nils-herrmann/clean_scirepeval huggingface dataset:
Galerkin projections-based ICI cancellation in OFDM systems with doubly selective channels

Doubly selective channels can cause severe performance degradation in orthogonal frequency division multiplexing (OFDM) systems, introducing inter-carrier interference (ICI) at the receiver. In such cases, equalization schemes which require matrix inversion are prohibitively complex for large OFDM symbol lengths. In this paper, we propose two low-complexity iterative successive interference cancellation schemes, applying Krylov subspace optimization methods.We first derive a reduce-drank preconditioned conjugate gradient (PCG) algorithm in order to estimate the equalization matrix with a reduced number of iterations. We then develop an improved PCG algorithm with the same complexity order, using the Galerkin projections theory. As verified via simulations, the proposed schemes may offer near optimal performance with reduced computational complexity.
A Cooperative Uplink Transmission Technique With Improved Diversity–Multiplexing Tradeoff

In this paper, a new decode-and-forward (DF) cooperative scheme is presented for half-duplex uplink transmission. The technique is based on a virtual multiple-input-multiple-output (MIMO) structure formed by the single-antenna source and relay nodes along with a multiantenna destination node. The new technique aims at providing increased diversity and multiplexing gains, contrary to existing approaches in which increased diversity gain is achieved at the cost of severe multiplexing gain loss. Furthermore, the proposed scheme is extended in a doubly opportunistic manner by incorporating both multiuser diversity and relay selection diversity. Novel criteria are suggested for selecting the best transmitting user and/or the best relays at each time slot. The theoretical outage probability and the corresponding diversity-multiplexing tradeoff curve of both the proposed technique and its opportunistic extension are derived. Indicative simulations verify the theoretical results and compare the performance of the proposed technique with the performance of existing cooperative schemes and of the noncooperative single-input-multiple-output (SIMO) system.
Efficient decision feedback equalization for sparse wireless channels

A new efficient decision feedback equalizer (DFE) appropriate for channels with long and sparse impulse response (IR) is proposed. Such channels are encountered in many high-speed wireless communications applications. It is shown that, in cases of sparse channels, the feedforward and feedback (FB) filters of the DFE have a particular structure, which can be exploited to derive efficient implementations of the DFE, provided that the time delays of the channel IR multipath components are known. This latter task is accomplished by a novel technique, which estimates the time delays based on the form of the channel input-output cross-correlation sequence in the frequency domain. A distinct feature of the resulting DFE is that the involved FB filter consists of a reduced number of active taps. As a result, it exhibits considerable computational savings, faster convergence, and improved tracking capabilities as compared with the conventional DFE. Note that faster convergence implies that a shorter training sequence is required. Moreover, the new algorithm has a simple form and its steady-state performance is almost identical to that of the conventional DFE.
A normalized lattice algorithm for AR spectral analysis and system identification filters with symmetric impulse response

A time-recursive normalized lattice algorithm is derived for the least-squares (LS) estimation of the unknown parameters of an FIR (finite impulse response) filter with symmetric impulse response. A byproduct is an algorithm for the LS estimation of the unit step backward predictor of the input time series, based on the simultaneous minimization of the forward and backward total squared error power, which has successfully been used in the maximum-entropy spectral analysis (MESA) method.
Blind distributed beamforming via matrix completion

In this paper, we consider the problem of distributed beamforming for maximization of the receiver signal-to-noise-ratio (SNR) subject to a total transmit power constraint. We investigate the case where the optimal beamforming weights are expressed based on the second-order statistics of the involved channels, while the communication among the relays is interference-limited. In this context, we propose a relay-cooperative scheme for interference minimization, where only a limited number of correlation quantities are sent to the fusion center (FC). We propose a technique which overcomes the problem of the incomplete covariance matrices via matrix completion. Through simulation results, we show that, after a number of iterations, the proposed technique converges to the true covariance matrices and thus the optimal beamformer may be computed.
New Levinson, Schur, and lattice type algorithms for linear phase filtering

The LS estimate of the impulse response of FIR filters with linear phase is known to be given by a structured linear set of equations. Three new algorithms are presented for the efficient solution of such systems. One is the unwindowed extension of a previously derived Levinson-type structurally symmetric algorithm. The second is a novel exact least squares lattice algorithm for time-recursive processing. The third is a Schur-type structurally symmetric algorithm with high degree of parallelism. The new structures have considerably lower computational complexity and more parsimonious parametrization compared to previously derived algorithms. This is because, in contrast to the previously derived algorithms, the new algorithms are developed so as to respect the symmetry which is intrinsic in the linear phase problem.
Distributed incremental-based LMS for node-specific parameter estimation over adaptive networks

We introduce an adaptive distributed technique that is suitable for node-specific parameter estimation in an adaptive network where each node is interested in a set of parameters of local interest as well as a set of network global parameters. The estimation of each set of parameters of local interest is undertaken by a local Least Mean Squares (LMS) algorithm at each node. At the same time and coupled with the previous local estimation processes, an incremental mode of cooperation is implemented at all nodes in order to perform an LMS algorithm which estimates the parameters of global interest. In the steady state, the new distributed technique converges to the MMSE solution of a centralized processor that is able to process all the observations. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.
Efficient spatial image watermarking via new perceptual masking and blind detection schemes

The aim of this paper is to improve the performance of spatial domain watermarking. To this end, a new perceptual mask and a new detection scheme are proposed. The proposed spatial perceptual mask is based on the cover image prediction error sequence and matches very well with the properties of the human visual system. It exhibits superior performance compared to existing spatial masking schemes. Moreover, it allows for a significantly increased strength of the watermark while, at the same time, the watermark visibility is decreased. The new blind detection scheme comprises an efficient prewhitening process and a correlation-based detector. The prewhitening process is based on the least-squares prediction error filter and substantially improves the detector's performance. The correlation-based detector that was selected is shown to be the most suitable for the problem at hand. The improved performance of the proposed detection scheme has been justified theoretically for the case of linear filtering plus noise attack and through extensive simulations. The theoretical analysis is independent of the proposed mask and the derived expressions can be used for any watermarking technique based on spatial masking. It is shown though that in most cases the detector performs better if the proposed mask is employed.
Multi-antenna cooperative systems with improved diversity multiplexing tradeoff

While Half-Duplex cooperative systems provide an alternative means to achieve diversity gain, they suffer from multiplexing gain loss due to the necessary two-phase transmission protocols that are employed. In this paper two new transmission techniques are presented for systems with multi-antenna relay nodes employing the Decode-and-Forward (DF) protocol which achieve improved multiplexing and diversity gains. The first technique extends to the multi-antenna relays case our previous work for single-antenna relay nodes [5]-[6] and is based on the channel's matrix singular value decomposition (SVD). The second technique exploits the multiple-antenna relays so as to achieve Full-Duplex communication by permitting the latter ones to transmit and receive simultaneously. The transmissions' orthogonality is achieved via a novel distributed interference nulling mechanism. The proposed schemes' performance is theoretically studied and the results are verified through typical simulations.
Sparse subspace tracking techniques for adaptive blind channel identification in OFDM systems

In this paper novel subspace-based blind schemes are proposed and applied to the sparse channel identification problem. Moreover, adaptive sparse subspace tracking methods are proposed so as to provide efficient real-time implementations. The new algorithms exploit the subspace sparsity either via employing ℓ1-norm relaxation or through greedy-based optimization. The derived schemes have been tested in a Zero-Prefix Orthogonal Frequency Division Multiplexing (ZP-OFDM) system and it turns out that, compared to state-of-art existing schemes, they offer improved performance in terms of convergence rate and steady-state error.
Blind opportunistic interference alignment in cognitive radio systems

In this paper a new Opportunistic Interference Alignment (OIA) technique is presented that can be applied in a blind manner to Cognitive Radio systems. The proposed technique aims at relaxing the strong assumptions made in existing approaches where it is assumed that the Secondary User (SU) has knowledge of Channel State Information (CSI) concerning the Primary User's (PU) transmissions, although this may not be possible, in general. First, the OIA technique of  is extended so that the required CSI can be blindly estimated from the second order statistics of the received symbols. More specifically, the Secondary Receiver post-coding matrix is re-designed in order to null-out completely the interference generated by the PU's transmissions, in contrast to the original work where an interference reduction approach was adopted. Then, a novel estimation mechanism is proposed that enables the SU to apply the new OIA technique in a blind manner. The performance of the proposed technique is evaluated both theoretically as well as via simulations, under perfect and imperfect CSI.
Regularized MMSE ICI equalization for OFDM systems over doubly selective channels

In this work, we consider a wireless OFDM system operating over doubly selective channels, where the Doppler effect destroys the orthogonality between subcarriers and hence, results into severe intercarrier interference (ICI). To mitigate this effect, computational demanding equalization schemes that require the inversion of the channel matrix, should be applied. In order to achieve linear complexity in the number of the subcarriers, a banded approximation of the channel matrix is usually adopted, whereas the performance of the equalizer is significantly degraded. To recover this performance loss, we propose a regularized estimation framework for MMSE ICI equalization in the frequency domain, where the complexity remains linear with respect to the number of the subcarriers. Simulation results verify the effectiveness of the proposed regularization.
Sorted Order-$K$  Voronoi Diagrams for Model-Independent Source Localization in Wireless Sensor Networks

Localization of an isotropic source using energy measurements from randomly deployed sensors is considered. In particular, an optimization problem that does not require knowledge of the underlaying energy decay model is proposed, and a condition under which the optimal solution can be computed is given. This condition employs a new geometric construct introduced here, called the sorted order-K Voronoi diagram. We give centralized and distributed algorithms for source localization in this setting. Finally, analytical results and simulations are used to verify the performance of the developed algorithms.
Adaptive Eigenvalue-Based Spectrum Sensing for multi-antenna cognitive radio systems

In this paper adaptive Eigenvalue-Based Spectrum Sensing (EBSS) techniques are proposed for multi-antenna cognitive receivers. In cases where fading channels are involved, and hence real-time processing is required, the adaptive techniques offer lower complexity and exhibit improved performance as compared to the corresponding batch EBSS techniques. At first, novel adaptive EBSS techniques are developed for the Maximum Eigenvalue Detector (MED), the Maximum-Minimum Eigenvalue Detector (MMED), and the Generalized Likelihood Ratio Test (GLRT) schemes, respectively, based on well-studied subspace tracking methods. Moreover, close approximations for the distribution functions of the adaptive test statistics of the MED, MMED and GLRT schemes are derived in order to compute the decision thresholds for a given probability of false alarm. The performance of the adaptive EBSS methods is verified via indicative simulations.
Distributed Blind Hyperspectral Unmixing via Joint Sparsity and Low-Rank Constrained Non-Negative Matrix Factorization

Hyperspectral unmixing is a crucial processing step in remote sensing image analysis. Its aim is the decomposition of each pixel in a hyperspectral image into a number of materials, the so-called endmembers, and their corresponding abundance fractions. Among the various unmixing approaches that have been suggested in the literature, we are interested here in unsupervised techniques that rely on some form of non-negative Matrix factorization (NMF). NMF-based techniques provide an easy way to simultaneously estimate the endmembers and their corresponding abundances, though they suffer from mediocre performance and high computational complexity due to the nonconvexity of the involved cost function. Improvements in performance have been recently achieved by imposing additional constraints to the NMF optimization problem related to the sparsity of the abundances. Another feature of hyperspectral images that can be exploited is their high spatial correlation, which is translated into the low rank of the involved abundance matrices. Motivated by this, in this paper we propose a novel unmixing method that is based on a simultaneously sparse and low-rank constrained NMF. In addition, prompted by the rapid evolution of multicore processors and graphics processing units, we devise a distributed unmixing scheme that processes in parallel different parts of the image. The proposed distributed unmixing algorithm achieves improved performance and faster convergence than existing state-of-the-art techniques as it is verified by extensive simulations on synthetic and real hyperspectral data.
Blind Opportunistic Interference Alignment in MIMO Cognitive Radio Systems

Opportunistic interference alignment (OIA) is a technique that enables the multiple-input multiple-output (MIMO) secondary user (SU) to exploit the inherent spatial spectrum holes of the MIMO primary user's (PU) communication link while the PU transmissions remain interference free, thus generalizing the concept of interweave cognitive radio (CR) systems. A typical characteristic of an interweave technique is that, in general, no interaction between the PU and the SU systems is assumed, thus rendering infeasible, in many cases, the channel state information (CSI) requirements of the existing OIA techniques. The goal of the present paper is to propose a MIMO SU transceiver system that applies an OIA technique in a manner completely oblivious to the PU system so that both the alignment technique and the corresponding CSI estimation scheme require no interaction with the PU system. To this end, at first, a new improved OIA technique is proposed. The resulting technique requires CSI that can be estimated blindly at the SU ends and achieves better performance in several cases as it aligns the PU and SU signals to orthogonal sub-spaces at both the PU and the SU receivers. Then, a novel blind CSI estimation scheme is developed for the new OIA technique. The exact theoretical ergodic capacity of the new OIA technique is derived for the perfect CSI case. Some insightful theoretical results are also derived for the imperfect CSI case which, to the best of our knowledge, are some of the first ones in the OIA/IA literature. The performance of the proposed schemes is verified via extensive simulations.
A Block Adaptive Frequency Domain MIMO DFE for Wideband Channels

The equalization of MIMO frequency selective channels is a challenging task due to the increased filter lengths required and the presence of interstream interference. In wideband systems where high delay spread channels arise and long transmission bursts are employed, adaptive equalization is necessary. In this work, we develop an adaptive MIMO DFE for wideband time varying systems. To reduce computational complexity and accelerate convergence rate, the equalizer is fully implemented in the frequency domain, while an inherent causality problem is efficiently solved through an iterative scheme. As verified by simulations, the proposed method offers a good trade off in terms of complexity and convergence, compared to other related adaptive algorithms.
Semi-blind estimation of multipath channel parameters via a separable least squares approach

A novel semi-blind technique is proposed for estimating the time delays and attenuation factors of a multipath channel. The technique is based on a suitable application of the subchannel response matching criterion. The resulting cost function is separable with respect to the two sets of unknown parameters, i.e. time delays and attenuation factors. Thus an efficient two step optimization procedure can be applied. The new method offers computational savings and a lower mean square estimation error as compared to existing semi-blind channel estimation methods. The performance of the new method has been justified theoretically and tested through extensive simulations.
On the Sum of Squared Correlated Rayleigh Variates and Applications to Maximal-Ratio Diversity

An infinite series representation for the moment generating function of the sum of squared arbitrarily correlated Rayleigh random variables is presented. Based on the derived formula, corresponding analytical expressions for the probability density and cumulative distribution functions are extracted. As an application for the aforementioned sum, exact analytical expressions for the outage and the average error probability, as well as, the channel average spectral efficiency of multibranch maximal-ratio diversity receivers operating over identically distributed and arbitrarily correlated Rayleigh fading channels are obtained. Our analysis is verified by comparing numerically evaluated results with extensive computer simulation ones.
Cooperative transmission of measurements in WSN for monitoring applications

Wireless Sensor Networks (WSNs) have recently received great attention from the scientific community, because they hold the key to revolutionize many aspects of the industry and our life. The process of collecting the measurements, acquired by a sensor network into a central sink node, constitutes one of the main challenges in this area of research and is often referred to as the sensor reachback problem. In this work, we extend a recently proposed power and rate allocation algorithm so as to be able to take into account possible cooperation between the nodes in the WSN. The derived power and rate allocation algorithm considers Distributed Source Coding (DSC), in order to reduce the amount of information that must be transmitted to the sink. Under the assumption that there are several very bad channels between nodes and the sink, our method achieves both a lower peak power threshold, as well as reduced total power consumption.
An efficient block Newton-type algorithm

The algorithm presented in the paper is an exact block processing counterpart of the fast Newton transversal filtering (FNTF) algorithm [Moustakides and Theodorides, 1991]. The main trait of the new algorithm is that the block processing is done in such a way so that the resulting estimates are mathematically equivalent with the respective estimates of the FNTF algorithm. In cases where the involved filter is of medium to long order the new algorithm offers a substantial saving in computational complexity without sacrificing performance.
Transform domain quasi-Newton algorithms for adaptive equalization in burst transmission systems

Two new adaptive equalizers are proposed which belong to the quasi-Newton (QN) algorithmic family. The first algorithm. is a linear equalizer (LE) and the second one is a decision feedback equalizer (DFE). In the LE case, the involved inverse Hessian matrix is approximated by a proper expansion consisting of powers of a Toeplitz matrix. Due to this formulation, the algorithm can be efficiently implemented in the transform domain (TD) using FFT. The same idea is applied to the feedforward part of the DFE. The derived algorithms enjoy the advantages of QN algorithms, that is, they exhibit faster convergence than their stochastic gradient counterparts and less computational complexity as compared to other Newton-type algorithms. These advantages are further enhanced due to TD implementation.
Full-Duplex Relaying over Block Fading Channel: A Diversity Perspective

In this paper, we study full-duplex (FD) operation from a diversity perspective and investigate several protocols that extract diversity gains over a block fading channel. The investigated approach introduces a block-by-block transmission and requires data codewords that span several independent realizations of channel fading. This fundamental approach ensures a diversity gain at least equal to one (i.e., it does not suffer from error floors) and is employed to different relaying strategies without loop interference cancellation (LIC) and with imperfect LIC: a) Amplify-and-Forward (AF) without LIC, b) AF with imperfect LIC and c) Decode-and-Forward with imperfect LIC. The proposed protocols are analyzed from a diversity-multiplexing tradeoff (DMT) standpoint and practical universal codes that achieve the DMT of the proposed schemes are also presented. We show that AF without LIC ensures a full time diversity independently on the statistics of the loop interference, and it is introduced as a general FD-based relaying scheme. In addition, we demonstrate that imperfect LIC is not efficient for cases with strong residual loop interference and limits the diversity gain to one.
MISO estimation of asynchronously mixed BPSK sources

In this paper, a method for discrete sources extraction from underdetermined, finite-bandwidth and delayed mixtures of BPSK sources with a single antenna receiver is proposed. Unlike most of the already existing algorithms which consider the unavoidable delay between the sources as undesirable, the proposed method takes advantage of such a delay. Indeed, it turns out that it is possible to recover the symbols even if there is neither gain nor phase diversity. The complexity of the proposed algorithm is quite low, which makes it efficient for real-time sources separation. The effectiveness of the method is illustrated via numerical simulations for different scenarios.