer to the customer. However, since xDSL lines deployed from an RT can share a binder with lines deployed from the central office (CO), a near-far problem is introduced and crosstalk may cause a severe performance degradation for the CO-deployed lines. RT lines have to be careful about how to allocate transmit power to the transmission frequencies, such that the impact on the CO lines is acceptable. Noise impact (NI) methods have been proposed based on a target noise shape generated by the disturbers (RT lines). In this paper new methods based on the actual impact on the bit rate of the victims (CO lines) are proposed. These bit impact (BI) methods closely approximate the optimal power allocation.

We introduce a distributed adaptive estimation algorithm operating in an ideal fully connected sensor network. The algorithm estimates node-specific signals at each node based on reduced-dimensionality sensor measurements of other nodes in the network. If the node-specific signals to be estimated are linearly dependent on a common latent process with a low dimension compared to the dimension of the sensor measurements, the algorithm can significantly reduce the required communication bandwidth and still provide the optimal linear estimator at each node as if all sensor measurements were available in every node. Because of its adaptive nature and fast convergence properties, the algorithm is suited for real-time applications in dynamic environments, such as speech enhancement in acoustic sensor networks.

In this paper two multichannel noise reduction strategies are compared in the context of binaural hearing aids. Recently a novel noise reduction method based on spatial-temporal prediction (STP) was introduced which showed an improvement over methods based on multichannel Wiener filtering, although at the cost of a higher computational complexity. Whereas this newmethod operates in the time domain, hearing aids typically demand faster frequency domain implementations. In this paper we therefore propose a frequency domain equivalent of the STP method. The performance of the new so-called spatial prediction (SP) method will be compared to a frequency domain implementation of the speech distortion weighted multichannel Wiener filter (SDW-MWF), theoretically as well as based on simulations with a binaural hearing aid configuration. It will be shown that the frequency domain SP method still achieves some improvement over the SDW-MWF, at the cost of higher computational complexity.

In low bit-rate coders, the near-sample and far-sample redundancies of the speech signal are usually removed by a cascade of a short-term and a long-term linear predictor. These two predictors are usually found in a sequential and therefore suboptimal approach. In this paper we propose an analysis model that jointly finds the two predictors by adding a regularization term in the minimization process to impose sparsity constraints on a high order predictor. The result is a linear predictor that can be easily factorized into the short-term and long-term predictors. This estimation method is then incorporated into an algebraic code excited linear prediction scheme and shows to have a better performance than traditional cascade methods and other joint optimization methods, offering lower distortion and higher perceptual speech quality.

In this paper, we present a novel method for joint estimation of the order and fundamental frequency of a set of harmonically related sinusoids. This method uses a subband based approach to estimate the involved parameters using subspace techniques, and the resulting algorithm is termed frequency-selective harmonic MUSIC (F-HMUSIC). The performance of F-HMUSIC is evaluated and compared to both harmonic MUSIC (HMUSIC) and Cramer-Rao lower bound (CRLB). Especially, in a low signal-to-noise ratio (SNR) with colored noise scenarios, where F-HMUSIC outperforms HMUSIC. F-HMUSIC is concluded to be more computationally efficient and more robust against colored noise than other subspace based fundamental frequency estimators.

The problem of estimating frequencies of sinusoids in noise has been studied intensively by the signal processing community during the last decades. Traditionally high resolution subspace-based techniques suffer from high computational complexity, and generally sensitive to the colored noise. We present here a frequency-domain based subspace parameter estimation algorithm termed frequency-selective MUltiple SIgnal Classification (F-MUSIC) that is based on the signal and noise subspace orthogonality property. The method is computationally efficient in providing estimates in the selected subband compared to the classic MUSIC. The performance of F-MUSIC is evaluated and compared to both MUSIC and Cramer-Rao lower bound (CRLB). In a low signal to noise ratio (SNR) with colored noise scenarios, F-MUSIC outperforms MUSIC.

Dynamic spectrum management (DSM) has been recognized as a key technology for tackling multi-user crosstalk interference for DSL broadband access. Up to now, DSM design has mainly been focusing on maximization of data rates. However, recently, reducing the total power has become a main target, as IT power consumption has been identified as a significant contributor to global warming. In this paper we extend traditional DSM design towards a much wider energy-efficient scope and show how to tackle the corresponding optimization problems. The impact of this 'green DSL' approach is evaluated for practice with some surprisingly good numerical results. Furthermore bounds are provided on the trade-off between data rate performance and power saving.

OFDM based systems are very sensitive to radio frequency (RF) impairments such as in-phase/quadrature-phase (IQ) imbalance and carrier frequency offset (CFO). In this paper, a generally applicable joint transmitter (Tx) and receiver (Rx) RF impairment compensation scheme is proposed. It is composed of a time domain equalizer (TEQ) and two frequency domain equalizers (FEQ). The TEQ utilizes the short training symbols (STS) to estimate and compensate the frequency selective Rx IQ imbalance along with the CFO. The two FEQs then work on the specially induced phase rotated long training symbols (LTS) in order to compensate the remaining Tx or the combined Tx and Rx IQ imbalance along with the channel. A frequency-domain smoothing technique is also utilized in order to further speed up the convergence of the equalizers. The resulting cascade of equalizers provide an efficient compensation scheme in terms of both computational complexity as well as faster convergence.

Block crosstalk cancellation techniques in practical multi-user digital subscriber line (DSL) environments may involve a high computational complexity as the channel and noise statistics can vary over time. We follow an adaptive approach by designing a structurally consistent significance-test feature within the normalized least-mean-square (NLMS) adaptive crosstalk canceller, aimed to detect significant crosstalkers within a DSL binder. The proposed detection-guided NLMS adaptive partial crosstalk canceller for DSL targets the dominant crosstalkers across user lines and tones, has low run-time complexity, demonstrates significantly faster convergence, and requires smaller training sequences when compared via simulation to the equivalent standard NLMS adaptive crosstalk canceller.

In a binaural hearing aid system, output signals need to be generated for the left and the right ear. Using the binaural multichannel Wiener filter (MWF), which exploits all microphone signals from both hearing aids, a significant reduction of background noise can be achieved. However, due to power and bandwidth limitations of the binaural link, it is typically not possible to transmit all microphone signals between the hearing aids. To limit the amount of transmitted information, this paper presents reduced-bandwidth MWF-based noise reduction algorithms, where a filtered combination of the contralateral microphone signals is transmitted. A first scheme uses a signal-independent beamformer, whereas a second scheme uses the output of a monaural MWF on the contralateral microphone signals and a third scheme involves an iterative distributed MWF (DB-MWF) procedure. It is shown that in the case of a rank-1 speech correlation matrix, corresponding to a single speech source, the DB-MWF procedure converges to the binaural MWF solution. Experimental results compare the noise reduction performance of the reduced-bandwidth algorithms with respect to the benchmark binaural MWF. It is shown that the best performance of the reduced-bandwidth algorithms is obtained by the DB-MWF procedure and that the performance of the DB-MWF procedure approaches quite well the optimal performance of the binaural MWF.

In this paper, an efficient computational scheme is proposed to calculate the symbol mean and variance from bit a priori information, when a so-called multilinear mapping is employed. The multilinear mapping is exploited to reduce the number of the terms needed for the calculation of the symbol mean and variance.

This paper presents a novel Reed-Solomon codes based transmission scheme called RS-SC-CP. While RS-SC-CP is essentially a Reed-Solomon (RS) coded single carrier with cyclic prefix (SC-CP) system, a filter bank representation of the RS code is used. This filter bank representation unveils a DFT synthesis bank, just as in a traditional Orthogonal Frequency Division Multiplexing (OFDM) system (allbeit in a finite field). Therefore, RS-SC-CP is topologically equivalent with OFDM. As such, the RS-SC-CP system inherits the advantages of an SC-CP system over a traditional OFDM system like a low Peak to Average Power Ratio (PAPR). But, more importantly, it allows us to use a novel equalization technique that resembles a traditional OFDM equalizer. The equalizer of an RS-SC-CP receiver is split into two stages: the first stage encompasses a partial equalization in the complex field, which ensures that the residual channel response has integer coefficients. It is calculated using a Minimum Mean Square Error (MMSE) criterion. The residual ISI is removed by a Galois field equalizer in the second stage, posterior to the RS decoding removing the noise. Finally, the performance of the RS-SC-CP system is further evaluated by simulations showing the performance gain of the RS-SC-CP system compared to a traditional coded OFDM or single carrier with cyclic prefix (SC-CP) scheme.

In xDSL systems, crosstalk can be separated into two categories, namely in-domain crosstalk and out-of-domain crosstalk. In-domain crosstalk is also referred to as self crosstalk. Out-of-domain crosstalk is crosstalk originating from outside the multi-pair system and is also denoted as external noise (alien crosstalk, radio frequency interference,...). While self crosstalk in itself can easily be canceled by a linear detector like the ZF detector, the presence of external noise requires a more advanced processing. Coordination between transmitters and receivers enables the self crosstalk and the external noise to be mitigated using MIMO signal processing, usually by means of a whitening filter and SVD. In this paper, we investigate the problem of finding the optimal power allocation in MIMO xDSL systems in the presence of self crosstalk and external noise. Optimal Tx/Rx structures and power allocation algorithms will be devised under practical limitations from xDSL systems, namely per-modem total power constraints and/or spectral mask constraints, leading to a generalized SVD-based transmission. Simulation results are given for bonded VDSL2 systems with external noise coming from ADSL2+ or VDSL2 disturbing lines, along with a comparison between algorithms with one-sided signal coordination either only at the transmit side or the receive side.

In this paper, we propose time-varying FIR equalization techniques for multiple-input multiple-output (MIMO) transmission over doubly selective channels. The doubly selective channel is modeled using the basis expansion model (BEM), and equalized by means of time-varying FIR filters. The time-varying FIR filters are also modeled using the BEM. By doing this, the time-varying deconvolution problem is converted into a two-dimensional time-invariant deconvolution problem in the time-invariant coefficients of the channel BEM and the time- invariant coefficients of the equalizer BEM. The time-varying FIR equalizers are derived based on the matched filtering criterion, as well as the minimum mean-squared error (MMSE) and the zero-forcing (ZF) criteria. The performance of the MMSE and ZF equalizers in the context of MIMO transmission is demonstrated by numerical simulations.

Per-tone pulse shaping has been proposed as an alternative to time domain spectral shaping for discrete multi-tone (DMT) transmitters, e.g. VDSL modems. This enables the transmitter to use more tones without violating the power spectral density (PSD) mask constraint for data transmission. The computational complexity of the per-tone pulse shaping and transmit power is evenly distributed over tones, however, resources (computational complexity and power) can be better exploited by using different filter lengths for different tones and by resorting to power loading. For a fixed pulse shaping filter length, the contribution of a particular tone to the stop band energy depends on the power allocated to the tone and on the distance of the tone from the band edges. The use of high order pulse shaping filters for the tones at the band edges (as well as the use of lower power) will reduce their contribution to the out of band PSD, whereas for the tones at the middle of the band these factors will have less effect on the out of band PSD. Therefore, the combination of both power loading and a variable length pulse shaping filter can be used to achieve a high data rate under resource and PSD constraints. In this paper we present an algorithm to optimally allocate the resources i.e. power and filter taps, using a dual problem formulation. This solves the problem of optimally distributing power and filter taps over tones for a given PSD mask constraint, with a relatively low complexity.

In modern DSL networks, crosstalk among different lines (i.e., users) is the major source of performance degradation. Dynamic spectrum management (DSM) refers to a set of techniques to mitigate the effect of crosstalk leading to spectacular performance gains. However the main research efforts in DSM aim at only physical layer performance whereas the true end user experience depends on what they see at the application rather than the physical layer. Upper layer performance metrics like throughput and delay may be much more important to improve the user satisfaction. To that end, we provide a framework to study upper layer performance by looking at scheduling and DSM together. We show how optimal scheduling can be combined with optimal DSM and provide throughput-optimal scheduling algorithms which require only polynomial complexity. We furthermore present extentions that significantly improve delay performance by using the specific structure of the underlying problem.

Per-tone pulse shaping has been proposed as an alternative to time domain spectral shaping for DMT transmitters, e.g. VDSL modems. It shapes the spectrum of individual tones such that the stop band energy of each tone can be minimized. This in particular enables transmitter to use more tones without violating the PSD mask constraint for data transmission. In per-tone pulse shaping based DMT transmitters a fixed length pulse shaping filter is typically used for every tone. The tones in the middle of the pass band however, contribute less to the overall stop band energy, so that using a high order pulse shaping filter for these tones does not result in a significant reduction of the stop band energy. As a result a significant number of pulse shaping filter taps are wasted on the tones in the middle of the pass band and do not bring any performance gain. Using a variable length pulse shaping filter which is designed such that the PSD mask constraint is not violated can then significantly reduce the total number of pulse shaping filter taps without compromising performance. In this paper, a resource allocation technique is presented for variable length pulse shaping filter design using a dual problem formulation. This optimally solves the problem of pulse shaping filter tap distribution over tones for given PSD mask constraints, with a relatively low complexity.

In this paper we propose a generally applicable frequency domain equalization and radio frequency (RF) impairment compensation technique for orthogonal frequency division multiplexing (OFDM) based multi-input multi-output (MIMO) systems. RF impairments such as in-phase quadrature-phase (IQ) imbalance and carrier frequency offset (CFO) are unavoidable in low-cost analog front-end systems, but can result in a severe performance degradation. In this paper, a digital compensation scheme is developed for joint transmitter and receiver IQ imbalance along with front-end filter mismatch, CFO and frequency selective channel distortions in OFDM based MIMO systems. This scheme can also be extended for the multi-user scenario where each user signal suffers from a different frequency offset along with IQ imbalance.

Modern DSL networks suffer from crosstalk between different lines in the same cable bundle. By carefully choosing the transmit power spectra, the impact of crosstalk can be minimized leading to spectacular performance gains. This is also referred to as dynamic spectrum management (DSM). This paper presents three novel low-complexity DSM algorithms with a different level of required message-passing. This level ranges from fully autonomous and distributed to semi-centralized execution. Simulations show good performances compared to existing state-of-the-art DSM algorithms.

Sound amplification systems having a closed signal loop often suffer from acoustic feedback, which limits the achievable amount of amplification and severely affects sound quality. A promising solution to the feedback problem consists in predicting the feedback signal using an adaptive filter, however, a bias is then introduced due to signal correlation. In speech applications, a prediction-error-method- based approach to adaptive feedback cancellation has proven to be capable of providing sufficient decorrelation without sacrificing speech quality. This approach, which is based on estimating an all-pole near-end signal model, appears to be unappropriate for musical audio signals because of their large degree of tonality. We propose a novel prediction-error-method-based adaptive feedback cancellation algorithm that features a frequency-warped all-pole near-end signal model, which is better suited for tonal audio signals. Simulation results show a doubling of the convergence speed, with only a relatively small increase in computational complexity.

This paper presents two new classes of linear prediction schemes. The first one is based on the concept of creating a sparse residual rather than a minimum variance one, which will allow a more efficient quantization; we will show that this works well in presence of voiced speech, where the excitation can be represented by an impulse train, and creates a sparser residual in the case of unvoiced speech. The second class aims at finding sparse prediction coefficients; interesting results can be seen applying it to the joint estimation of long-term and short-term predictors. The proposed estimators are all solutions to convex optimization problems, which can be solved efficiently and reliably using, e.g., interior-point methods. Index Terms: linear prediction, all-pole modeling, convex optimization 1.

We present an approach for the extraction of parameters of a damped complex exponential model from a spectrogram modified by a binary mask.The parameters are estimated by a frequency domain based methods using subspace techniques, where the core algorithm is F-ESPRIT.The sub-band defined by the binary mask provides a reduced number of DFT-samples for the parameter extractions, which results in a computational efficient scheme with high parameter estimation accuracy.The proposed synthesis system has synthesis performance comparable to the so-called LSEE-MSTFT.The estimated parameters can be used in many applications such as audio/speech coding, pitch estimation and pitch scale modification.

The multi-carrier interference channel where interference is treated as additive white Gaussian noise, is a very active topic of research, particularly important in the area of Dynamic Spectrum Management (DSM) for Digital Subscriber Lines (DSL). Here, multiple users optimize their transmit power spectra so as to maximize the total weighted sum of data rates. The corresponding optimization problem is however nonconvex and thus computationally intractable, i.e. a certificate of global optimality requires exponential time complexity algorithms. This paper shows that under certain channel conditions, this nonconvex problem can be solved in polynomial time with a certificate of global optimality. The channel conditions are discussed consisting of different interference models including synchronous and asynchronous DSL transmission. Simulations demonstrate its applicability to realistic DSL scenarios.

Multi-input multi-output (MIMO) systems are often realized with low cost front-end architectures, e.g. the so called direct conversion architectures. However, such systems are very sensitive to imperfections in the analog front-end, resulting in radio frequency (RF) impairments such as in-phase/quadrature-phase (IQ) imbalance and carrier frequency offset (CFO). The RF impairments in such low cost front-end systems are unavoidable and can result in a severe performance degradation. In this paper we propose a generally applicable equalization technique for space-time block coded (STBC) MIMO orthogonal frequency division multiplexing (OFDM) communication systems. The Alamouti based STBC scheme is examined in detail. It is shown that the compensation scheme can be easily extended to other higher order STBC systems. We consider a digital compensation scheme for joint transmitter and receiver IQ imbalance along with front-end filter mismatch, CFO and frequency selective channel distortions.

This paper describes the binaural cue preservation of a noise reduction algorithm for bilateral hearing aids, namely the multichannel Wiener filter with interaural transfer function extension (MWF-ITF). An extra term is added to the cost function to preserve the binaural cues of both the speech and noise component of a signal at the cost of some noise reduction. This paper combines the theoretical analysis with objective binaural performance measures and a perceptual evaluation.

The detection of auditory steady-state responses (ASSRs) provides an objective and frequency specific technique to assess reliable hearing thresholds at audiometric frequencies. Unfortunately, the duration of ASSR measurements can be long, which is unpractical for wide scale clinical application. Therefore, we propose a multi-channel Wiener filtering (MWF) based technique with a priori knowledge through LQ factorisation as a tool to improve the ASSR detection in recorded multi-channel electroencephalogram (EEG) data obtained at intensities above hearing threshold. We conclude that this technique is able to reduce measurement duration significantly. For a multi-channel data set and implementation, near-optimal performance is obtained with five-channel recordings.

In xDSL systems, in-domain crosstalk is easily dealt with based on zero-forcing receiver or precoding while out of domain or alien crosstalk requires a more advanced processing. In vector channels, multiple input multiple output (MIMO) signal processing mitigates both types of crosstalk, usually by means of a pre-whitening filter, singular value decomposition (SVD) based transmission and waterfilling based power allocation. In this paper, we investigate the problem of power allocation in xDSL vector channels under in-domain and alien crosstalk. We propose a new power allocation algorithm to maximize the MIMO capacity under per-modem total power constraints and spectral mask constraints, leading to a generalized SVD-based transmission.

Direct conversion architectures are currently receiving a lot of interest in OFDM based wireless transmission systems. However such systems are very sensitive to in-phase/quadrature-phase (IQ) imbalances in the front-end analog processing. In this paper the joint effect of frequency selective IQ imbalances at both transmitter and receiver is studied. When the cyclic prefix is long enough to accommodate the combined channel, transmitter and receiver filter impulse, we propose a low complexity two tap equalizer with LMS based adaptation. When the cyclic prefix is not sufficiently long, this results in inter-block-interference (IBI) between the OFDM symbols. In this case we propose a frequency domain per-tone equalizer (PTEQ) initialized by RLS based adaptation. Both algorithms provide a very efficient post-FFT adaptive equalization and their performance is close to the ideal case.

In modern DSL networks, crosstalk between different DSL lines in the same cable bundle is a major source of performance degradation. By balancing the transmit power spectra, also referred to as multi-user power control, the impact of crosstalk can be minimized leading to spectacular performance gains. In this paper a novel low-complexity spectrum balancing algorithm is presented. Its performance is compared to optimal spectrum balancing for multiple-user scenarios and it is seen to yield similar results but with a huge reduction in complexity. Moreover, by the use of a spectrum management center and limited message-passing the algorithm can be executed in a distributed fashion, which is a great asset in current DSL networks.

Fixed superdirective beamformers using small-sized microphone arrays are known to be highly sensitive to errors in the assumed microphone array characteristics (gain, phase, position). This paper discusses the design of robust superdirective beamformers by taking into account the statistics of the microphone characteristics. Different design procedures are considered: applying a white noise gain constraint, trading off the mean noise and distortion energy, minimizing the mean deviation from the desired superdirective directivity pattern, and maximizing the mean or the worst case directivity factor. When computational complexity is not an issue, maximizing the mean or the worst case directivity factor is the preferred design procedure. In addition, it is shown how to determine a suitable parameter range for the other design procedures such that both a high directivity and a high level of robustness are obtained

In this paper, solutions for combined feedback and noise suppression in hearing aids are developed. The techniques presented are based on the generalized sidelobe canceller (GSC) and adaptive feedback canceller (AFC), with a prediction error method (PEM) adaptation to avoid speech distortion. Two possible cascades of GSC-based noise reduction and AFC, namely an ldquoAFC firstrdquo and a ldquoGSC first,rdquo as well as a truly integrated solution that jointly suppresses feedback and noise are discussed. The integrated solution (called PEM-GFIC) achieves optimum synergies between noise and feedback suppression at the lowest computational cost. In addition, it cancels more feedback than the generalized echo and interference canceller, a joint solution for echo and noise suppression. In the cascaded solutions, the feedback and noise suppression filters are not always optimally exploited. For high input SNRs, ldquoAFC firstrdquo scheme generally may achieve better feedback cancellation because of its larger number of degrees of freedom. However, noise reduction by the GSC-stage seriously affects the feedback cancellation performance. At low SNRs, ldquoGSC firstrdquo generally achieves more feedback cancellation than PEM-GFIC at the expense of worse noise reduction. At high hearing aid gains and/or large SNRs, the noise reduction stage however negatively affects the performance of the feedback cancellation filter, resulting in a worse feedback and noise suppression compared to PEM-GFIC.

A new procedure is presented for designing second-order parametric equalizer filters. In contrast to the traditional approach, in which the design is based on a bilinear transform of an analog filter, the presented procedure allows for designing the filter directly in the digital domain. A rather intuitive technique known as pole-zero placement, is treated here in a quantitative way. It is shown that by making some meaningful approximations, a set of relatively simple design equations can be obtained. Design examples of both notch and resonance filters are included to illustrate the performance of the proposed method and to compare with state-of-the-art solutions.

This letter presents a linear crosstalk precoder for very-high-speed digital subscriber lines (VDSL) that has a low run-time complexity. A lower bound on the data rate of the precoder is developed, and guarantees that the precoder achieves near-optimal performance in 99% of VDSL channels

This paper provides an extension of RS-OFDM for transmission over Rayleigh fading channels with additive white Gaussian noise (AWGN). RS-OFDM is a combination of Reed-Solomon (RS) codes and orthogonal frequency division multiplexing (OFDM), in which part of the RS code operates as an OFDM front-end. Apart from other advantages such as low peak to average power ratio (PAPR), this scheme benefits mainly from the fact that the overall code is an RS code, leading to optimal performance. This result was obtained previously under the constraint of impulse noise and a fixed integer valued channel. This paper shows that these constraints can be removed by splitting the equalization in two stages: the first stage encompasses a partial equalization in the complex field, which avoids noise amplification. This complex field equalizer also ensures that the residual channel response has integer coefficients. It is calculated using a minimum mean square error (MMSE) criterion. The residual ISI is removed by a Galois field equalizer in the second stage, posterior to the RS decoding removing the noise. Finally, the performance of the RS-OFDM system is further evaluated by simulations showing the performance gain of the RS-OFDM system compared to a traditional coded OFDM or single carrier with cyclic prefix (SC-CP) scheme.

Crosstalk is a major source of performance degradation in modern xDSL systems. Optimal Spectrum Balancing (OSB) is an algorithm that mitigates the effect of crosstalk by allocating optimal transmit spectra to all interfering DSL modems. Unfortunately, its complexity grows exponentially with the number of lines in the binder. For multiple user scenarios this becomes computationally intractable. This paper presents a branch and bound approach to OSB. The proposed branch and bound operations require almost no computation keeping the total computational complexity low. Simulations show enormous complexity reductions, especially for a large number of users.

Fixed superdirective beamformers using small-size microphone arrays are known to be highly sensitive to errors in the assumed microphone array characteristics. This paper discusses the design of robust superdirective beamformers by taking into account the statistics of the microphone characteristics. Different design procedures are considered: applying a white noise gain constraint, trading off the mean noise and distortion energy, and maximizing the mean or the minimum directivity factor. When computational complexity is not important, maximizing the mean or the minimum directivity factor is the preferred design procedure. In addition, it is shown how to determine a suitable parameter range for the other design procedures

This paper presents an extension of the binaural multi-channel Wiener filtering algorithm discussed in T.J. Klasen et al. (2005). The goal of this paper is to preserve both the interaural time difference (ITD) and interaural level difference (ILD) of the speech and noise components. This is done by extending the cost function to incorporate terms for the interaural transfer functions (ITF) of the speech and noise components. Using weights, the emphasis on the preservation of the ITFs can be controlled in addition to the emphasis on noise reduction. Adapting these parameters allows one to preserve the ITFs of the speech and noise component, and therefore ITD and ILD cues, while enhancing the signal-to-noise ratio

In this paper, a joint solution to the design problem of a good error-correcting code for an OFDM scheme with a low peak to average power ratio (PAPR) in the case of impulse noise is presented. The PAPR problem is tackled by decomposing the circulant channel matrix into parallel channels by using DFT matrices in a Galois field of odd characteristic, rather than in a complex field. The modulo operation inherent to this field then limits the power of the modulated signal. More importantly, it is explained how this OFDM scheme can be seamlessly merged with a Reed-Solomon (RS) code, which due to its maximal Hamming distance is the preferred code for impulse noise cancellation. The overall scheme, referred to as RS-OFDM, shows an error correcting code that is matched to the OFDM-modulator. It is shown that the optimal Hamming distance of the RS code is preserved not only by the OFDM modulator, but also by the channel

In modern DSL systems, multi-user crosstalk is a major source of performance degradation. Optimal spectrum balancing (OSB) is a centralized algorithm that optimally allocates the available transmit power over frequencies, thereby mitigating the effect of crosstalk. OSB uses Lagrange multipliers to enforce constraints that are coupled over frequencies. However, finding the optimal Lagrange multipliers can become complex when more than two users are considered. This paper presents a number of properties of the Lagrange multipliers which lead to an efficient search algorithm. Simulations show that the required number of Lagrange multiplier evaluations is independent of the number of users and much smaller compared to the number of evaluations of currently known search algorithms

Optimal spectrum balancing (OSB) is a centralized algorithm that optimally allocates transmit power over frequencies in a multi-user DSL environment where crosstalk is a major factor limiting performance. By using a dual decomposition, OSB decouples the spectrum management problem over frequencies. This results in per-tone optimization problems that are solved with an exhaustive search. This exhaustive search, however, has an exponential complexity in the number of users. For scenarios with several users this often becomes computationally intractable. In this paper, this complexity is reduced by limiting the possible power loadings on each tone to on/off loading with an adjustable on-level. This leads to a simple OSB algorithm with manageable complexity, simple flat transmit spectra and only minor performance degradation

In modern DSL systems, crosstalk is a major source of performance degradation. Crosstalk cancellation schemes have been proposed to mitigate the effect of crosstalk. However, the complexity of crosstalk cancellation grows with the square of the number of lines in the binder. Fortunately, most of the crosstalk originates from a limited number of lines on a limited number of tones. As a result, a fraction of the complexity of full crosstalk cancellation suffices to cancel most of the crosstalk. The challenge is then to determine which crosstalk to cancel on which tones, given a certain complexity constraint. This paper presents an algorithm based on a dual decomposition to optimally solve this problem. The proposed algorithm naturally incorporates rate constraints and the complexity of the algorithm compares favourably to a known resource allocation algorithm, where a multi-user extension is made to incorporate the rate constraints.

For frequency selective interference channels where interference is treated as noise, distributively attaining the boundary of the rate region is an open problem, and is particularly important for broadband DSL access. This paper develops, analyzes, and simulates a new algorithm for power allocation in frequency selective interference channels called autonomous spectrum balancing (ASB). It utilizes the concept of a "reference line", which mimics a typical victim line in the interference channel. Compared with the state-of-the-art iterative watefilling and optimum spectrum balancing methods, the ASB algorithm is completely autonomous, has linear complexity in both the number of users and tones, and gives close to near-optimal performance. Convergence of a version of ASB is proven for any number of users

Crosstalk is a major issue in modern digital subscriber line (DSL) systems such as ADSL and VDSL. Static spectrum management, which is the traditional way of ensuring spectral compatibility, employs spectral masks that can be overly conservative and lead to poor performance. This paper presents a centralized algorithm for optimal spectrum balancing in DSL. The algorithm uses the dual decomposition method to optimize spectra in an efficient and computationally tractable way. The algorithm shows significant performance gains over existing dynamics spectrum management (DSM) techniques, e.g., in one of the cases studied, the proposed centralized algorithm leads to a factor-of-four increase in data rate over the distributed DSM algorithm iterative waterfilling.

The paper presents a binaural extension of a monaural multi-channel noise reduction algorithm for hearing aids based on Wiener filtering. The algorithm provides the hearing aid user with a binaural output. In addition to significantly suppressing the noise interference, the algorithm preserves the interaural time delay (ITD) cues of the received speech, thus allowing the user to localize the speech source correctly.

The last decade shows a growing interest in soft decoding techniques, motivated by a soft decoding gain of roughly 2 dB. Most of the techniques are applied to concatenated codes, in particular to turbo codes. This is in strong contrast to many existing coding schemes where Reed-Solomon (RS) codes are common. Recently, we unveiled a filterbank structure behind the RS codes. Using this filterbank decomposition, a RS code is broken into many smaller subcodes that can consequently be used to build a soft-in soft-out (SISO) RS decoder. A limitation of this previous work is that it is only applicable to RS codes where the codeword and dataword length are not coprime. In this paper, this constraint is eliminated. A purely algebraic method is presented to construct a filterbank decomposition for any RS code, as long as a subfield exists in the Galois field in which the RS code operates. This method gives a lot of insight into the algebraic structure of RS codes and their corresponding filterbanks.

We propose an instrumental variable method for adaptive feedback cancellation (IV-AFC) in hearing aids that is based on the autoregressive modelling of the desired signal. The IV-AFC offers better feedback suppression for spectrally colored signals than the standard continuous adaptation feedback cancellers. In contrast to a previously proposed prediction error method based feedback canceller, the IV-AFC does not suffer from stability problems when the adaptive feedback canceller is highly time-varying.

Digital subscriber line (DSL) deployment is evolving to ever higher bit rates resulting in the use of broader spectra. The DSL flavor using the broadest spectrum today is known as VDSL. The reach performance of VDSL is upstream limited as the upstream bands use the highest frequencies. These high frequencies experience more channel attenuation, resulting in smaller signal-to-noise ratio (SNR) values. In this paper, a way of increasing the upstream reach performance is described. After investigating the VDSL transceiver, the reach performance of a VDSL modem is identified to be dominated by the echo power. Therefore, limiting the downstream echo yields a better upstream receive signal. This results in trading off downstream bit rate for more upstream bit rate. The way to optimally limit the echo is derived and high performance gains can be achieved. Finally, the optimal solution is approximated with a near-optimal solution with considerably less complexity. The near-optimal solution performs very well compared to the optimal solution.

Dynamic spectrum management (DSM) is an important technique for mitigating crosstalk in DSL. One of the first DSM algorithms proposed, iterative waterfilling (IW), has a low complexity and demonstrates the spectacular performance gains that are possible. Unfortunately IW tends to be highly sub-optimal in mixed CO/RT deployments and upstream VDSL. Another DSM algorithm, optimal spectrum balancing (OSB), uses a weighted rate-sum to find the theoretically optimal transmit spectra. Unfortunately its complexity scales exponentially with the number of lines in the binder N. Typical binders contain 25-100 lines, for which OSB is intractable. This paper presents a new iterative algorithm for spectrum management in DSL. The algorithm optimizes the weighted rate-sum in an iterative fashion, which leads to a quadratic, rather than exponential, complexity in N. The algorithm is tractable for large N and can be used to optimize entire binders. Simulations show that the algorithm performs very close to the theoretical optimum achieved by OSB.

The last decade shows a growing interest in soft decoding techniques, motivated by a soft decoding gain of roughly 2dB. Most of the techniques are applied to concatenated codes, with turbo codes as the most famous example. This is in strong contrast with many existing coding schemes where Reed-Solomon (RS) codes are common. Recently, we unveiled an (FIR) filterbank structure behind the RS codes. Using this filterbank decomposition, a RS code is broken into many smaller subcodes that can consequently be used to build a SISO RS decoder. The aim of this paper is twofold: in the first part, an algebraic shortcut to construct the FIR filterbank is presented. This method gives a lot of insight into the algebraic structure of RS codes and their corresponding filterbanks. In the second part, motivated by the importance of systematic RS codes, a critically subsampled IIR filterbank structure implementing a systematic RS code is examined. The SISO RS decoder based on the IIR filterbank shows a lower computational complexity and better performance compared to the FIR counterpart.

Multirate systems and filterbanks are known to be powerful tools in image and audio applications. Recently, they are also recognised to play an important role in communication systems. This paper covers the use of filterbanks in a coding context. It is shown that their inherent periodically time varying character matches remarkably well with the cyclic properties of the family of Bose-Chaudhuri-Hochquenghem (BCH) codes. In this paper, the important subclass of Reed-Solomon (RS) codes is dealt with first. This section of the paper proves that an RS code can be implemented by a critically subsampled filterbank. The redundancy is added by the subbandfilters, which are shown to be variants of non-primitive BCH codes. The second part of the paper deals with BCH codes. In this part, a BCH code is decomposed as a sum of critically subsampled filterbanks. The critical subsampling is an important aspect in a number of applications. The use of these filterbanks in a CDMA system and in a soft-in soft-out (SISO) decoding context is briefly discussed

In the last decade, there has been a growing interest in soft decoding techniques. These techniques are used in the context of concatenated codes, with Turbo codes as the main example, but are almost never applied to existing classical codes. In this paper, the family of Reed-Solomon codes is considered and the complexity problem of Soft-In Soft-Out RS decoding is tackled by breaking RS codes into several smaller subcodes. The decomposition, presented here, is based on filterbanks with one subcode in each subband. The critical subsampling is then crucial if the filterbank is going to be used in a soft decoding setup. This paper focuses on the construction of such filterbanks, starting from non-critically subsampled filterbanks and evolving to a critically subsampled filterbank.

In this letter, we prove that the output signal-to-noise ratio (SNR) after noise reduction with the speech-distortion weighted multichannel Wiener filter is always larger than or equal to the input SNR, for any filter length, for any value of the tradeoff parameter between noise reduction and speech distortion, and for all possible speech and noise correlation matrices.

Recently, a generalized singular value decomposition (GSVD)-based optimal filtering technique has been proposed for enhancing multimicrophone speech signals degraded by additive colored noise. The GSVD-based optimal filtering technique has a better noise reduction performance than standard beamforming techniques provided that the used filter length is large enough. In this paper, it is shown that the same noise reduction performance can be obtained with shorter filter lengths at a lower computational complexity by incorporating the GSVD-based optimal filtering technique in a generalized sidelobe canceller type structure, i.e., by adding an adaptive noise cancellation (ANC) postprocessing stage. Even when using short filter lengths, the total computational complexity is essentially determined by the calculation of the GSVD of a speech and a noise data matrix. It is shown that the complexity can be significantly reduced by using recursive GSVD-updating algorithms and by using subsampling. Simulations have been performed for various acoustic scenarios (different and multiple noise sources and different reverberation conditions), where both the improvement in signal-to-noise ratio and speech distortion have been analyzed. These simulations show that the GSVD-based optimal filtering technique with an ANC postprocessing stage has a better noise reduction performance than standard fixed and adaptive beamforming techniques while introducing an acceptable amount of speech distortion.

We derive a fast QRD-least-squares lattice (QRD-LSL) based unconstrained optimal filtering algorithm for multichannel acoustic noise reduction. As known from the literature, the unconstrained optimal filtering approach is an alternative to the popular GSC beamforming, which does not rely on a priori information and hence possesses improved robustness. The optimal filtering problem involved is special in that the desired response signal is not known explicitly. The derivation of the QRD-LSL algorithm is based on a significantly reorganized version of a QRD-RLS-based unconstrained optimal filtering scheme. Overall an order of magnitude complexity reduction is obtained without any performance penalty, which makes this new approach affordable for real time implementation.

For small-sized arrays such as hearing aids, noise reduction is obtained at the expense of an increased sensitivity to errors in the assumed signal model, i.e., microphone mismatch, variations in speaker and microphone positions, reverberation. However, the noise reduction algorithm should still be robust, i.e., insensitive to small signal model errors. In this paper, we evaluate the robustness of the Generalized Sidelobe Canceller (GSC) and a recently developed Multichannel Wiener Filtering (MWF) technique for hearing aid applications both analytically and experimentally. The analysis reveals that robustness of the GSC is especially crucial in complicated noise scenarios and that microphone mismatch is particularly harmful to the GSC, even when the adaptive noise canceller is adapted during noise only. Hence, a constraint on the noise sensitivity of the GSC is essential, at the expense of less noise reduction. The MWF on the other hand, is not affected by microphone mismatch and has a potential benefit over the robust GSC with noise sensitivity constraint. However, the MWF is sensitive to the estimation accuracy of the second order statistics of speech and noise so that its benefit may be lost in nonstationary noise scenarios.

We propose a time-varying (TV) finite impulse response (FIR) equalizer for doubly selective (time- and frequency-selective) channels. We use a basis expansion model (BEM) to approximate the doubly selective channel and to design the TV FIR equalizer. This allows us to turn a complicated equalization problem into an equivalent simpler equalization problem, containing only the BEM coefficients of both the doubly selective channel and the TV FIR equalizer. The minimum mean-square error (MMSE) as well as the zero-forcing (ZF) solutions are considered. Comparisons with the block linear equalizer (BLE) are made. The TV FIR equalization we propose here unifies and extends many previously proposed serial equalization approaches. In contrast to the BLE, the proposed TV FIR equalizer allows a flexible tradeoff between complexity and performance. Moreover, through computer simulations, we show that the performance of the proposed MMSE TV FIR equalizer comes close to the performance of the ZF and MMSE BLE, at a point where the design as well as the implementation complexity are much lower.

In this paper, we address the problem of identifying convolutive channels using a Gaussian maximum-likelihood (ML) approach when short training sequences (possibly shorter than the channel impulse-response length) are periodically inserted in the transmitted signal. We consider the case where the channel is quasi-static (i.e., the sampling period is several orders of magnitude smaller than the coherence time of the channel). Several training sequences can thus be used in order to produce the channel estimate. The proposed method can be classified as semiblind and exploits all channel-output samples containing contributions from the training sequences (including those containing contributions from the unknown surrounding data symbols). Experimental results show that the proposed method closely approaches the Cramer-Rao bound and outperforms existing training-based methods (which solely exploit the channel-output samples containing contributions from the training sequences only). Existing semiblind ML methods are tested as well and appear to be outperformed by the proposed method in the considered context. A major advantage of the proposed approach is its computational complexity, which is significantly lower than that of existing semiblind methods.

Nowadays, a lot of effort is spent on developing inexpensive orthogonal frequency-division multiplexing (OFDM) receivers. Especially, zero intermediate frequency (zero-IF) receivers are very appealing, because they avoid costly IF filters. However, zero-IF front-ends also introduce significant additional front-end distortion, such as IQ imbalance. Moreover, zero-IF does not solve the phase noise problem. Unfortunately, OFDM is very sensitive to the receiver nonidealities IQ imbalance and phase noise. Therefore, we developed a new estimation/compensation scheme to jointly combat the IQ imbalance and phase noise at baseband. In this letter, we describe the algorithms and present the performance results. Our compensation scheme eliminates the IQ imbalance based on one OFDM symbol and performs well in the presence of phase noise. The compensation scheme has a fast convergence and a small residual degradation: even for large IQ imbalance, the overall system performance for an OFDM-wireless local area network (WLAN) case study is within 0.6 dB of the optimal case. As such, our approach greatly relaxes the mismatch specifications and thus enables low-cost zero-IF receivers.

Crosstalk is a serious problem in next-generation DSL systems, such as VDSL. Several non-linear crosstalk cancelers and pre-compensators have been proposed to address this. Unfortunately, they all suffer from high complexity, DFE error propagation and/or require modification of CPE. We propose the use of a simple linear zero-forcing crosstalk canceler in upstream transmission and a simple linear diagonalizing crosstalk precoder in downstream transmission. Certain properties of DSL channels ensure that these simple linear designs lead to near-optimal performance. We formulate a bound on the performance of these schemes and show that in 99% of upstream DSL channels, the linear zero-forcing canceler achieves 97% of the theoretical channel capacity. Similarly, in 99% of downstream DSL channels, the linear diagonalizing precoder achieves 91% of the theoretical channel capacity.

We aim to bridge the gap between classical coding theory and soft decoding. Reed-Solomon (RS) codes are chosen as an example, and a few standard possibilities for a soft RS decoder are explored. Soon, it is noticed that the resulting (standard) algorithms are too complex and/or not very performant. However, by employing a special critically sampled filter bank representation for the RS codes, an algorithm is developed that shows a good tradeoff between computational complexity and soft decoding performance. Furthermore, it is demonstrated that this algorithm shows remarkable similarities with turbo codes, thereby preserving the perfectness of the original RS code.

We discuss time-domain equalization of OFDM over doubly-selective channels. We consider the most general case, where the channel delay spread is larger than the cyclic prefix (CP), which results in inter-block interference (IBI). IBI, in conjunction with the Doppler effect, destroys the orthogonality between subcarriers and, hence, results in intercarrier interference (ICI). The time-domain equalizer (TEQ) is assumed to be a time-varying finite impulse response (TV FIR). The purpose of the TEQ is to convert the doubly-selective channel into a purely frequency-selective channel whose delay spread fits within the CP. In other words, the purpose of the TEQ is to restore orthogonality between subcarriers in the OFDM system.

Dynamic spectrum management (DSM) is a new technique for multiuser power allocation and/or detection in digital subscriber line (DSL) networks. In DSM, transmit spectra are adapted based on the direct and crosstalk channels seen by the modems within a network. This allows binder bit rate performance maximisation. At the Alcatel Research and Innovation labs, we have recently developed a DSM testbed which allows the performance of DSM algorithms to be evaluated in practice. The paper describes the development of this DSM testbed. With this testbed we have evaluated the performance of a DSM algorithm known as iterative water-filling. It has been demonstrated that iterative water-filling can yield data-rate gains of up to 500% in the real world. Practical issues on the implementation of iterative water-filling are also discussed.

Crosstalk is the major source of performance degradation in next generation DSL systems such as VDSL. In downstream communications, transmitting modems are co-located at the central office. This allows crosstalk precompensation to be employed. In crosstalk precompensation, the transmitted signal is pre-distorted such that the pre-distortion destructively interferes with the crosstalk introduced by the channel. Existing crosstalk precompensation techniques either give poor performance or require modification of customer premises equipment (CPF). This is impractical since there are millions of legacy CPE modems already in use. We present a novel crosstalk precompensation technique, based on a diagonalization of the crosstalk channel matrix. This technique does not require modification of CPE. Furthermore, certain properties of the DSL channel ensure that this diagonalizing precompensator achieves near-optimal performance.

The last decade has shown a growing interest in soft decoding techniques. These techniques are almost never applied to existing nearly perfect codes, but instead other families of concatenated codes arise with Turbo codes as the main example. The problem is that for the application of soft decoding, the perfect codes need to be broken into several smaller component codes. In this paper, the family of Reed-Solomon codes is considered, and, using filterbanks, they are broken into several component codes (one in each subband). This paper focuses on the construction of such filterbanks, and gradually evolves towards a critically subsampled filterbank. The critical subsampling is crucial if the filterbank is going to be used in a soft decoding setup.

Recently, a generalized noise reduction scheme was proposed, called the spatially preprocessed speech distortion weighted multi-channel Wiener filter (SP-SDW-MWF). Compared to GSC with quadratic inequality constraint (QIC-GSC), the SP-SDW-MWF reduces more noise, for a given maximum speech distortion level. We develop time-domain and frequency-domain stochastic gradient implementations of the SP-SDW-MWF. Experimental results with a hearing aid show that the proposed stochastic gradient algorithm preserves the benefit of the SP-SDW-MWF over the QIC-GSC, while its computational cost is comparable to the NLMS based scaled projection algorithm (SPA) for QIC-GSC.

Zero-IF transceivers are gaining interest because of their potential to enable low-cost OFDM terminals. However, the zero-IF architecture introduces IQ imbalance which may have a huge impact on the performance. Rather than increasing component cost to decrease the IQ imbalance, an alternative is to tolerate the IQ imbalance and compensate it digitally. Current solutions require extra analog hardware at the transmitter. We analyze transmit IQ imbalance estimation and propose a low-cost, highly effective estimation scheme, which is fully digital and located at the receiver. Performance analysis shows that this scheme can provide up to 4 dB gain while meeting the IEEE 802.11a constellation accuracy specification and more if larger IQ imbalance is present in the transmitter. It therefore enables the design of low-cost, low-complexity OFDM modems.

We propose a per-tone frequency-domain equalization approach for OFDM over doubly-selective channels. We consider the most general case, where the doubly-selective channel delay spread is larger than the cyclic prefix (CP), which results into inter-block interference (IBI). IBI in conjunction with the Doppler effect destroys the orthogonality between subcarriers and hence, results into severe intercarrier interference (ICI). In this paper, we propose a novel per-tone frequency-domain equalizer (PTFEQ) that is obtained through transferring a time-varying time-domain equalizer (TV-TEQ) to the frequency-domain. The purpose of the TV-TEQ is to restore orthogonality between subcarriers and eliminate ICI. We use the mean-square error criterion to design the PTFEQ. An efficient implementation of the proposed PTFEQ is also discussed. Finally, we show some simulation results of the proposed equalization technique.

Crosstalk is a major issue in modern DSL systems such as ADSL and VDSL. Static spectrum management, the traditional way of ensuring spectral compatibility, employs spectral masks which can be overly conservative and lead to poor performance. In this paper we present a centralized algorithm for optimal spectrum management (OSM) in DSL. The algorithm uses a dual decomposition to solve the spectrum management problem in an efficient and computationally tractable way. The algorithm shows significant performance gains over existing DSM techniques, e.g. in a downstream ADSL scenario the centralized OSM algorithm can outperform a distributed DSM algorithm such as iterative waterfilling by up to 135%.

Sampling clock synchronization in discrete multitone systems, such as digital subscriber line modems can be done with a phase locked loop. This requires expensive analog hardware, such as a voltage controlled oscillator. However, it is possible to use a cheaper free-running oscillator and tackle the problem in the digital domain. Using resampling or interpolation, the timing correction becomes transparent for subsequent equalizers. This article describes a novel resampling technique using Chebyshev polynomials.

Recently, serial linear equalizers (SLEs) and serial decision feedback equalizers (SDFEs) have been proposed to mitigate the doubly-selective channel effects. To design the SLE/SDFE and to model the doubly-selective channel, a so-called finite impulse response basis expansion model (FIR-BEM) is used. Initially, the FIR-BEM coefficients of the SLE/SDFE were designed based on the exact knowledge of the FIR-BEM coefficients of the doubly-selective channel. In practice, we can use a direct SLE/SDFE design procedure, which avoids an intermediate channel estimation step. In this paper, we describe this idea for the SLE and focus on direct semi-blind design of the FIR- BEM coefficients of the SLE. Simulation results demonstrate the validity of the proposed approach.

In this paper, we study the equalization procedure in discrete multitone (DMT)-based systems, in particular in DMT-based ADSL systems. Traditionally, equalization is performed in the time domain by means of a channel shortening filter. Shifting the equalization operations to the frequency domain, as is done in per tone equalization K. Van Acker et al. (2001), increases the achieved bitrate with 5-10%. We show that the application of the turbo principle in this per tone equalization can provide significant additional gains. Optimal equalization methods for minimizing the bit error rate use maximum a posteriori (MAP) estimation algorithms, which suffer from high computational complexity for large signal constellations and long channel impulse responses. In this approach, as in M. Tuchler et al. (2002)|, the MAP equalizer is replaced by a linear equalizer, based on the minimum mean squared error (MMSE) criterion. We give a description of an efficient implementation of such an equalizer in the per tone structure. Simulations show that we obtain a bitrate increase of 14-17% compared to the original per tone equalization based receiver structure.

Asymmetric digital subscriber line (ADSL) employs the discrete multi-tone modulation (UMT) as the transmission format. To separate up- and downstream signals, the ADSL standard allows the use of echo cancellation resulting in improved bit rates, reach and/or noise margins. DMT-based modems typically use the mixed time/frequency (MTF) domain echo canceling (EC) scheme of M. Ho et al. (Jul. 1996), where the echo-filter is estimated in the frequency domain by means of the least mean square (LMS) algorithm with the transmitted echo symbols as update directions. Since not every tone of the transmitted echo signal is excited, the MTF adaptation process leads to an inferior estimate of the echo channel, unless extra power on unused echo tones is transmitted. Since transmitting extra power on such tones is often undesired, we present in this paper an alternative EC scheme, the circulant decomposition canceler (CDC), which works without extra power requirements and which exhibits comparable complexity as M. Ho et al. (Jul. 1996). In addition, we have shown how the CDC scheme can be incorporated into a receiver structure using per tone equalization and double talk cancellation to allow fast tracking and/or convergence in the presence of a far end signal.

A time-domain equalizer (TEQ) is inserted in discrete multitone (DMT) receivers to impose channel shortening. Many algorithms have been developed to initialize this TEQ, but none of them really optimizes the bitrate. We present a truly bitrate-maximizing TEQ (BM-TEQ) cost function that is based on an exact formulation of the subchannel signal-to-noise ratio as a function of the TEQ taps. The performance of this BM-TEQ comes close to the performance of the per-tone equalizer.

We propose a minimum mean-square error (MMSE) time-varying (TV) finite impulse response (FIR) decision feedback equalizer (DFE) for doubly-selective (time- and frequency-selective) channels. We use the basis expansion model (BEM) to approximate the doubly-selective channel and to design the TV FIR DFE. This allows us to turn a large design problem into an equivalent small design problem, containing only the BEM coefficients of the doubly-selective channel and the BEM coefficients of the TV FIR feedforward and feedback equalization filters. Through computer simulations we show that the performance of the proposed TV FIR DFE approaches the performance of the block DF equalizer, while the equalization and the design complexity are generally much lower.

We investigate power allocation and crosstalk cancellation for MIMO-DSL (a.k.a. bonded-DSL) systems. Exploiting a property of the DSL channel, namely column-wise diagonal dominance, allows us to simplify the power allocation and crosstalk cancellation problems. This leads to a significant reduction in initialisation and run-time complexity whilst maintaining near-optimal performance. We. develop a bound which relates the deviation of the simplified power allocation to the degree of column-wise diagonal dominance. Reliable transmission at near-capacity data-rates is also demonstrated through simulation.

The combination of space-time block coding (STBC) and direct-sequence code division multiple access (DS-CDMA) has the potential to increase the performance of multiple users in a cellular environment. However, if not carefully designed, the resulting communication scheme suffers from increased multi-user interference (MUI), which dramatically deteriorates the performance. To tackle this MUI problem in the downlink, we combine two specific CDMA and STBC techniques, namely single-carrier block-spread (SCBS) CDMA and time-reversal (TR) STBC. The resulting transceiver allows for deterministic maximum likelihood (ML) user separation through low-complexity code-matched filtering as well as deterministic transmit stream separation through linear processing. These properties guarantee maximum diversity gains of N/sub T/N/sub R/(L+1) for every user in the system, irrespective of the system load, where N/sub T/ is the number of transmit antennas, N/sub R/ the number of receive antennas and L the order of the underlying multipath channels. Moreover, it turns out that a low-complexity linear receiver based on frequency-domain equalization comes close to extracting the full diversity in reduced as well as full load settings.

In this paper, we address the general problem of identifying convolutive channels when several training sequences are inserted in the transmitted data symbols stream. We analyze the general situation where the training sequences differ from each other. We consider quasi-static channels (i.e. the sampling period is several orders of magnitude below the coherence time of the channel). There are no requirements on the length of the training sequence and all the received symbols that contain contributions from the training symbols are used for the identification. We first propose an iterative method that quickly converges to the maximum likelihood (ML) channel estimate. We also derive a simple closed form expression that approximates the ML channel estimate.

Zero-IF receivers are gaining interest because they enable low-cost WLAN OFDM terminals. However, zero-IF receivers introduce IQ imbalance which may have a huge impact on performance. Rather than increasing component cost to decrease the IQ imbalance, an alternative is to tolerate the IQ imbalance and compensate it digitally. Current solutions converge too slowly for bursty WLAN communication. Moreover, the tremendous impact of a frequency offset on the IQ estimation/compensation problem is not considered. We analyze joint IQ-CFO estimation/compensation and propose a low-cost, highly effective compensation scheme. For large IQ imbalance (/spl epsi/=10%, /spl Delta//spl phi/=10/spl deg/) and large frequency offset, our solution results in an average remaining degradation below 0.5 dB compared to the reference case without IQ imbalance or frequency offset. It therefore enables the design of low-cost, low-complexity WLAN OFDM receivers.

Crosstalk is the major source of performance degradation in VDSL. A number of crosstalk cancellation techniques have been proposed to address this. Whilst these schemes lead to large performance increases they also have high run-time complexities, a problem which grows rapidly with the number of lines within a binder. Since the majority of crosstalk typically comes from only a few dominant crosstalkers, it is possible to do partial crosstalk cancellation. We present a low-complexity, partial crosstalk cancellation technique for VDSL based on line selection. We derive the optimal line selection technique, and several low-complexity selection algorithms which give near-optimal performance in most scenarios. These techniques lead to significant reductions in runtime complexity whilst giving similar performance to full crosstalk cancellation.

Discrete multi tone (DMT) uses an inverse discrete Fourier transform (IDFT) to modulate data on the carriers. The rather high side lobes of the IDFT filter bank can lead to spurious emissions in unauthorised frequency bands. Applying a window function within the DMT symbol alleviates this, but introduces distortions that are generally not easy to compensate. In this paper a special class of window functions is derived for which this compensation only requires a modest amount of processing at the receiver.

Fixed broadband beamformers for speech applications using small-sized microphone arrays are known to be highly sensitive to errors in the microphone array characteristics. This paper describes two procedures for designing broadband beamformers with an arbitrary spatial directivity pattern, which are robust against gain and phase errors. The first design procedure optimises the mean performance of the broadband beamformer and requires knowledge of the gain and phase probability density functions, whereas the second design procedure optimises the worst-case performance by using a minimax criterion. Simulations with a small-sized microphone array show the performance improvement that can be obtained by using a robust broadband beamformer design procedure.

In this paper, we propose a time-varying (TV) finite impulse response (FIR) equalizer for doubly-selective (time- and frequency-selective) channels. We use the basis expansion model (BEM) to approximate the doubly-selective channel and to design the TV FIR equalizer. This structure allows us to turn a large design problem into an equivalent small design problem, containing only the BEM coefficients of both the doubly-selective channel and the TV FIR equalizer. Focus is on the minimum mean-square error (MMSE) solution, but the zero-forcing (ZF) solution is also discussed. Comparisons with the linear block equalizer (LBE) are made. Through computer simulations we show that the performance of the MMSE TV FIR equalizer approaches that of the MMSE LBE, while the design as well as the implementation complexity are much lower.

The noise reduction performance of the generalized sidelobe canceller (GSC) depends on the validity of a priori assumptions about the signal model, whereas the recently developed multi-channel Wiener filter (MWF) techniques do not, hence, their potential benefit. However, both techniques rely on a speech detection algorithm. In this paper, we analyze the average effect of speech detection errors on the performance of the GSC and MWF both theoretically and experimentally. It is shown that the MWF preserves its benefit over the GSC for a reasonable speech detection error rate of 20% or less, even when the GSC is supplied with a robustness constraint.

In this paper we propose a zero forcing (ZF) time-varying (TV) finite-impulse response (FIR) equalizer for doubly-selective (time- and frequency-selective) channels. We use the basis expansion model (BEM) to approximate the doubly-selective channel and to design the TV FIR equalizer. This allows us to turn a large TV problem into an equivalent small-time invariant (TIV) problem, containing only the BEM coefficients of the doubly-selective channel and the TV FIR equalizer. It is shown that a ZF TV FIR equalizer only exists if there is more than one receive antenna. The ZF TV FIR equalizer approach we propose here unifies and extends many previously proposed serial equalization approaches. Through computer simulations we show that the performance of the ZF TV FIR equalizer approaches the one of the ZF block equalizer, while the equalization as well as the design complexity is much lower.

Discrete multitone (DMT) offers an elegant way to achieve high capacity, dividing the spectrum into small bands and processing these individually. The per tone equalizer (PTEQ) optimizes the capacity for each band individually, thus optimizing the whole. However, it provides little protection against narrow band radio frequency interference (RFI), being spread over all tones because of the high side lobes of the DFT filter band used in the receiver. The use of windowing functions limits this noise spreading, but is difficult to combine with the PTEQ. This paper describes a method to combine the PTEQ with a raised cosine window, while keeping the complexity reasonable. Extensions to other windowing functions are also given.

This paper focuses on multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, where the MIMO channel order is larger than the length of the cyclic prefix (CP). By swapping the filtering operations of the MIMO channel and the fast Fourier transform (FFT), it is shown that each tone of a MIMO OFDM system can be viewed as a MIMO single-carrier (SC) system. As a result, the existing equalization approach for MIMO SC systems can be applied to each tone of MIMO OFDM systems. This so-called per-tone equalization (PTEQ) approach for MIMO OFDM systems is an attractive alternative for recently developed time-domain equalization (TEQ) approach for MIMO OFDM systems. The main difference between the PTEQ and TEQ approach is that a per-tone equalizer equalizes a single tone on the symbol level (low rate), whereas a time-domain equalizer equalizes all tones together on the sample level (high rate). Next to some other advantages, this means that a per-tone equalizer can much more easily be designed in practice than a time-domain equalizer. This is illustrated in the second part, where we adapt an existing semi-blind equalization algorithm for a generalized space-time block coded (GSTBC) MIMO SC system to a semi-blind per-tone equalization algorithm for a GSTBC MIMO OFDM system.

A time-domain equalizer (TEQ) is inserted in discrete multitone (DMT) receivers to impose channel shortening and hence overcome the need for a too long cyclic prefix. Many algorithms have been developed to initialize this TEQ, but none of them really optimizes the bitrate. In this paper, we present a new bitrate maximizing TEQ (BM-TEQ), cost function that results in a TEQ design/initialization that outperforms any other TEQ design. In the derivation, we exploit the fact that the frequency-domain equalizers (FEQ) do not alter the SNR on the individual tones. The performance of this BM-TEQ comes close to the performance of the per-tone equalizer (PTEQ), an alternative DMT equalization structure that ensures consistently better performance than a TEQ. Finally, the presented BM-TEQ design is also used in a "per group" equalization scheme (PGEQ), which is intermediate (in terms of memory requirement and performance) between TEQ and PTEQ. The PGEQ design then encompasses BM-TEQ and PTEQ design procedures as extreme cases.

In the past, several papers have reported on echo canceller (EC) structures developed for discrete multitone transmission (DMT) based asymmetric digital subscriber lines (ADSL) transmission. The most commonly known scheme, developed by Ho, Cioffi and Bingham, is based on canceling the received echo noise efficiently in times as well as in the frequency domain. The time domain processing part, which is commonly called cyclic echo synthesis (CES), can be minimized by choosing the optimal temporal alignment between the echo transmitter and the EC in the modem. This paper improves previously published work by minimizing the CES complexity without choosing a specific temporal alignment. Moreover, the obtained structure is highly suitable for making the update process of the echo canceller coefficients independent of the far end signal, leading to improved convergence behavior.

The performance of large vocabulary recognition systems, for instance in a dictation application, typically deteriorates severely when used in a reverberant environment. This can be partially avoided by adding a dereverberation algorithm as a speech signal preprocessing step. The purpose of this paper is to compare the effect of different speech dereverberation algorithms on the performance of a recognition system. Experiments were conducted on the Wall Street Journal dictation benchmark. Reverberation was added to the clean acoustic data in the benchmark both by simulation and by re-recording the data in a reverberant room. Moreover additive noise was added to investigate its effect on the dereverberation algorithms. We found that dereverberation based on a delay-and-sum beamforming algorithm has the best performance of the investigated algorithms.

The combination of space-time block coding (STBC) and direct-sequence code-division multiple access (DS-CDMA) has the potential to increase the performance of multiple users in a cellular network. However, if not carefully designed, the resulting transmission scheme suffers from increased multiuser interference (MUI), which dramatically deteriorates the performance. To tackle this MUI problem in the downlink, we combine two specific DS-CDMA and STBC techniques, namely single-carrier block transmission (SCBT) DS-CDMA and time-reversal STBC. The resulting transmission scheme allows for deterministic maximum-likelihood (ML) user separation through low-complexity code-matched filtering, as well as deterministic ML transmit stream separation through linear processing. Moreover, it can achieve maximum diversity gains of N/sub T/N/sub R/(L+1) for every user in the system, irrespective of the system load, where N/sub T/ is the number of transmit antennas, N/sub R/ the number of receive antennas, and L the order of the underlying multipath channels. In addition, it turns out that a low-complexity linear receiver based on frequency-domain equalization comes close to extracting the full diversity in reduced, as well as full load settings. In this perspective, we also develop two (recursive) least squares methods for direct equalizer design. Simulation results demonstrate the outstanding performance of the proposed transceiver compared to competing alternatives.

For high quality acoustic echo cancellation long echoes have to be suppressed. classical LMS-based adaptive filters are not attractive as they are suboptimal from a computational point of view. Multirate adaptive filters such as the partitioned block frequency-domain adaptive filter (PBFDAF) are good alternatives and are widely used in commercial echo cancellers nowadays. In this paper the PBFDRAP is analyzed, which combines frequency-domain adaptive filtering with so-called "row action projection." Fast versions of the algorithm are derived and it is shown that the PBFDRAP outperforms the PBFDAF in a realistic echo cancellation setup.

Per-tone equalization has recently been proposed as an alternative receiver structure for discrete multitone-based systems improving upon the well-known structure based on time-domain equalization. Fast initialization of all the equalizer coefficients has been identified as an open problem. In this letter, a recursive initialization scheme based on recursive least squares with inverse updating is presented for the per-tone equalizers. Simulation results show convergence with an acceptably small number of training symbols. Complexity calculations are made for per-tone equalization and for the case where tones are grouped. It is demonstrated with an example that in the latter case, initialization complexity becomes sufficiently low and comparable to complexity during data transmission.

A new echo cancellation structure for discrete multitone systems is presented, where each used tone has its own per-tone echo canceller in addition to a per-tone equalizer, which provides an alternative to currently employed time domain and time/frequency domain approaches. The per-tone approach enables us to optimize the signal-to-noise ratio for each tone separately by solving a minimum mean-square error problem for each tone, with implicit so-called joint shortening. Complexity during data transmission is compared for time domain, time/frequency domain, and per-tone echo cancellation. Structures with reduced complexity are derived for an interpolated and a decimated rate setup. Finally, simulation results for an asymmetric digital subscriber line setting demonstrate improved performance over time domain (or time/frequency domain) echo cancellation.

Single-carrier block transmission (SCBT) DS-CDMA, also known as chip-interleaved block-spread (CIBS) CDMA, is an interesting transmission technique for future broadband cellular systems because it inherits the benign properties of both SCBT and CDMA. By zero padding (ZP) each chip block, the orthogonality of the spreading codes is retained regardless of the underlying multipath channel which allows for deterministic maximum likelihood (ML) user separation employing low-complexity code-matched filtering. In this paper, we focus on downlink single-carrier block transmissions with known symbol padding (KSP) (as opposed to ZP) which pad each chip block with a postfix of known symbols that can be used for training purposes at the receiver. Specifically, we propose three methods for direct equalizer estimation that all exploit the presence of the known symbol postfix but differ in the amount of additional a-priori information they assume to determine the equalizer coefficients. Simulation results demonstrate the outstanding performance of the semi-blind joint CDMP/KSP-trained method, that additionally assumes knowledge of a code division multiplexed pilot (CDMP) and the multiuser code correlation matrix.

A novel equalization technique for Discrete multitone (DMT) based modems is proposed which incorporates receiver windowing operations. The method is especially useful in an environment dominated by crosstalk and narrowband interference, such as digital subscriber line (DSL). The high sidelobe level of the DFT filters present in classical DMT receivers leads to considerable performance degradation due to intercarrier interference and broadband susceptibility to narrowband disturbers. Window functions can be used to alleviate this, but they are difficult to combine with equalization techniques, e.g. per tone equalization [1]. The presented method offers a way to do so without unduly increasing the complexity.

In this paper, we extend space-time block coding techniques, originally proposed for point-to-point communication links, to point-to-multipoint communication links, thereby taking into account the multiple access technique in the design of the transmission scheme. In specific, we propose two per-tone linear space-time chip equalizers for a space-time coded MC-DS-CDMA downlink with linear precoding. Both the training-based and the semi-blind chip equalizer exploit the presence of a continuous code-multiplexed pilot in the transmitted signal but differ in the amount of a-priori information they assume to estimate their coefficients. With Mttransmit antennas at the base-station, Mrreceive antennas at the mobile station and L the order of the multipath channel, they come close to extracting the full diversity of order Mt· Mr· (L + 1) in reduced as well as full load settings.

In classical discrete multi tone receivers, the equalizer structure consists of a (real) time domain equalizer (TEQ) combined with (complex) 1-taps frequency domain equalizers. Recently, an alternative receiver was introduced based on a separate per tone equalization, where the SNR on each tone and hence the bitrate is maximized. In this paper, a new adaptive initialization scheme for this per tone equalizer (PTEQ) is introduced, based on a combination of Least Mean Squares (LMS) and Recursive Least Squares (RLS) with inverse updating. It will be shown that the proposed method exhibits only slightly slower convergence compared to full RLS with inverse updating, while complexity as well as memory cost are reduced considerably.

In downlink CDMA, frequency-selectivity destroys the orthogonality of the user signals and introduces multi-user interference (MUI). A space-time chip equalizer is an attractive tool to restore the orthogonality of the user signals and suppress MUI. Recently, efficient pilot-based methods have been developed to design such a space-time chip equalizer. In this paper, we show how these pilot-based methods can be generalized to space-time coded downlink CDMA. As space-time coded downlink CDMA transmission scheme, we consider the conventional single-antenna downlink CDMA transmission scheme followed by a space-time block code for single-carrier block transmissions that exploits the maximum achievable diversity in a frequency-selective fading channel. Simulation results show improved performance over a pilot-based space-time RAKE-type receiver applied to the space-time coded downlink CDMA transmission schemes that were proposed for the UMTS and IS-2000 W-CDMA standards.

In the downlink of WCDMA systems, multi-path propagation destroys the orthogonality of the user signals and causes multi-user interference (MUI). Chip-level equalization can restore the orthogonality and suppress the MUI. However, adaptive implementations of the chip-level equalizer receiver that can track time-varying multi-path channels are hard to realize in practice for two reasons: loss of spectral efficiency when using a training sequence and loss of performance when using pure blind techniques. We propose new training-based and semi-blind space-time chip-level equalizer receivers for the downlink of WCDMA systems employing long spreading codes and a continuous code-multiplexed pilot. The proposed receivers exploit the presence of common pilot symbols in the so-called Common PIlot CHannel (CPICH) of the Universal Terrestrial Radio Access (UTRA) for 3G systems. Moreover, they can simultaneously track multiple base-station signals, whenever the mobile station enters soft handover mode. For both receivers, we derive a recursive least squares (RLS) algorithm for adaptive processing. The proposed receivers are compared with the conventional space-time RAKE receiver and the ideal space-time chip-level equalizer receiver both in terms of performance and complexity.

In the downlink of traditional single input single output (SISO) DS-CDMA systems, multipath propagation destroys the orthogonality of the user signals and causes multi-user interference (MUI). Chip equalization can restore the orthogonality and suppress the MUI. In the context of multiple input multiple output (MIMO) DS-CDMA systems, spatial multiplexing emits independent data streams from the M/sub T/ transmit antennas which allows one to significantly enhance the spectral efficiency. However, combined with DS-CDMA, spatial multiplexing also leads to an M/sub T/-fold increase of the MUI. Linear chip equalization only is not able to cope with the induced MUI. In this paper, we therefore propose a new combined linear and non-linear multi-user receiver for the spatially multiplexed DS-CDMA (SM DS-CDMA) downlink. The proposed receiver consists of an initial linear chip equalization stage and possibly multiple non-linear parallel interference cancellation (PIC) stages with RAKE combining. With a single PIC/RAKE stage, it achieves a 5.1 dB gain given a BER of 10/sup -3/, compared to the linear MMSE receiver only. A second PIC/RAKE stage brings an additional 0.9 dB gain at the expense of increased complexity.

The so-called affine projection algorithm (APA) has become a popular method in adaptive filtering applications and fast versions of it have been developed previously, such as fast affine projection (FAP) and the frequency domain block exact fast affine projection (BEFAP). While it is known that regularization is an absolute necessity in affine projection based adaptive filtering algorithms, FAP and BEFAP rely on an implicit "small regularization parameter" assumption. In this paper, a block exact affine projection algorithm (BE-APA) is derived that does not rely on the assumption of a small regularization parameter. It is an exact frequency domain translation of the original AP algorithm, and still has about the same complexity as, block exact FAR Unlike BEFAP, it can be used with strong regularization. It is then extended to incorporate an alternative to explicit regularization that is based on so-called "sparse" equations.

In the downlink of WCDMA systems, multipath propagation destroys the orthogonality of the user signals and causes multi-user interference (MUI). Chip-level equalization can restore the orthogonality and suppress the MUI. However, adaptive implementations of the chip equalizer receiver that can track time-varying multipath channels are hard to realize in practice. In this paper, we propose new training-based and semi-blind space-time chip equalizer receivers for the downlink of WCDMA systems employing long spreading codes and a code-multiplexed pilot. The proposed receivers exploit the presence of common pilot symbols in the so-called Common Pilot Channel (CPICH) of the Universal Terrestrial Radio Access (UTRA) for 3G systems. Moreover, they can simultaneously track multiple base-station signals, whenever the mobile station enters soft handover mode. For both receivers, we derive a Least Squares algorithm for block processing and a Recursive Least Squares algorithm for adaptive processing. The proposed receivers are compared in terms of performance with the conventional space-time RAKE receiver and the ideal fully-trained space-time chip equalizer receiver.

Link adaptation (LA) can significantly increase the spectral efficiency and render various levels of services, both of which are highly desirable for today's high-speed communication systems. Although optimality can be ensured through careful designing for a fixed link configuration, due to changes of available power, data rate, and especially channel statistics, it is better to adapt the adaptation scheme per se in real time. We term this technique adaptive link adaptation (ALA). Based on our previous work on a general optimal link adaptation scheme for a certain configuration, we continue to investigate the problem of adapting the adaptation scheme online. This paper first recapitulates that general optimal link adaptation scheme, then gives the theoretic basis of ALA, and finally presents a simple yet working implementation of the update algorithm for ALA. Both theoretical and simulation results indicate the viability of ALA and an 18 dB gain can be achieved for the investigated simple scenario. With low complexity, optimality, and generality, the proposed ALA provides a promising technique to substantially increase the spectral efficiency of practical communication systems.

Recently, an alternative receiver structure was presented for discrete multitone (DMT)-based systems. The traditional structure consisting of a (real) time domain equalizer (TEQ) with a (complex) 1-tap frequency domain equalizer (FEQ) per tone is modified into a structure with a (complex) multitap FEQ per tone. The signal-to-noise ratio (SNR) for each individual tone is maximized, hence the term "per tone equalization". Here, we derive an equalization scheme for DMT-based systems in an alternative way. We start from the assumption that the transmission channel to equalize has an infinite impulse response (IIR) or pole-zero model. We conclude that, under certain numerator and denominator conditions, the per tone equalizer is a close approximation of the optimal minimum mean square error (MMSE) equalizer. In case the numerator order condition is not fulfilled, we propose a low-complexity generalization of the per tone equalizer. This generalization is based on a suboptimal MMSE criterion and exploits transmit redundancy introduced by means of pilot and/or unused tones. We evaluate the performance of the new, generalized per tone equalizer in an ADSL context. The principles are applicable to OFDM as well.

A new echo canceller for discrete multitone (DMT) systems is presented where each used tone has its own per tone echo canceller (PT-EC) in addition to a per tone equalizer (PT-EQ). This enables us to optimize the signal-to-noise ratio (SNR) for each tone separately by solving a minimum mean square error (MMSE) problem for each tone. Simulation results confirm improved performance over time domain echo cancellation.

In the forward link of WCDMA systems, the multipath propagation channel destroys the orthogonality of the spreading codes and therefore causes multi-user interference (MUI). We propose new training-based and semi-blind space-time chip equalizer receivers for the forward link of WCDMA systems with a continuous code-multiplexed pilot. Both least-squares (LS) algorithms for block processing and recursive least-squares (RLS) algorithms for adaptive processing are derived. The proposed receivers can track fast fading multipath channels and outperform the RAKE receiver with perfect channel knowledge.

Future broadband satellite systems, supporting user terminal mobility, will employ regenerative LEO satellites with multiple spot-beams per satellite. MC-CDMA is a promising multiple-access technique for these systems, because it exploits the advantages of both OFDM and CDMA. However, the detector design for the reverse link, taking into account both intra- and inter-beam interference, is still an open area of research. In this paper, we propose a new MC-CDMA multiuser detector, that can deal with both intra- and inter-beam interference in frequency-selective fading channels. The ideal detector consists of an initial linear MMSE stage and possibly multiple nonlinear pcPIC-MMSE stages, that act on a per-carrier basis. Furthermore, to take care of its initialisation, we devised a training-based fully adaptive detector, that does not require any a priori information and achieves the same steady-state performance as the ideal detector. The MMSE/pcPIC-MMSE detector outperforms a classical MMSE detector by 4 dB for a BER of 10/sup -2/, while still maintaining linear complexity in the number of users.

In the forward link of WCDMA systems, multipath propagation destroys the orthogonality of the user signals and causes multi-user interference (MUI). Channel equalization can restore the orthogonality and suppress the MUI. However, adaptive implementations of the chip equalizer receiver that can track time-varying multipath channels are hard to realize in practice. In this paper, we propose new training-based and semi-blind space-time chip equalizer receivers for the forward link of WCDMA systems employing long spreading codes and a time-multiplexed pilot. The proposed receivers exploit the presence of user specific pilot symbols in the so-called dedicated physical control channel (DPCCH) of the Universal Terrestrial Radio Access (UTRA). Whereas the DPCCH-trained receiver only assumes knowledge of the desired user's pilot symbols and code sequence in a training-based cost function, the enhanced DPCCH-trained receiver assumes knowledge of each active user's pilot symbols and code sequence in a semi-blind cost function. For both receivers, we derive a least squares algorithm for block processing and a recursive least squares algorithm for adaptive processing. Both receivers can track time-varying multipath channels and outperform the conventional RAKE receiver with perfect channel knowledge. The enhanced DPCCH-trained chip equalizer receiver outperforms the regular DPCCH-trained chip equalizer receiver and comes close to the performance of the ideal fully-trained chip equalizer receiver.

Link adaptation (LA) can significantly increase the spectral efficiency and meantime provide services of various qualities, both of which are highly desirable in today's high-speed communication systems. However how to perform precise switching optimally in practical systems involving non-flat fading channels and several adaptive techniques is seldom treated. We therefore investigate this problem and propose a general optimal switching scheme. This paper gives a framework of such a scheme for link adaptation and a detailed designing procedure. We present a vigorous proof of the optimality condition and show that the optimal switching boundaries have a linear relationship, which is independent of coding, modulation and channels. Also, we extend this scheme to multicarrier, MIMO and multiservice systems to ensure global optimality. With low complexity, optimality, and generality the proposed general optimal switching scheme provides a promising technique to substantially increase the spectral efficiency of practical communication systems.

An alternative receiver structure is presented for discrete multitone-based systems. The usual structure consisting of a (real) time-domain equalizer in combination with a (complex) 1-tap frequency-domain equalizer (FEQ) per tone, is modified into a structure with a (complex) multitap FEQ per tone. By solving a minimum mean-square-error problem, the signal-to-noise ratio is maximized for each individual tone. The result is a larger bit rate while complexity during data transmission is kept at the same level. Moreover, the per tone equalization is shown to have a reduced sensitivity to the synchronization delay.

This paper describes two schemes for combining acoustic echo and noise reduction using a GSVD-(generalized singular value decomposition-) based optimal filtering technique. The GSVD-based filtering technique is a signal enhancement technique which has previously been proposed for noise reduction in multi-microphone speech signals. In many speech communication applications however also a far-end echo source is present. Therefore a combined echo and noise reduction scheme is needed. The first scheme combines a standard multi-channel adaptive echo canceller with the GSVD-based noise reduction technique. The second scheme incorporates the far-end echo reference directly into the GSVD-based signal enhancement technique without cancelling the echo in every microphone signal. The two different schemes are compared with regard to performance and computational complexity.

We consider a shift-orthogonal (a shift-orthogonal set of code sequences is used) quasi-synchronous DS-CDMA system based on block spreading in the presence of frequency-selective fading. For such a system, we can develop a simple receiver that eliminates the multi-user interference (MUI) deterministically, without using any channel information, and suppresses the remaining (single-user) intersymbol interference (ISI), using a single-user channel estimation method (training-based or blind) or using a single-user direct approach (training-based or blind). The proposed transceiver is less complex than the previously developed VL-AMOUR transceiver (which has the same properties as the proposed transceiver), while their performances are comparable.

Two major technical challenges in the design of future broadband wireless networks are the impairments of the propagation channel and the need for spectral efficiency. We previously proposed a combined OFDM/SDMA approach that mitigates the channel impairments by orthogonal frequency division multiplexing (OFDM) with cyclic prefix insertion and that achieves a high spectral efficiency by space division multiple access (SDMA). However, because of the multicarrier modulation, this approach requires high-backoff power amplifiers in the analog frontend. We present a SC-FD-SDMA basestation, which avoids these expensive amplifiers by using constant-envelope single-carrier (SC) modulation and still features the advantages of frequency-domain (FD) multipath mitigation and SDMA. We pay special attention to the initialization of such basestation and its fixed point requirements, since they are critical aspects of any realistic implementation. A case-study shows how SC-FD-SDMA enables a 100 Mbps wireless LAN with a bandwidth efficiency of 8 bps/Hz and an uncoded BER of 10/sup -3/ at 13.5 dB.

The blind channel identification problem is formulated in a stochastic state space framework. Starting from a state space model we present a preprocessing step based on two orthogonal subspace projections. Using these orthogonal projections, we derive an algorithm for blind channel estimation which is insensitive to the spatial color of the noise. The performance of this new algorithm is demonstrated through simulation examples.

Deconvolution problems are encountered in signal processing applications where an unknown input signal can only be observed after propagation through one or more noise corrupted FIR channels. The first step in recovering the input usually entails an estimation of the FIR channels through training based or blind algorithms. The 'standard' procedure then uses least squares estimation to recover the input. A recursive implementation with constant computational cost is based on the Kalman filter. In this paper we focus on a total least squares based approach, which is more appropriate if errors are expected both on the output samples and the estimates of the FIR channels. We will develop a recursive total least squares algorithm (RTLS) which closely approximates the performance of the non-recursive TLS algorithm and this at a much lower computational cost.

A fully adaptive algorithm for multiuser blind channel equalization is presented. The algorithm is based on an adaptive matrix singular value decomposition (SVD) for a (virtual) channel identification type operation and the Viterbi algorithm for subsequent symbol detection. Unlike other blind multiuser detection schemes that have appeared in the literature, the present algorithm removes multiple access interference (MAI) by exploiting the finite alphabet property of the input signals together with channel coding redundancy. The latter is believed to be a crucial new ingredient for performance in the context of MAI suppression.

We consider a recursive least squares (RLS) adaptive filtering problem where the input signal can be modelled as the output of a low order autoregressive (AR) process. We show how a good estimate of the Kalman gain vector can be obtained using a small least squares lattice (LSL) filter. This estimate can then be used in the normal way to determine the optimum filter coefficients. The resulting adaptive filtering algorithm is similar in concept to the fast Newton algorithm. The main difference is the use of the LSL instead of a low order covariance domain fast RLS algorithm. The potential advantage of this new algorithm is that, unlike a covariance domain algorithm, a LSL can be implemented in a numerically stable form.

First a brief review is given of a fully pipelined algorithm for recursive least squares (RLS) estimation, based on so-called 'inverse updating'. Then a specific class of (block) RLS algorithms is considered, which embraces normalized LMS as a special case (with block size equal to one). It is shown that such algorithms may be cast in the 'inverse-updating RLS' framework. This allows one to achieve any degree of pipelining, by performing algorithmic transformations which eliminate critical feedback loops in the original algorithms.

A graphical derivation is presented for a parallel filter structure (systolic array) for updating eigenvalue and singular value decompositions. The derivation of this array is non-trivial due to the presence of feedback loops and data contra-flow in the underlying signal flow graph (SFG). This would normally prohibit pipelined processing. However, it is shown that suitable delays may be introduced to the SFG by performing simple algorithmic transformations which compensate for the interference of crossing data flows and eliminate the critical feedback loops. The pipelined array is then obtained either by 2-slowing and retiming the SFG or by means of dependence graph scheduling and assignment, and turns out to be an improved version of the array presented in Moonen et al. (1993).

We introduce a factored spherical SVD updating algorithm which can be used for subspace tracking. It is a non-iterative algorithm for approximate SVD updating. The orthogonal matrix tracking the signal subspace is parameterized as a sequence of Givens rotations. This factorization has two important advantages. On the algorithmic level it cures the error accumulation problem inherent in the algorithm. The subspace matrix is now confined to the manifold of orthogonal matrices at all time. On the architectural level the factored algorithm is more amenable to parallel even systolic-implementation. Moreover, the SFG contains only rotation nodes. Therefore, an ideal processor for a real-time parallel ASIC architecture is a CORDIC processor.

A formal proof is presented, for the first time, for a recently developed systolic array for recursive least squares estimation proposed by Moonen and McWhirter (see Electronics Letters, 1993). The derivation of this array is non-trivial due to the presence of feedback loops and data contra-flow in the underlying signal flow graph (SFG). This would normally prohibit pipelined processing. However, it is shown that suitable delays may be introduced to the SFG by performing simple algorithmic transformations which compensate for the interference of crossing data flows and eliminate the critical feedback loops. The pipelined systolic array is then obtained either by 2-slowing and retiming the SFG or by means of dependence graph scheduling and assignment.>

This paper addresses the problem of estimating the frequency content of a two-dimensional object, e.g. an image or a set of multi-sensor snapshots, stored in a matrix. The basic assumption is that the data matrix consists of a sum of 2-D complex sinusoids. Our algebraically coupled matrix pencils (ACMP) algorithm splits the 2-D problem into two related 1-D estimation problems. In each direction the frequencies are estimated using a computationally efficient ESPRIT-like subspace algorithm. A further increase in efficiency is due to the algebraic pairing of the horizontal and vertical estimates.>

We present the proof of a new systolic array for implementing a recursive least squares (RLS) algorithm based on the QR decomposition (QRD) inverse-updates method. In its basic formulation, this algorithm contains a long data feedback path which makes the construction of a systolic architecture very difficult. Here we show how to transform the algorithm so as to circumvent this problem.>

The authors focus on an orthogonal-inverse updating algorithm, and show how a systolic implementation can be derived. They avoid the critical path problem by introducing a few additional computations such that the different algorithmic steps can be executed at the same time. The overall efficiency is then roughly 67%, and the obtained throughput is independent of the problem size.>

Some geometrically inspired concepts are studied for the identification of models for multivariable linear time-invariant systems from noisy input-output observations. Starting from a fundamental highly structured input-output matrix equation, it is shown how the singular value decomposition allows the order of the observable part of the system and its state-space model matrices to be estimated. Moreover, conditions for persistency of excitation of the inputs and the behavior of the algorithm when the data are perturbed by noise can easily be studied from a geometrical point of view. The singular values allow these concepts to be quantified. An example with an industrial plant identification is presented.>