nils-herrmann/clean_scirepeval huggingface dataset:
Fair resource allocation in multiuser OFDMA downlink with passive eavesdropping

In this paper, we study the problem of resource allocation for a multiuser orthogonal frequency-division multiple access (OFDMA) downlink with eavesdropping. The considered setup consists of a base station, several users and a single eavesdropper that indents to wiretap the transmitted message within each OFDMA subchannel. By taking into consideration the existence of the eavesdropper, the base station aims to assign subchannels and allocate the available power in order to optimize the max-min fairness criterion over the users' secrecy rate. The investigated problem is hard to be solved because of its combinatorial and nonlinear nature. Thus, optimality is discussed for two particular cases where the available power tends to infinity and zero, respectively. The optimal solution is obtained by formulating a mixed integer linear problem in the first case and a series of linear sum assignment problems in the second. In addition, two low-complexity solutions are presented which are based on decoupling the subchannel and the power allocation subproblems. Numerical results are provided to illustrate the performance of the presented solutions.
Performance Analysis of Cooperative Networks With Relay Selection Over Nakagami-$m$  Fading Channels

The performance of dual-hop Decode-and-Forward relaying with relay selection (RS) is analyzed over Nakagami-m fading channels. Assuming that the direct source-to-destination link is active, closed-form expressions for the moment generating and the cumulative distribution functions of a RS-based cooperation scheme that utilizes maximal-ratio diversity at the destination are derived. These expressions are used to obtain the outage probability (OP) and average symbol error probability (ASEP) of this pure RS scheme as well as of a rate-selective one that utilizes RS only when it provides higher achievable rate than that of the direct transmission. Numerically evaluated results, verified by computer simulations, show that, although, in terms of ASEP, relaying is always beneficial, in terms of OP, it should be disabled whenever the direct link is strong.
On the Multivariate Weibull Fading Model With Arbitrary Correlation Matrix

An efficient approximation for the multivariate Weibull distribution with arbitrary correlations is presented. By approximating the Gaussian correlation matrix with a Green's matrix, a useful analytical expression for the joint distribution of the fading envelopes is derived. As an application, the outage performance of multibranch receivers operating over arbitrarily correlated Weibull fading channels is studied. Numerical and computer simulation results are presented and compared to illustrate the accuracy of the proposed approximation
A Coalitional Game Theoretic Outlook on Distributed Adaptive Parameter Estimation

In this paper, the parameter estimation problem based on diffusion least-mean-squares strategies is analyzed from a coalitional game theoretical perspective. Specifically, while selfishly minimizing only their own mean-square costs, the nodes in a network form coalitions that benefit them. Due to its nature, the problem is modeled as a nontransferable game and two scenarios are studied, one where each node's payoff includes only a suitable estimation accuracy criterion and another one in which a graph-based communication cost is also considered. In the former scenario, we first analyze the nonemptiness of the core of the games corresponding to traditional diffusion strategies, and then, the analysis is extended to a recently proposed node-specific parameter estimation setting where the nodes have overlapped but different estimation interests. In the latter scenario, after formulating a coalitional graph game and providing sufficient conditions for its core nonemptiness, we propose a distributed graph formation algorithm, based on merge-and-split approach, which converges to a stable coalition structure.
A decentralized approach to generalized power system state estimation

Distributed generation and the urge for a more efficient grid operation will increase the frequency of network topology reconfigurations in tomorrow's power grids. High-throughput synchrophasor and intelligent electronic device readings provide unprecedented instrumentation capabilities for generalized state estimation (GSE), which deals with identifying the power system state jointly with its network topology. This task is critically challenged by the complexity scale of a grid interconnection, especially under the detailed GSE model. Upon modifying the original GSE cost by block-sparsity promoting regularizers, a decentralized solver with enhanced circuit breaker verification capabilities is developed. Built on the alternating direction method of multipliers, the novel method maintains compatibility with existing solvers and requires minimum information exchanges across the control centers of neighboring power grids. Numerical tests on an extended IEEE 14-bus model corroborate the effectiveness of the novel approach.
Blind image-adaptive watermarking

In this paper a new blind image-adaptive watermarking technique is proposed. The main contributions in this work are the following. First, a new spatial mask taking into account the Human Visual System (HVS) properties, is proposed. The mask is constructed based on the local variance of the cover image prediction error sequence. Second, an improved detection scheme has been developed, which is blind, in the sense that no knowledge concerning the cover image is required. The similarity measure used in the detector is the normalized correlation between the reproduced watermark and the prediction error of the watermarked and possibly attacked image (instead of the image itself). Due to the above modifications the proposed technique exhibits superior performance as compared to the conventional HVS-based blind adaptive watermarking. This performance improvement has been justified theoretically and verified through extensive simulations. In particular, the proposed technique is robust to additive white noise, JPEG and Wavelet compression, filtering etc.
Optimal training design and placement for channel estimation in cooperative networks

In this paper, optimal training design and placement are studied for channel estimation in a frequency selective, single relay, amplify and forward cooperative system. Using a two-phase transmission protocol, the power distribution between the phases, among the pilot tones as well as the positioning of pilot tones within the transmitted OFDM symbols are examined. The LMMSE and two variants of the LS estimator are analyzed and the optimal training design and placement strategies are derived. Closed-form expressions are derived for the minimum achievable MSE in each case, which are precisely verified through simulations.
Adaptive Conjugate Gradient DFEs for Wideband MIMO Systems

New adaptive equalization algorithms for wireless systems operating over wideband multiple-input multiple-output (MIMO) channels are proposed. The problem of MIMO decision feedback equalizer (DFE) design is formulated as a set of linear equations with multiple right-hand sides (RHSs) evolving in time. By applying an adaptive modified conjugate gradient algorithm, we derive an equalizer with identical convergence, improved tracking capabilities, but higher computational load as compared to the Recursive Least Squares (RLS) algorithm. To reduce its complexity, two updating strategies of the equalizer filters based on Galerkin projections are employed.
New results for the multivariate Nakagami-m fading model with arbitrary correlation matrix and applications

New results for the multichannel Nakagami-m fading model with an arbitrary correlation matrix are presented in this paper. By using an efficient tridiagonalization method based on Householder matrices, the inverse of the Gaussian correlation matrix is transformed to tridiagonal, managing to derive a closed-form union upper bound for the joint Nakagami-m probability density function and an exact analytical expression for the moment generating function of the sum of identically distributed gamma random variables. Our analysis considers an arbitrary correlation structure, which includes as special cases the exponential, constant, circular, and linear correlation ones. Based on the proposed mathematical analysis, we obtain a tight union upper bound for the outage probability of multibranch selection diversity receivers as well as exact analytical expressions for the outage and the average error probability of multibranch maximal-ratio diversity receivers. Our analysis is verified by comparing numerically evaluated with extensive computer simulation performance evaluation results, showing the usefulness of the proposed approach.
A Linear Complexity Turbo Equalizer Based on a Modified Soft Interference Canceller

Soft-input soft-output (SISO) equalizers based on linear filters have proven to be good, low complexity, alternatives to trellis-based SISO equalizers. In particular, the soft interference canceller (SIC) has recently received great interest, especially for receivers performing turbo equalization. In this paper, we modify the way in which the SIC incorporates soft information. In existing literature the input to the cancellation filter is the expectation of the symbols based solely on the a-priori probabilities coming from the decoder, whereas here we propose to use the conditional expectation of those symbols, given both the a-priori probabilities and the received sequence. This modification results in performance gains at the expense of increased computational complexity. However, by introducing an approximation to the aforementioned algorithm a linear complexity SISO equalizer can be derived. Simulation results for an 8-PSK constellation and hostile radio channels have shown the effectiveness of the proposed algorithms in mitigating the inter-symbol interference (ISI)
An Adaptive Decision Feedback Equalizer for Time-Varying Frequency Selective MIMO Channels

A new adaptive equalizer for wireless systems operating over time-varying and frequency selective multiple-input multiple-output (MIMO) channels is proposed. The equalizer consists of a number of decision feedback equalizer (DFE) stages, each one detecting a single stream. The equalizer filters, as well as the ordering by which the streams are extracted, are updated according to a LS cost function in a VBLAST-like fashion. By taking advantage of the underlying order recursive problem structure, a computationally efficient and numerically robust algorithm is developed based on the Cholesky factorization of the input data autocorrelation matrix. The convergence and tracking capabilities of the equalizer are studied through extensive computer simulations, and its BER performance is evaluated for hostile, time and frequency selective channels
Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where the nodes are interested in estimating parameters that can be of local interest, common interest to a subset of nodes and global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different, yet coupled Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.
Bandwidth efficient transmission through sparse channels using a parametric channel estimation-based DFE

A novel equalization schemes based on parametric channel estimation is presented. The proposed scheme is appropriate for channels with long and sparse impulse response such as encountered in high definition digital video transmission systems. The main trait of the channel estimation part is that instead of seeking the whole channel impulse response (CIR), only the unknown time delays and attenuation factors of the physical channel multipath components are efficiently estimated. The equalization part comprises an adaptive decision feedback equalizer (DFE), whose initialization part is properly designed for sparse channels. The information needed in the initialization part is provided by the channel estimation part. The DFE converges much faster compared to the conventional DFE and the whole scheme utilizes a very short training sequence and hence a significant saving in bandwidth is achieved.
A step-by-step quasi-Newton algorithm in the frequency domain and its application to adaptive channel equalization

In this paper, a new efficient adaptive filtering algorithm belonging to the Quasi-Newton (QN) family is proposed. In the new algorithm, 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 implemented in the frequency domain (FD) using the fast Fourier transform (FFT). Efficient recursive relations for the frequency domain quantities updated on a step-by-step basis have been derived. The proposed algorithm turns out to be particularly suitable for adaptive channel equalization in wireless burst transmission systems. Based on this approach, new adaptive linear equalization (LE) and decision feedback equalization (DFE) algorithms have been developed. These algorithms enjoy the combined advantages of QN formulation and FD implementation, exhibiting faster convergence rate than their stochastic gradient counterparts and less computational complexity, as compared with other Newton-type algorithms.
Distributed blind adaptive computation of beamforming weights for relay networks

In the present paper, we propose two novel algorithms which enable the relay cooperation for the distributed computation of the beamforming weights in a blind and adaptive manner, without the need to forward the data to a fusion center. In the first scheme, the beamforming vector is computed through minimization of the total transmit power subject to a receiver quality-of-service constraint (QoS). In the second scheme, the beamforming weights are obtained through maximization of the receiver signal-to-noise-ratio (SNR) subject to a total transmit power constraint. The proposed approaches distribute the computational overhead equally among the relay nodes and achieve close performance to the one of the optimal beamforming solutions. Note, that the aforementioned optimal solutions are derived assuming perfect channel state information at the relays' side. In order to verify the performance of the proposed approaches, indicative simulations were carried out for static and time-varying channels.
Distributed diffusion-based LMS for node-specific parameter estimation over adaptive networks

A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local or global parameters. Although all the different LMS algorithms are coupled, the diffusion-based implementation of each LMS algorithm is exclusively undertaken by the nodes of the network interested in a specific set of local or global parameters. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.
Greedy algorithms for sparse adaptive decision feedback equalization

In this paper we propose two new adaptive decision feedback equalization (DFE) schemes for channels with long and sparse impulse responses. It has been shown that for a class of channels, and under reasonable assumptions concerning the DFE filter sizes, the feedforward (FF) and feedback (FB) filters possess also a sparse form. The sparsity form of both the channel impulse response (CIR) and the equalizer filters is properly exploited and two novel adaptive greedy schemes are derived. The first scheme is a channel estimation based one. In this scheme, the non-negligible taps of the involved CIR are first estimated via a new greedy algorithm, and then the FF and FB filters are adaptively computed by exploiting a useful relation between these filters and the CIR. The channel estimation part of this new technique is based on the steepest descent (SD) method and offers considerably improved performance as compared to other adaptive greedy algorithms that have been proposed. The second scheme is a direct adaptive sparse equalizer based on a SD-based greedy algorithm. Compared to non sparsity aware DFE, both of our schemes exhibit faster convergence, improved tracking capabilities and reduced complexity.
Linear least squares based acoustic source localization utilizing energy measurements

Localization of an isotropic acoustic source using energy measurements from distributed sensors is considered. While most acoustic source localization algorithms require that distance estimates between the sensors and the source of interest are available, we propose a linear least squares criterion that does not make such an assumption. The new criterion can yield the location of the source and its transmit power in closed form. A weighted least squares cost function is also considered, and distributed implementation of the proposed estimators is studied. Numerical results indicate significant performance improvement as compared to a linear least squares based approach that utilizes energy ratios, and comparable performance to other estimators of higher computational complexity.
A Cooperative Uplink Transmission Technique for the Single- and Multi-User Case

A new technique for the uplink transmission of a cooperative system consisting of single-antenna source and relay nodes and a multi-antenna destination node is presented. The proposed technique is based on the singular value decomposition of the channel matrix and, for the same rate, it exhibits higher diversity gain as compared to the existing ones. Theoretical analysis of the technique is carried out when the Decode-and-Forward protocol is employed. The analysis reveals the way the decoding errors occurring at the relays degrade the performance of the system. Furthermore, the proposed technique is extended to a multi-user environment so as to exploit multi-user diversity. A novel criterion is suggested for selecting the best transmitting user at each time slot. The performance of the system in the multi-user case is theoretically studied and the number of users needed so as the system to achieve its maximum performance is computed. The derived theoretical results are verified via typical simulations.
Cognition-based dynamic spectrum access and interference coordination for heterogeneous networks

We define and propose a cognition-based radio architecture for multi-tiered spectrum access in next-generation wireless heterogeneous networks (HetNets). In this architecture, femtocell nodes are embedded in a macrocell environment to increase spectrum utilization. In order to realize the potential benefits of HetNets and achieve a frequency reuse factor of 1, we investigate the radio resource allocation strategy and efficient inter-cell interference coordination. A novel scheme based on full system cognition and base station cooperation is proposed. We claim that the proposed scheme can be an enabler for high capacity HetNets and that a careful design of the cognitive radio architecture can lead to improved spectral efficiency for the overall system.
Distributed dictionary learning via projections onto convex sets

We study a problem in which the nodes of a network, each with different data, are interested in computing a common dictionary that is suitable for the efficient sparse coding of all their data. To this end, distributed processing is employed, that is, the nodes merge local and neighboring information. We formulate this as a convex feasibility problem, and propose a suitable distributed algorithm for obtaining a solution that employs projections onto convex sets. A fast method for computing the involved projection operations is also given. The proposed approach allows the associated convex sets to be updated at every iteration of the algorithm, thus resulting into a faster agreement of the nodes in a common dictionary. Simulation results are provided that demonstrate the effectiveness of the proposed scheme in computing a common dictionary, in a scenario where the data of the nodes are significantly different and a second scenario, in which the nodes have the same data.
Stochastic Gradient Pursuit for Adaptive Equalization of Sparse Multipath Channels

In this paper, a new heuristic algorithm for the sparse adaptive equalization problem, termed as stochastic gradient pursuit, is proposed. A decision-feedback equalization structure is used in order to effectively mitigate the effect of long multipath channels. Diverging from the commonly used approach of sparse channel identification, we exploit the sparsity of the inverse problem under the compressive sensing perspective. Also, an extension to the case where the sparsity order parameter is unknown, is developed. Simulation results verify that the proposed schemes exhibit faster convergence and improved tracking capabilities compared to conventional and other sparse aware equalization schemes, offering at the same time a reduced computational complexity.
Frequency Domain Channel Estimation for Cooperative Communication Networks

In this correspondence, we deal with the problem of channel estimation in amplify-and-forward (AF) wideband cooperative relay-based networks. Two types of frequency domain channel estimation techniques are proposed and analyzed. First, a training based technique is presented for which an optimal pilot placement and power allocation strategy is described. Second, hybrid techniques are introduced in which both training as well as channel output correlation information is utilized for channel estimation. A theoretical performance study of the proposed algorithms is presented and closed-form expressions for the mean squared channel estimation error are provided. The presented theoretical analysis is verified via extensive Monte Carlo simulations.
Efficient graph-based matrix completion on incomplete animated models

Recently, there has been increasing interest for easy and reliable generation of 3D animated models facilitating several real-time applications. In most of these applications, the reconstruction of soft body animations is based on time-varying point clouds which are irregularly sampled and highly incomplete. To overcome these imperfections, we introduce a novel reconstruction technique, using graph-based matrix completion approaches. The presented method exploits spatio-temporal coherences by implicitly forcing the proximity of the adjacent 3D points in time and space. The proposed constraints are modeled by using the weighted Laplacian graphs and are constructed from the available points. Extensive evaluation studies, carried out using a collection of different highly-incomplete dynamic models, verify that the proposed technique achieves plausible reconstruction output despite the constraints posed by arbitrarily complex and motion scenarios.