nils-herrmann/clean_scirepeval huggingface dataset:
Block subspace updating algorithms for tracking directions of coherent signals in SDMA mobile systems

In this paper we address the problem of estimating and tracking the directions-of-arrival (DOA) of multiple and possibly coherent signals impinging on the antenna array of a base station. The problems of coherency and time variation of DOAs have been treated independently in literature, and successful methods have been proposed for each case separately. In this paper a new approach is proposed tackling both problems simultaneously. To this end it is shown that a family of subspace tracking techniques can be properly extended by incorporating the idea of spatial smoothing. The resulting algorithms track efficiently the signal subspace and provide accurate estimates of the new DOAs each time a new burst of data is received.
Efficient least squares adaptive algorithms for FIR transversal filtering

A unified view of algorithms for adaptive transversal FIR filtering and system identification has been presented. Wiener filtering and stochastic approximation are the origins from which all the algorithms have been derived, via a suitable choice of iterative optimization schemes and appropriate design parameters. Following this philosophy, the LMS algorithm and its offspring have been presented and interpreted as stochastic approximations of iterative deterministic steepest descent optimization schemes. On the other hand, the RLS and the quasi-RLS algorithms, like the quasi-Newton, the FNTN, and the affine projection algorithm, have been derived as stochastic approximations of iterative deterministic Newton and quasi-Newton methods. Fast implementations of these methods have been discussed. Block-adaptive, and block-exact adaptive filtering have also been considered. The performance of the adaptive algorithms has been demonstrated by computer simulations.
Efficient block implementation of the decision feedback equalizer

A new block adaptive decision feedback equalizer is developed. Both the feedforward (FF) and the feedback (FB) filters are updated once every K sample time intervals, with K being the block length. It should be noted that this block adaptation is done in such a way that the resulting filters, and the decisions as well, are identical to those computed by the conventional sample by sample LMS-based decision feedback equalizer (LMS-DFE). The new algorithm offers substantial computational savings as compared to the sample-by-sample LMS-DFE with no loss in performance. The new block DFE turns out to be particularly suitable for applications requiring long equalizers.
BER Performance Analysis of Cooperative DaF Relay Networks and a New Optimal DaF Strategy

An analytical bit error rate (BER) performance study of three detect-and-forward (DaF) policies under a common framework is presented in this paper. More specifically, the direct, thresholded and link-adaptive schemes are studied, which differ in the way the decision on the transmitted symbol is forwarded from the relay to the destination. The analysis is carried out for a single relay DaF transmission protocol and takes into account the symbol decision errors that may occur at the relay. Simple closed-form analytical BER expressions are derived for all three schemes, which are completely verified by simulations. Moreover, by minimizing a properly selected conditional BER expression, a novel optimal DaF strategy is proposed, which outperforms all previously known related methods.
New Results on SC and MRC Over Nakagami-m Fading Channels with Arbitrary Correlation Matrix

New results for the multichannel Nakagami-m fading model with an arbitrary correlation matrix are presented. By using an efficient tridiagonalization method based on Householder matrices, a union upper bound for the joint Nakagami-m probability density function and an infinite series representation for the moment generating function of the sum of gamma random variables are derived. Based on the proposed mathematical analysis, a tight union upper bound for the outage probability of multibranch selection diversity, as well as, exact analytical expressions for the outage and average error probability of multibranch maximal-ratio diversity receivers operating over identically distributed and arbitrarily correlated Nakagami-m fading channels are obtained. Our analysis is verified by comparisons of numerically evaluated results with extensive computer simulation ones.
An adaptive beamforming scheme for cooperative wireless networks

In this work we present an adaptive beamforming scheme for the uplink transmission of a cooperative wireless system consisted of a single antenna source, single antenna relays and a multiple antenna destination (base station). First, we present a SVD-based beamforming scheme under the assumption that perfect Channel State Information (CSI) is available at the transmitting nodes. Then, we develop an efficient adaptive version of this scheme which requires only partial CSI. The amount of CSI feedback is reduced by employing a prediction mechanism for the singular vectors, at both the transmitters' and the receiver's ends. We evaluate the performance of this system for the amplify-and-forward cooperation protocol.
Coalitional games for a distributed signal enhancement application

We consider a scenario in which a number of sensor nodes monitor an area, where several sources are active. Each node has an interest to estimate the signal of a particular source using measurements that, unavoidably, are mixtures of the source signals. Nodes could improve the quality of the signal of interest if they were able to use the signals measured by other nodes, however, in a such a case, communication costs must be properly taken into account. To this end, coalitional game theory is used in our study. In the case where the communication cost is zero, we prove that the cooperation of all nodes is beneficial for all. In contrast, when the communication costs are taken into account, we employ a distributed merge-split coalition formation algorithm to organize the nodes into stable cooperative groups. Simulation results are in accordance with the theoretical findings.
Efficient Symmetric Algorithms for the Modified Covariance Method for Autoregressive Spectral Analysis

Two new algorithms are developed for the efficient computation of the parameters of an autoregressive (AR) pro- cess. The proposed algorithms are based on the simultaneous least squares (LS) minimization of the forward and backward error powers. This method is known as the forward-backward linear prediction (FBLP) or the modified covariance method and is appropriate for AR spectral analysis. One of the derived al- gorithms is for batch processing, and the other is for sequential processing. The developed algorithms are called symmetric be- cause they take advantage of the computationally attractive property of symmetry, which is imposed onto the FBLP prob- lem. Due to this fact, both schemes evolve around symmetric internal variables and offer substantial computational savings over previously derived algorithms.
Adapt-Align-Combine for diffusion-based distributed dictionary learning

Diffusion-based distributed dictionary learning methods are studied in this work. We consider the classical mixed l2-l1 cost function, that employs an l2 representation error term and an l1 sparsity promoting regularizer. First, we observe that this cost function suffers from an inherent permutation ambiguity. This ambiguity may deteriorate significantly the performance of diffusion-based schemes, since the involved combination step may combine different atoms even when the same atoms exist at all dictionaries. Thus, we propose to align the dictionaries prior to the combination step. Furthermore, we define a new problem, that we call the node-specific distributed dictionary learning problem. The proposed Adapt-Align-Combine algorithm enjoys increased convergence rate as compared with a scheme that does not align the dictionaries prior to the combination. Simulation results support our findings.
New LS and SVD based methods for estimating frequencies of complex sinusoids

New least squares (LS) and singular value decomposition (SVD) based methods for the estimation of frequencies of complex sinusoids in white noise are presented. The methods are based on a new prediction problem that has some very useful properties leading to algorithms that have considerably reduced complexity. This is achieved without the sacrifice of any significant measure of performance with respect to existing methods that are based on the forward-backward predictor.<<ETX>>
Channel estimation techniques in amplify and forward relay networks

In this work we present efficient channel estimation algorithms for wideband amplify-and-forward (AF) based relay networks which utilize a recently proposed transmission model. It is shown that all channels in the network from the source through the relays to the destination/receiver node can be blindly estimated up to a phase ambiguities vector which contains the phases of the direct source to destination channel frequency response. Hence, by employing a small number of pilot symbols, phase ambiguities can be effectively resolved. As verified by computer simulations, the proposed methods exhibit high estimation accuracy even for a short training sequence, and outperform direct training-based channel estimation. A performance study of the proposed schemes in high SNR conditions is also presented and verified through computer simulations.
Opportunistic user scheduling for rate maximization in cognitive radio networks

A resource allocation framework is presented for spectrum underlay in cognitive radio networks. Novel scheduling schemes aiming at reuse of radio resources by secondary users while considering interference limitations to primary users are proposed. The focus of this work is on investigating how cognitive radio networks can benefit from spectrum sharing while maximizing the multi-user and frequency selective diversity gains, assuming single information stream in both primary and secondary links. Opportunistic interference management is used in conjunction with utility-based scheduling for the radio resource assignment in the downlink channel. Furthermore in one of the proposed schemes, network cooperation between the primary and the secondary network is considered as a means to maximize spectral efficiency.
An efficient partitioning-based scheme for 2-D convolution and application to image registration

A new scheme for efficiently computing the 2-dimensional (2-D) convolution between a fixed filter and an input sequence is presented. The scheme is particularly suitable for a special type of convolution encountered in several 2-D applications. In this type of convolution the involved filter is assumed fixed for a number of outputs which is less than the number of filter taps. By properly partitioning both the filter and the input sequences the whole problem is divided into a number of smaller convolution problems which in turn are solved efficiently using FFT. The scheme exhibits considerably reduced computational complexity as compared to other well-known FFT-based techniques. Moreover it lends itself for efficient VLSI implementation since it employs FFTs of relatively small size, as well as for parallel implementation since most steps of the method can be performed in parallel.
Adaptive completion of the correlation matrix in wireless sensor networks

The correlation structure among the sensor observations is a significant characteristic of the wireless sensor network (WSN) which can be exploited to drastically enhance the overall network performance. This structure is usually expressed as a low-rank approximation of the correlation matrix, although, in many cases the correlation of the captured data is full-rank. Thus, the computation of the full-rank correlation matrix by centralizing all the measurements into one node, puts at risk the privacy of the WSN. To overcome this problem, we impose privacy-preserving restrictions, in order to constrain the cooperation among the nodes, and hence promote the privacy. To this end, the decentralized estimation of the network-wide correlation matrix is obtained via a novel adaptive matrix completion technique, where at each step, a rank-one completion problem is solved. Through simulation experiments it has been verified that proposed algorithm converges to the full rank correlation matrix. Moreover, the proposed algorithm exhibits significantly lower computational complexity than the conventional technique.
New fast split-type algorithm for adaptive autoregressive spectral analysis

A fast time iterative algorithm is derived for the estimation of the backward predictor based on the simultaneous minimization of the forward-backward error powers. The algorithm belongs in the split-type type family and the involved internal variables are all symmetric. This is achieved by defining two symmetric vectors whose combination results in the forward-backward least-squares (FBLS) predictor without multiplications being involved. The time update of these two externally imposed symmetric vectors can be carried out using only symmetric quantities. The overall complexity of the new algorithm is 6.5 multiplications per time recursion. A saving on the order of 30% is achieved compared to the algorithm of N. Kalouptsidis and S. Theodoridis (1987).<<ETX>>
Security-Aware Max–Min Resource Allocation in Multiuser OFDMA Downlink

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 intends 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 considered problem is a mixed integer nonlinear program. For a fixed subchannel assignment, the optimal power allocation is obtained by developing an algorithm of polynomial computational complexity. In the general case, the problem is investigated from two different perspectives due to its combinatorial nature. In the first, the number of users is equal or higher than the number of subchannels, whereas in the second, the number of users is less than the number of subchannels. In the first case, we provide the optimal solution in polynomial time by transforming the original problem into an assignment one for which there are polynomial time algorithms. In the second case, the secrecy rate formula is linearly approximated and the problem is transformed to a mixed integer linear program, which is solved by a branch-and-bound algorithm. Moreover, optimality is discussed for two particular cases where the available power tends to infinity and zero, respectively. Based on the resulting insights, three heuristic schemes of polynomial complexity are proposed, offering a better balance between performance and complexity. Simulation results demonstrate that each one of these schemes achieves its highest performance at a different power regime of the system.
A robust method for estimating multipath channel parameters in the uplink of a DS-CDMA system

In this paper, the problem of estimating the multipath channel parameters of a new user entering the uplink of an asynchronous direct sequence-code division multiple access (DS-CDMA) system is addressed. The problem is described via a least squares (LS) cost function, which is non-linear with respect to the time delays and linear with respect to the gains of the multipath channel. This cost function is proved to be approximately decoupled in terms of the path delays, and thus an iterative procedure of one-dimensional searches turns out to be adequate for time delays estimation. The resulting method is computationally efficient and performs well even for a small number of training symbols. Simulation results show that the proposed technique offers a better estimation accuracy compared to existing related methods, and is robust to multiple access interference.
A new efficient decision-aided ISI canceler for mobile communication systems

Intersymbol interference (ISI) can be effectively cancelled by using a decision-aided canceler (DAC). Such a scheme exploits the fact that the introduced ISI may be adequately modeled as a linear combination of past and future decisions. Since future decisions are unavailable, a first stage is required, which provides tentative decisions to the second (the cancellation) stage. In the new DAC presented here, block adaptive schemes, implemented in the frequency domain, are used in both stages. The new DAC outperforms the existing DAC schemes in both steady-state performance and convergence speed and at the same time it exhibits a much lower computational complexity. Its good performance is retained even under hostile conditions such as those encountered in wideband mobile communication systems.
Coalitional game theoretic approach to distributed adaptive parameter estimation

In this paper, the parameter estimation problem based on diffusion least mean squares strategies is studied from a coalitional game theoretical perspective. The problem has been modeled as a non-transferable coalitional game and two scenarios have been considered, one where the value function includes only a suitable estimation accuracy criterion and another one in which the cost of coalition formation is taken into account as well. In the former scenario, we first analyze the non-emptiness of the core of the games corresponding to traditional diffusion strategies, and then, we extend the analysis to a recently proposed node-specific parameter estimation setting where the nodes have overlapped but different estimation interests. In the latter scenario, we employ a distributed coalition formation algorithm, based on merge-and-split steps, which converges to a stable coalition structure.
Efficient decision feedback equalizer for sparse multipath channels

This paper addresses the problem of blind channel equalization in the context of digital communications. Recent results have shown that certain operations applied to the source signal at the transmitter help in the blind identification and equalization of the channel at the receiver. For example, cyclostationarity is induced by multiplying the baseband data signal with a periodic sequence. Here, the baseband data signal is multiplied with a chirp sequence. Exploiting certain structural properties arising from this operation, a simple and fast converging algorithm is obtained for estimating the equalizer coefficients. The algorithm is deterministic, i.e. it provides optimal equalizer settings using finite data in the noiseless case.
Relay selection vs. repetitive transmission cooperation: Analysis under Nakagami-m fading

The performance of dual-hop Decode-and-Forward relaying with relay selection (RS) and repetitive transmission is studied over Nakagami-m fading channels. Closed-form expressions for the moment generating function of RS-based transmission with maximal-ratio diversity at the destination and repetitive transmission with selection diversity (SD) are derived. We obtain the outage probability (OP) and the average symbol error probability (ASEP) for the following relaying schemes: repetitive transmission, pure RS-based transmission and a rate-selective scheme that utilizes the selected relay node only if it provides higher achievable rate than direct transmission. Performance results show that RS-based schemes always outperform the repetitive ones. Furthermore it is shown that in terms of ASEP pure RS is always beneficial, although in terms of OP the single-hop transmission outperforms dual-hop relaying if the source to destination channel is sufficiently strong.
An Exact Performance Analysis of MRC / OSTBC over Generalized Fading Channels

A unified analytical framework for evaluating the performance of maximal ratio combining (MRC) and orthogonal space-time block coding (OSTBC) over generalized fading channels is presented. The basic motivation for developing such a framework pertains to analyzing the distribution of a sum of squared random variables (RVs) belonging to different families of fading distributions. Following a novel analytical approach stemming from the definition of a common moment generating function (MGF) model for these families of distributions, the probability density function (PDF) and the cumulative distribution function (CDF) of a general sum of squared RVs are expressed by simple infinite Gamma series expansions. Based on these expressions, the capacity and error probability of MRC/OSTBC over generalized fading channels is thoroughly studied. The developed theory is used to evaluate the performance of OSTBC for a mixed Nakagami-m/Rice fading model and novel analytical results are presented. The accuracy of the derived expressions is validated by extensive Monte Carlo simulations.
A new adaptive linear phase LS lattice structure with application to spectral line estimation

Least-squares estimation of the unknown symmetric impulse response of an FIR (finite-impulse-response) filter with linear phase results in a well-structured linear system of equations. Recently two novel algorithms for the efficient solution of this system, which respect the underlying symmetry of the linear phase filtering, were introduced. The symmetry of the problem is further exploited here to derive a novel, exact least-squares lattice algorithm for adaptive processing. The new lattice structure has considerably lower computational complexity and a more parsimonious parameterization, compared to previously derived lattice algorithms.<<ETX>>
Householder-Matrices based Analysis of SC Receivers over Rayleigh Fading Channels with Arbitrary Correlation

In this paper, new results for the multivariate Rayleigh distribution with an arbitrary correlation matrix are presented. By using an efficient tridiagonalization method based on Householder matrices, closed-form union upper bounds for its joint probability density and cumulative density functions are derived. Based on the proposed mathematical analysis, a tight union upper bound and an analytical approximation for the outage probability of multibranch selection 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.
A frequency-domain decision feedback equalizer for multipath echo cancellation

A block decision feedback equalizer (DFE) implemented in the frequency domain is derived. The new algorithm is directly applicable to multipath echo cancellation in digital transmission systems. The new equalizer consists of two distinct parts, namely a filtering part of low complexity implementable on a single VLSI chip, and an efficient filter updating part which can be implemented in today's technology DSPs. In both parts, block formulations in the frequency domain are developed, with different block sizes for each case. The resulting equalizer enjoys the computational advantages of frequency domain implementations (studied extensively for linear equalization schemes) offering at the same time the well-known superior performance of DFE.
A new fast block adaptive algorithm

This paper describes a novel efficient algorithm appropriate for adapting filters of long order. The scheme is an exact block processing counterpart of the fast Newton transversal filtering algorithm. The filters required by the algorithm blocks are much smaller than the filter length, and the obtained estimates are mathematically equivalent to those of the sample-by-sample version. This leads to a substantial saving in computational complexity without sacrificing performance as well as not having to resort to long processing delays, which limit the performance of the adaptive system.