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In cell-free massive multiple-input multiple-output systems, a large number of distributed wireless access points (AP) simultaneously serve a number of user equipments (UEs). This setup has recently been introduced as a promising alternative for the current 5G cellular networks. The setup has the ability to offer a good quality of service, especially for the UEs on the cell edges, be it that there is still a need for low-complexity signal processing algorithms. In this paper, the problem of optimal power allocation combined with uplink receive combining and downlink transmit precoding is tackled by providing efficient distributed MMSE-based algorithms. The necessary fronthaul communications to estimate the combining/precoding vectors and the necessary large-scale channel statistics are reduced to a minimum and rely on in-network summation that can be accomplished whenever the APs can be arranged into a tree-topology. Non-weighted max-sum and max–min are used as utilities for the power allocation, but the algorithms are not limited to these cases. Simulations show that the proposed algorithms outperform heuristic power allocation methods, both in uplink and downlink. [ABSTRACT FROM AUTHOR]

The generalized eigenvalue decomposition (GEVD) of a pair of matrices generalizes the concept of the eigenvalue decomposition (EVD) of a single matrix. It is a widely used tool in signal processing applications, in particular in a context of spatial filtering and subspace estimation. In this paper, we describe a distributed adaptive algorithm to estimate generalized eigenvectors (GEVCs) of a pair of sensor signal covariance matrices in a fully connected wireless sensor network. The algorithm computes these GEVCs in an iterative fashion without explicitly constructing the full network-wide covariance matrices. Instead, the nodes only exchange compressed sensor signal observations, providing a significant reduction in per-node communication and computational cost compared to the scenario where all the raw sensor signal observations are collected and processed in a fusion center. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: In this paper, we consider distributed signal estimation in sensor networks where the nodes exchange compressed sensor signal observations to estimate different node-specific signals. In particular, we revisit the so-called distributed adaptive node-specific signal estimation (DANSE) algorithm, which applies to the case where the nodes share a so-called ‘common interest’, and cast it in the more general setting where the nodes have ‘different interests’. We prove existence of an equilibrium state for such a setting by using a result from fixed point theory. By establishing a link between the DANSE algorithm and game theory, we point out that any equilibrium of the DANSE algorithm is a Nash equilibrium of the corresponding game. This provides an intuitive interpretation to the resulting signal estimators. The equilibrium state existence proof also reveals a problem with discontinuities in the DANSE update function, which may result in non-convergence of the algorithm. However, since these discontinuities are identifiable, they can easily be avoided by applying a minor heuristic modification to the algorithm. We demonstrate the effectiveness of this modification by means of numerical examples. [Copyright &y& Elsevier]

In this paper, we consider the linearly constrained distributed adaptive node-specific signal estimation (LC-DANSE) algorithm, which generates a node-specific linearly constrained minimum variance (LCMV) beamformer, i.e., with node-specific linear constraints, at each node of a wireless sensor network. The algorithm significantly reduces the number of signals that are exchanged between nodes, and yet obtains the optimal LCMV beamformers as if each node has access to all the signals in the network. We consider the case where all the steering vectors are known, as well as the blind beamforming case where the steering vectors are not known. We formally prove convergence and optimality for both versions of the LC-DANSE algorithm. We also consider the case where nodes update their local beamformers simultaneously instead of sequentially, and we demonstrate by means of simulations that applying a relaxation is often required to obtain a converging algorithm in this case. We also provide simulation results that demonstrate the effectiveness of the algorithm in a realistic speech enhancement scenario. [ABSTRACT FROM PUBLISHER]

We present a distributed adaptive node-specific signal estimation (DANSE) algorithm that operates in a wireless sensor network with a tree topology. The algorithm extends the DANSE algorithm for fully connected sensor networks, as described in previous work. It is argued why a tree topology is the natural choice if the network is not fully connected. If the node-specific desired signals share a common latent signal subspace, it is shown that the distributed algorithm converges to the same linear MMSE solutions as obtained with the centralized version of the algorithm. The computational load is then shared between the different nodes in the network, and nodes exchange only linear combinations of their sensor signal observations and data received from their neighbors. Despite the low connectivity of the network and the multi-hop signal paths, the algorithm is fully scalable in terms of communication bandwidth and computational power. Two different cases are considered concerning the communication protocol between the nodes: point-to-point transmission and local broadcasting. The former assumes that there is a reserved communication link between node-pairs, whereas with the latter, nodes communicate the same data to all of their neighbors simultaneously. The convergence properties of the algorithm are demonstrated by means of numerical examples. [ABSTRACT FROM AUTHOR]

Abstract: Multi-input multi-output (MIMO) systems are often realized with low cost front-end architectures, e.g. the so-called direct conversion (or zero IF) 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). These RF impairments can result in a severe performance degradation. In this paper we propose RF impairment compensation techniques for orthogonal frequency division multiplexing (OFDM) based MIMO systems. We consider a digital compensation scheme for joint transmitter/receiver frequency selective IQ imbalance, CFO and channel distortion. We also show that in the case where there is no transmitter IQ imbalance, the receiver IQ imbalance compensation can be de-coupled from the channel equalization resulting in a compensation in two stages. The two-stage scheme results in an overall lower computational requirement. The various compensation schemes are demonstrated to provide a performance close to the ideal case without RF impairments. [Copyright &y& Elsevier]

Abstract: In this paper, we consider a noise-free blind source separation problem with independent non-negative source signals, also known as non-negative independent component analysis (NICA). We assume that the source signals are well-grounded, which means that they have a non-vanishing pdf in a positive neighborhood of zero. We propose a novel algorithm, referred to as multiplicative NICA (M-NICA), which uses multiplicative updates together with a subspace projection based correction step to reconstruct the original source signals from the observed linear mixtures, and which is based only on second order statistics. A multiplicative update has the facilitating property that it preserves non-negativity, and does not depend on a user-defined learning rate, as opposed to gradient based updates such as in the non-negative PCA (NPCA) algorithm. We provide batch mode simulations of M-NICA and compare its performance to NPCA, for different types of signals. It is observed that M-NICA generally yields a better unmixing accuracy, but converges slower than NPCA. Especially when the amount of data samples is small, M-NICA significantly outperforms NPCA. Furthermore, a sliding window implementation of both algorithms is described and simulated, where M-NICA is observed to provide the best results. [Copyright &y& Elsevier]

Abstract: Acoustic feedback occurs in many audio applications involving musical sound signals. However, research efforts in acoustic feedback control have mainly been focused on speech applications. Since sound quality is of prime importance in audio applications, a proactive approach to acoustic feedback control is preferred to avoid ringing, howling, and excessive reverberation. Adaptive feedback cancellation (AFC) using a prediction-error-method (PEM)-based approach is a promising proactive solution, but existing algorithms are again designed for speech applications only. We propose to replace the all-pole near-end speech signal model in the PEM-based approach with a cascade of two near-end signal models: a tonal components model and a noise components model. We derive the identifiability conditions for joint identification of the acoustic feedback path and the cascaded near-end signal models. Depending on the model structure that is used for the near-end tonal components, three different PEM-based AFC algorithms are considered. By applying some relevant model approximations, the computational overhead of the proposed algorithms compared to the normalized least mean squares (NLMS) algorithm can be reduced to 25% of the NLMS complexity. Simulation results for both room acoustic and hearing aid scenarios indicate a significant performance improvement in terms of the misadjustment and the maximum stable gain increase. [Copyright &y& Elsevier]

Blind identification is a crucial subtask in signal processing problems such as blind signal separation (BSS) and direction-of-arrival (DOA) estimation. This paper presents a procedure for multiple-input multiple-output instantaneous blind identification based on second-order temporal properties of the signals, such as coloredness and nonstationarity. The procedure consists of two stages. First, based on assumptions on the second-order temporal structure (SOTS) of the source and noise signals, and using subspace techniques, the problem is reformulated in a particular way such that each column of the unknown mixing matrix satisfies a system of multivariate homogeneous polynomial equations. Then, this nonlinear system of equations is solved by means of a so-called homotopy method, which provides a general tool for solving (possibly nonexact) systems of nonlinear equations by smoothly deforming the known solutions of a simple start system into the desired solutions of the target system. Our blind identification procedure allows to estimate the mixing matrix for scenarios with more sources than sensors without resorting to sparsity assumptions, something that is often believed to be impossible when using only second-order statistics. In addition, since our algorithm does not require any assumption on the mixing matrix, also mixing matrices that are rank-deficient or even have identical columns can be identified. Finally, we give examples and performance results for speech source signals. [ABSTRACT FROM AUTHOR]

Per-tone equalization (PTEQ) has been proposed as an alternative to time-domain equalization in asymmetric digital subscriber line (ADSL) modems. It optimizes the bit rate performance of the system as each tone can be equalized independently. In a per-tone equalization-based ADSL system a fixed-length frequency-domain equalizer is typically used for every tone. The ADSL channel has, however, a lower channel gain at high frequencies; therefore, using a longer equalizer for these frequencies does not result in a significant performance gain. With a fixed equalizer length, the length of the equalizer is determined by the required length at the lower frequencies; hence, a significant number of equalizer taps are wasted in the high-frequency range without any performance gain. Using a variable-length equalizer designed such that an overall performance criterion is optimized can significantly decrease the total number of equalizer taps without coripromising performance. In this correspondence, a resource allocation technique is presented for a variable-length equalizer design using a dual problem formulation. This optimally solves the problem of equalizer length distribution over tones for given resource constraints, with a relatively low complexity. [ABSTRACT FROM AUTHOR]

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 (RS) codes is considered, and the complexity problem of soft-in soft-out (SISO) RS decoding is tackled by breaking RS codes into several smaller subcodes. Finally, the decoders of these subcodes work together in a Turbo-like fashion (Gallager's algorithm) to find an approximate maximum a posteriori (MAP) solution. The decomposition that is presented here is based on critically subsampled filterbanks, with one sub-code in each subband. A critically subsampled filterbank allows us to define a number of parallel independent subcodes. Furthermore, noncritically subsampled filterbanks have a larger number of subband variables than the codeword length, causing a message passing decoder (Gallager's algorithm) to fail. This paper focuses on the construction of such filterbanks, starting from noncritically subsampled filterbanks, it gradually evolves towards a critically subsampled filterbank. [ABSTRACT FROM AUTHOR]

This paper focuses on multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with channel order larger than the cyclic prefix (CP) length. Writing the demodulating fast Fourier transform (FFT) as a sliding FFT followed by a downsampling operation, we show in this paper that by swapping the filtering operations of the MIMO channel and the sliding FFT, the data model for the temporally smoothened received signal of each individual tone of the MIMO OFDM system is very similar to the data model for the temporally smoothened received signal of a MIMO single-carrier (SC) system. As a result, to recover the data symbol vectors, the conventional equalization approach for MIMO SC systems can be applied to each individual tone of the MIMO OFDM system. This so-called per-tone equalization (PTEQ) approach for MIMO OFDM systems is an attractive alternative to the recently developed time-domain equalization (TEQ) approach for MIMO OFDM systems. In the second part of this paper, we focus on direct per-tone equalizer design and adapt an existing semi-blind equalizer design method for space-time block coding (STBC) SC systems to the corresponding semi-blind per-tone equalizer design method for STBC OFDM systems. [ABSTRACT FROM AUTHOR]

Proposes a generalized singular value decomposition (GSVD) based algorithm for enhancing multimicrophone speech signals degraded by additive colored noise. Description of the optimal filtering technique for enhancing multimicrophone noisy speech signals; Discussion of the practical computation using a GSVD; Information on symmetry properties and averaging operation.

Abstract: In modern DSL systems, crosstalk is a major source of performance degradation. Crosstalk cancellation techniques have been proposed to mitigate the effect of crosstalk. However, the run-time complexity of these crosstalk cancellation techniques grows with the square of the number of lines. Therefore one has to be selective in cancelling crosstalk to reduce complexity. Secondly, crosstalk cancellation requires signal-level coordination between transmitters or receivers, which is not always available. Because of accessibility constraints, crosstalk between certain lines cannot be cancelled and so has to be mitigated through spectrum management. After a complexity study, this paper presents a solution for the joint spectrum management and constrained partial crosstalk cancellation problem. The complexity of the partial crosstalk cancellation part of the problem is reduced based on a line selection and user independence observation. However, to fully benefit from these observations, power loading has to be applied in the spectrum management part. We therefore also consider ON/OFF power loading, which has a low complexity and shows only a minor performance degradation compared to normal power loading. The resulting algorithm will be compared to currently available algorithms for independent spectrum management and partial crosstalk cancellation. [Copyright &y& Elsevier]

This paper describes a least squares (LS) channel estimation scheme for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems based on pilot tones. We first compute the mean square error (MSE) of the LS channel estimate. We then derive optimal pilot sequences and optimal placement of the pilot tones with respect to this MSE. It is shown that the optimal pilot sequences are equipowered, equispaced, and phase shift orthogonal. To reduce the training overhead, an LS channel estimation scheme over multiple OFDM symbols is also discussed. Moreover, to enhance channel estimation, a recursive LS (RLS) algorithm is proposed, for which we derive the optimal forgetting or tracking factor. This factor is found to be a function of both the noise variance and the channel Doppler spread. Through simulations, it is shown that the optimal pilot sequences derived in this paper outperform both the orthogonal and random pilot sequences. It is also shown that a considerable gain in signal-to-noise ratio (SNR) can be obtained by using the RLS algorithm, especially in slowly time-varying channels. [ABSTRACT FROM AUTHOR]

In discrete multitone receivers, a time domain equalizer (TEQ) is used to shorten the channel impulse response, so that the equalized channel impulse response is shorter than the inserted prefix. The aim of this paper is to show that the minimum mean square error (MMSE) channel shortening problem with two different energy constraints, remarkably, lead to the same TEQ coefficients, up to a scaling factor. Moreover, implying the two energy constraints together in the MMSE optimization again yields the same result and comes down to a canonical correlation analysis between the subspace spanned by the transmitted samples and the received samples, respectively. Hence, the TEQ obtained by these three distinct MMSE cases yields the same performance in terms of bit rate. Since the resulting problem can easily be reformulated as a maximization problem, an iterative procedure based on power iterations can be devised to reduce the computational complexity. [Copyright &y& Elsevier]

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 $ \alpha $ -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 $ \alpha $ -fair DSM algorithms that are tractable for large scenarios against little performance loss. Simulations of a G.fast cable binder show that the $ \alpha $ -fair DSM algorithms achieve an efficient trade-off between fairness and performance in contrast to current UEO methods. [ABSTRACT FROM AUTHOR]

Abstract: A wireless sensor network is envisaged that performs signal estimation by means of the distributed adaptive node-specific signal estimation (DANSE) algorithm. This wireless sensor network has constraints such that only a subset of the nodes are used for the estimation of a signal. While an optimal node selection strategy is NP-hard due to its combinatorial nature, we propose a greedy procedure that can add or remove nodes in an iterative fashion until the constraints are satisfied based on their utility. With the proposed definition of utility, a centralized algorithm can efficiently compute each nodes's utility at hardly any additional computational cost. Unfortunately, in a distributed scenario this approach becomes intractable. However, by using the convergence and optimality properties of the DANSE algorithm, it is shown that for node removal, each node can efficiently compute a utility upper bound such that the MMSE increase after removal will never exceed this value. In the case of node addition, each node can determine a utility lower bound such that the MMSE decrease will always exceed this value once added. The greedy node selection procedure can then use these upper and lower bounds to facilitate distributed node selection. [Copyright &y& Elsevier]

Abstract: In this paper, we present an iterative method for estimation of pitches from signals containing multiple sources using subspace techniques. The resulting estimator is termed Iterative Harmonic MUltiple SIgnal Classification (I-HMUSIC). Different modifications of I-HMUSIC are proposed that improve upon the classical MUSIC algorithm, including a computationally efficient method for noise subspace updating I-HMUSIC and its modifications are evaluated and compared with both the Cramér–Rao lower bound (CRLB) and non-iterative HMUSIC; good statistical performances have been obtained. [Copyright &y& Elsevier]

While several proactive acoustic feedback (Larsen-effect) cancellation schemes have been presented for speech applications with short acoustic feedback paths as encountered in hearing aids, these schemes fail with the long impulse responses inherent to, for instance, public address systems. We derive a new prediction error method (PEM)-based scheme (referred to as PEM-AFROW) which identifies both the acoustic feedback path and the nonstationary speech source model. A cascade of a short- and a long-term predictor removes the coloring and periodicity in voiced speech segments, which account for the unwanted correlation between the loudspeaker signal and the speech source signal. The predictors calculate row operations which are applied to prewhiten the speech source signal, resulting in a least squares system that is solved recursively by means of normalized least mean square or recursive least squares algorithms. Simulations show that this approach is indeed superior to earlier approaches whenever long acoustic channels are dealt with. [ABSTRACT FROM AUTHOR]