nils-herrmann/clean_scirepeval huggingface dataset:
Noise Variance Estimation in DS-CDMA and its Effects on the Individually Optimum Receiver

In the context of synchronous random DS-CDMA (direct sequence code division multiple access) communications over a mobile network, the receiver that minimizes the per-user bit error rate (BER) is the symbol maximum a posteriori (MAP) detector. This receiver is derived under the hypothesis of perfect channel state information at the receiver. In this paper we consider the case where the channel noise variance is estimated and analyze the effect of this mismatch. We show that the bit error rate (BER) is piecewise monotonic wrt. the estimated noise variance, reaching its minimum for the true channel variance. We also provide an upper bound of the individually optimum receiver performance under noise variance mismatch. Thus we give a theoretical justification for the usual bias towards noise variance underestimation adopted by the community
Performance analysis of the MAP equalizer within an iterative receiver including a channel estimator

In this paper, we consider an iterative receiver composed of a maximum a posteriori (MAP) equalizer and a MAP decoder. During the iterations, the equalizer and the decoder exchange extrinsic information and use them as a priori in order to improve their performance. We consider here an iterative receiver including a channel estimator. We propose to study analytically the impact of both the a priori information and the channel estimation errors on the equalizer performance. We show that it is equivalent to a shift in terms of signal-to-noise ratio (SNR) and we provide an analytical expression. Simulation results show that the analytical expression we give approximate quite well the equalizer performance.
Correlation model selection for interactive video communication

Interactive video communication has been recently proposed for multi-view videos. In this scheme, the server has to store the views as compact as possible, while being able to transmit them independently to the users, who are allowed to navigate interactively among the views, hence requesting a subset of them. To achieve this goal, the compression must be done using a model-based coding in which the correlation between the predicted view generated on the user side and the original view has to be modeled by a statistical distribution. In this paper we propose a framework for lossless fixed-length source coding to select a model among a candidate set of models that incurs the lowest extra rate cost to the system. Moreover, in cases where the depth image is available, we provide a method to estimate the correlation model.
Turbo multiuser detection for coded asynchronous DS-CDMA over frequency selective channels

In order to achieve performant but low-complexity multiuser detection for coded signals, we propose to use a full turbo-CDMA structure. Noticing that a good order of demodulation is very effective in improving performance, we propose a new criterion for the order of demodulation in an asynchronous and frequency selective CDMA context. It takes into account both intersymbol and multiuser interference.
Memory consumption analysis for the GOE and PET Unequal Erasure Protection schemes

Unequal Erasure Protection (UEP) is an attractive approach to protect data flows that contain information of different priority levels. The various solutions that have been proposed can be classified into three families. The first one consists of specific, UEP-aware FEC codes, that map the information dependency within the code structure. It enables to design a specific solution, valid for a specific data flow, as in . However, because this is a specific solution, its practical interest is also narrowed. In this work we focus on two additional solution families. One family implements UEP thanks to a dedicated packetization scheme, as it is the case with Priority Encoding Transmission (PET) , while the other family uses a dedicated signaling scheme, as is the case with the Generalized Object Encoding (GOE) . These two solutions have the main benefit of being compatible with existing standardized Application Layer FEC (AL-FEC) schemes, which is a major practical benefit. Through a careful modeling of both proposals, we have demonstrated that the protection performance of both approaches are equivalent [7]. However additional key differences become apparent when considering such a practical metric as the peak memory consumption. Thanks to a modeling of the packet storage behavior at the receiver side, and by considering two major parameters, namely the channel loss probability and the permutation type, we show that the GOE scheme (without interleaver) requires a smaller memory storage than PET. This result is reversed if GOE uses a uniform interleaver.
Exemplar-based image inpainting: Fast priority and coherent nearest neighbor search

Greedy exemplar-based algorithms for inpainting face two main problems, decision of filling-in order and selection of good exemplars from which the missing region is synthesized. We propose an algorithm that tackle these problems with improvements in the preservation of linear edges, and reduction of error propagation compared to well-known algorithms from the literature. Our improvement in the filling-in order is based on a combination of priority terms, previously defined by Criminisi, that better encourages the early synthesis of linear structures. The second contribution helps reducing the error propagation thanks to a better detection of outliers from the candidate patches carried. This is obtained with a new metric that incorporates the whole information of the candidate patches. Moreover, our proposal has significant lower computational load than most of the algorithms used for comparison in this paper.
Exact performance analysis of the oracle receiver for compressed sensing reconstruction

A sparse or compressible signal can be recovered from a certain number of noisy random projections, smaller than what dictated by classic Shannon/Nyquist theory. In this paper, we derive the closed-form expression of the mean square error performance of the oracle receiver, knowing the sparsity pattern of the signal. With respect to existing bounds, our result is exact and does not depend on a particular realization of the sensing matrix. Moreover, our result holds irrespective of whether the noise affecting the measurements is white or correlated. Numerical results show a perfect match between equations and simulations, confirming the validity of the result.
Turbo decoding of multiple description source and channel codes

We address the joint use of a heterogeneous source of redundancy in a coding chain: multiple description coding and channel coding where the redundancy on the source symbols and on the transmitted bits is generated by quantized frame expansions and convolutional coding, respectively. The received signal is viewed as the noisy output of a serial concatenated coding system, and a turbo-type decoder that relies on a soft information exchange between its decoding blocks is proposed to iteratively reconstruct the source signal. EXIT charts are used to characterize the convergence behavior of the proposed iterative receiver, which in turn is used to select system parameters such as the channel code. The turbo receiver is simulated for 2 /spl times/ 5 frame expansions where the results indicate a significant gain achieved by the decoding iteration both in the erasure and no erasure cases.
Improved interference cancellation for turbo-equalization

Sub-optimal joint equalization and decoding is performed by iterated equalization and decoding. This processing is named turbo-equalization with reference to the turbo-decoding of serially concatenated codes. We propose to optimize the equalizer structure (an interference canceler) thanks to the training sequence available to estimate the channel impulse response. The interference canceler, optimized at each iteration, permits one to reduce the number of iterations needed to achieve a given performance. The gain is all the more important that the channel is hard to equalize.
Low-cost Turbo receiver for coded asynchronous DS-CDMA in frequency selective channels

When channel coding is used on a CDMA transmission, the most appropriate approach for reception is joint Multiuser detection and decoding. This joint problem can be successfully processed by the Turbo-techniques. Therefore, we consider a low-complexity iterative receiver for the CDMA uplink transmission, called Turbo-CDMA, and we propose to further improve it. First, we derive a new DFE to be used at the first iteration. Then, we propose to fully use the decoded information in a new structure called Full Turbo-CDMA. Finally, we derive a new demodulation order that is well suited to the Turbo-CDMA receiver. This improved receiver, called Full Turbo-CDMA, aims to eliminate MUI and ISI almost completely in the presence of frequency selective channels. Improved performance is illustrated by simulations.
Optimal Matching in Wireless Sensor Networks

We design a wireless sensor network (WSN) in terms of rate and power allocation in order to send without loss the data gathered by the nodes to a common sink. Correlation between the data and channel impairments dictate the constraints of the optimization problem. We further assume that the WSN uses off-the-shelf compression and channel coding algorithms. More precisely source and channel coding are separated and distributed source coding is performed by pairs of nodes. This raises the problem of optimally matching the nodes. We show that under all these constraints the optimal design (including rate/power allocation and matching) has polynomial complexity (in the number of nodes in the network). A closed form solution is given for the rate/power allocation, and the matching solution is readily interpreted. For noiseless channels, the optimization matches close nodes whereas, for noisy channels, there is a tradeoff between matching close nodes and matching nodes with different distances to the sink. This fact is illustrated by simulations based on empirical measures. We also show that the matching technique provides substantial gains in either storage capacity or power consumption for the WSN with regard to the case where the correlation between the nodes is not used.
Iterative multi-user algorithm for convolutionally coded asynchronous DS-CDMA systems: turbo-CDMA

A low-complexity and iterative receiver is proposed for the CDMA uplink, consisting of a low-complexity equalizer and single-user decoders. Iterations are used to feed the equalizer with more reliable data, which improves performance. This receiver called turbo-CDMA is aimed to eliminate MUI almost completely in the presence of multipath and to be robust to the near-far effect. Simulation results show that the performance approaches single-user performance at moderate and also low signal-to-noise ratios, even for a severe distortion channel.
Joint training interval length and power allocation optimization for MIMO flat fading channels

In this paper, we jointly optimize the training interval length and the power allocation for MIMO (Multiple-Input Multiple-Output) flat fading channels when a Maximum A Posteriori (MAP) detector is used at the receiver and the training and data powers are allowed to vary. We calculate the equivalent Signal-to-Noise Ratio (SNR) at the output of the MAP detector. Based on this expression, we define an effective SNR taking into account the data throughput loss due to the use of pilot symbols. We find that the optimal length of the training interval maximizing this quantity is equal to the number of transmit antennas. When the values of the pilot and data powers are not allowed to be different, we give the optimal training interval length maximizing the effective SNR and we show that it can be larger than the number of transmit antennas.
Generalized Map: Sequence Detection for Non-Ideal Frequency Selective Channel Knowledge

In this paper, we consider the problem of maximum a posteriori (MAP) equalization of the received signal over a frequency selective channel when the channel is not perfectly known at the receiver. The derivation of the MAP criterion in this case leads to an expression for which no exact implementation exists in the literature. In this paper, we propose to solve the problem by using the expectation-maximization (EM) algorithm. The algorithm we propose has linear-time complexity per iteration. Simulations show that few iterations are required to reach the performance of the MAP equalizer with perfect channel knowledge.
Symbol MAP detection aided with first channel estimate for TDMA systems over a frequency selective channel

In this paper, we consider joint iterative equalization and channel estimation for TDMA systems over a frequency selective, quasi-static fading channel. We assume a slowly varying channel impulse response from one burst to the following one. We modify the maximum a posteriori (MAP) criterion by taking into account an a priori channel estimate obtained from the burst preceding the one processed. Assuming that the receiver knows the channel statistics, we propose to use the EM (Expectation-Maximization) algorithm for a joint iterative equalization and channel estimation. During each iteration of the receiver, the EM algorithm reestimates the channel by using the a posteriori probabilities (APP) available at the output of the equalizer. The algorithm we propose has linear-time complexity per iteration. Simulations show that our MAP receiver reaches the performance of the MAP equalizer with perfect channel knowledge after a few iterations, for high SNR and for small values of normalized Doppler spread. They show also that our receiver outperforms the MAP equalizer that uses a channel estimate obtained by using a training sequence.
Source Coding with Side Information at the Decoder and Uncertain Knowledge of the Correlation

This paper considers the problem of lossless source coding with side information at the decoder, when the correlation model between the source and the side information is uncertain. Four parametrized models representing the correlation between the source and the side information are introduced. The uncertainty on the correlation appears through the lack of knowledge on the value of the parameters. For each model, we propose a practical coding scheme based on non-binary Low Density Parity Check Codes and able to deal with the parameter uncertainty. At the encoder, the choice of the coding rate results from an information theoretical analysis. Then we propose decoding algorithms that jointly estimate the source vector and the parameters. As the proposed decoder is based on the Expectation-Maximization algorithm, which is very sensitive to initialization, we also propose a method to produce first a coarse estimate of the parameters.
Joint equalization and decoding: why choose the iterative solution?

This paper deals with turbo-equalization as a joint equalization and decoding algorithm. The performance analysis shows that there is a trigger point in this iterative process, followed by a breakdown effect. Actually after a given point (the trigger one), the BER decreases steeply as a function of the decoding step p. We compare the performance of the turbo-equalizer with that of the optimal joint receiver and show that it can match the bound over the optimal disjoint receiver.
The impact of both a priori information and channel estimation errors on the MAP equalizer performance

To combat the effects of intersymbol interference (ISI), the optimal equalizer to be used is based on maximum a posteriori (MAP) detection. In this paper, we consider the case where the MAP equalizer is fed with a priori information on the transmitted data and propose to study analytically their impact on the MAP equalizer performance. We assume that the channel is not perfectly estimated and show that the use of both the a priori information and the channel estimate is equivalent to a shift in terms of signal-to-noise ratio (SNR) for which we provide an analytical expression. Simulation results show that the analytical expression approximates well the equalizer behavior
Universal lossless coding with random user access: The cost of interactivity

We consider the problem of video compression with free viewpoint interactivity. It is well believed that allowing the user to choose its view will incur some loss in terms of compression efficiency. Here we derive the complete rate-storage region for universal lossless coding under the constraint of choosing the view at the receiver. This leads to a counterintuitive result: freely choosing its view at the receiver incurs a loss in terms of storage only and not in the transmission rate. The gain of the optimal scheme with respect to interactive schemes proposed so far is derived and a practical scheme that achieves this gain is proposed.
Design methods for irregular repeat-accumulate codes

We optimize the random-like ensemble of irregular repeat-accumulate (IRA) codes for binary-input symmetric channels in the large block-length limit. Our optimization technique is based on approximating the evolution of the densities (DE) of the messages exchanged by the belief-propagation (BP) message-passing decoder by a one-dimensional dynamical system. In this way, the code ensemble optimization can be solved by linear programming. We propose four such DE approximation methods, and compare the performance of the obtained code ensembles over the binary-symmetric channel (BSC) and the binary-antipodal input additive white Gaussian noise channel (BIAWGNC). Our results clearly identify the best among the proposed methods and show that the IRA codes obtained by these methods are competitive with respect to the best known irregular low-density parity-check (LDPC) codes. In view of this and the very simple encoding structure of IRA codes, they emerge as attractive design choices.
Training Interval Length Optimization for MIMO Flat Fading Channels Using Decision-Directed Channel Estimation

In this paper, we address the problem of optimization of the training sequence interval for MIMO (Multiple- Input Multiple-Output) flat fading channels when an iterative receiver composed of a likelihood generator and a Maximum A Posteriori (MAP) decoder is used. At each iteration of the receiver, the channel is estimated using the hard decisions on the transmitted symbols at the output of the decoder. The optimal length of the training interval is found by maximizing an effective signal-to-noise ratio (SNR) taking into account the data throughput loss due to the use of pilot symbols.
Performance analysis of the MAP equalizer with a priori and distribution of the extrinsic LLRs

To combat the effects of intersymbol interference, the optimal equalizer to be used is based on maximum a posteriori (MAP) detection. We consider the case where the MAP equalizer is provided with a priori information on the transmitted data, as in a turbo equalizer. We propose to study analytically the impact of the a priori on the MAP equalizer performance. We distinguish two cases: the case of unreliable a priori and the case of reliable a priori. We show that when the a priori is reliable, it is better to perform the detection of the transmitted symbols based on the a posteriori outputs of the MAP equalizer rather than on the a priori only. In a turbo equalizer, the equalizer and the decoder exchange extrinsic log likelihood ratios (LLRs). We propose here to study analytically their distribution. The aim of this study is to perform in the future the analytical convergence analysis of turbo equalizers using the MAP equalization.
Training sequence optimization for frequency selective channels with MAP equalization

In this paper, we address the problem of optimization of the training sequence length for frequency selective channels when a maximum a posteriori (MAP) equalizer is used. The optimal length of the training sequence is found by maximizing an effective signal-to-noise ratio (SNR) and an effective channel capacity of the training-based transmission scheme. We study these problems of optimization when the training and data powers are equal and when they are allowed to be different. When the powers can be different, we give the optimal power allocation.
On the analysis of the MAP equalizer performance within an iterative receiver

We consider an iterative receiver composed of a maximum a posteriori (MAP) equalizer and a MAP decoder. During the iterations, the equalizer and the decoder exchange extrinsic information and use them as a priori in order to improve their performance. We propose here to study analytically the impact of the a priori information provided by the channel decoder on the equalizer performance. We show that it is equivalent to a gain in terms of signal-to-noise ratio (SNR) and we provide an analytical expression. Simulation results show that this expression approximates quite well the SNR gain.
Universal Wyner-Ziv coding for Gaussian sources

This paper considers the problem of lossy source coding with side information at the decoder only, for Gaussian sources, when the joint statistics of the sources are partly unknown. We propose a practical universal coding scheme based on scalar quantization and nonbinary LDPC codes, which avoids the binarization of the quantized coefficients. We first explain how to choose the rate and to construct the LDPC coding matrix. Then, a decoding algorithm that jointly estimates the source sequence and the joint statistics of the sources is proposed. The proposed coding scheme suffers no loss compared to the practical coding scheme with same rate but known variance.
Lower bounds on the performance of the map equalizer with a priori over mimo systems

In this paper, we consider a transmission of quadrature phase shift keying (QPSK) symbols over a frequency selective multiple input multiple output (MIMO) channel. We propose to study analytically the impact of a priori information (provided for instance by a decoder in a turbo equalizer) on the maximum a posteriori (MAP) equalizer performance. We derive analytical expressions of tight lower bounds on the bit error probability at the output of the equalizer. Simulations show that the analytical expressions approximate well the bit error rate (BER) at the output of the MAP equalizer.
Impact of physical layer design on the deployment of a sensor network with correlated observations

We consider a sensor network which observes a spatially correlated random field. The Slepian-Wolf scheme is known to help reduce the transmission requirement in such a scenario. We seek a "good" operating point for this network, in order to maximize the information gathered with a constraint on the network budget (which includes the number of nodes and their transmission powers). We present a comparative study of the system performance obtained under various transmission/reception schemes and fading coefficient statistics. Finally, we study the optimal density of the sensor network.
Practical Coding Scheme for Universal Source Coding with Side Information at the Decoder

This paper considers the problem of universal lossless source coding with side information at the decoder only. The correlation channel between the source and the side information is unknown and belongs to a class parametrized by some unknown parameter vector. A complete coding scheme is proposed that works well for any distribution in the class. At the encoder, the proposed scheme encompasses the determination of the coding rate and the design of the encoding process. Both contributions result from the information-theoretical compression bounds of universal lossless source coding with side information. Then a novel decoder is proposed that takes into account the available information regarding the class. The proposed scheme avoids the use of a feedback channel or the transmission of a learning sequence, which both would result in a rate increase at finite length.