Figure 1 - uploaded by Armando Vannucci
Content may be subject to copyright.
Source publication
Impulsive noise is the main limiting factor for transmission over channels affected by electromagnetic interference. We study the estimation of (correlated) Gaussian signals in an impulsive noise scenarios. In this work, we analyze some of the existing as well as some novel estimation algorithms. Their performance is compared, for the first time, f...
Contexts in source publication
Context 1
... nature of impulsive noise, a Markov-Gaussian model was proposed in [3]. The following transition probability matrix, accounts for the correlation between successive noise states. As in any transition matrix, the elements are such that P GG + P GB = 1 and P BG + P BB = 1, so that in the state diagram represention of the Markov-Gaussian noise, in Fig. 1, the outgoing transition probabilities sum to one. Moreover, by introducing a correlation parameter γ = (P GB + P BG ) −1 , the stady-state probabilities of the two noise states are P G = γP GB and P B = γP BG . The parameter γ determines the amount of correlation between successive noise states, so that a larger γ implies an increased ...
Context 2
... nature of impulsive noise, a Markov-Gaussian model was proposed in [3]. The following transition probability matrix, accounts for the correlation between successive noise states. As in any transition matrix, the elements are such that P GG + P GB = 1 and P BG + P BB = 1, so that in the state diagram represention of the Markov-Gaussian noise, in Fig. 1, the outgoing transition probabilities sum to one. Moreover, by introducing a correlation parameter γ = (P GB + P BG ) −1 , the stady-state probabilities of the two noise states are P G = γP GB and P B = γP BG . The parameter γ determines the amount of correlation between successive noise states, so that a larger γ implies an increased ...
Similar publications
MPI communication optimization is essential for high-performance applications. The communication performance models have made some achievements in improving the efficiency of collective algorithms and optimizing communication scheduling. Instead of using hardware-related parameters such as bandwidth and latency for communication modeling, recent st...
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i.e., message passing) behind GNNs inducing unfairness issue remains unknown. In this work, we theoretically and experimentally demonstrate that rep...
Many cyber-physical systems are offloading computation-heavy programs to hardware accelerators (e.g., GPU and TPU) to reduce execution time. These applications will self-suspend between offloading data to the accelerators and obtaining the returned results. Previous efforts have shown that self-suspending tasks can cause scheduling anomalies, but n...
The growing demand for transparency and interpretability in critical domains has driven increased interests in comprehending the explainability of Message-Passing (MP) Graph Neural Networks (GNNs). Although substantial research efforts have been made to generate explanations for individual graph instances, identifying global explaining concepts for...
The atomic cluster expansion provides local, complete basis functions that enable efficient parametrization of many-atom interactions. We extend the atomic cluster expansion to incorporate graph basis functions. This naturally leads to representations that enable the efficient description of semilocal interactions in physically and chemically trans...
Citations
This paper addresses the problem of mitigating unknown partial path overlaps in communication systems. This study demonstrates that by utilizing the front-end insight of communication systems along with the sum–product algorithm applied to factor graphs, it is possible not only to track these overlapping components accurately, but also to detect all multipath channel impairments simultaneously. The proposed methodology involves discretizing channel parameters, such as channel paths and attenuation coefficients, to ensure the most accurate computation of means of Gaussian observations. These parameters are modeled as Bernoulli random variables with priors set to 0.5. A notable aspect of the algorithm is its integration of the received signal power into the calculation of noise variance, which is critical for its performance. To further reduce the receiver complexity, a novel implementation strategy, based on provided pre-defined look up tables (LOTs) to the reciver, is introduced. The simulation results, covering both distributed and concentrated pilot scenarios, reveal that the algorithm performs almost equally under both conditions and surpasses the established upper bound in performance.
