Kullback–Leibler Divergence Between Multivariate Generalized Gaussian Distributions

Bouhlel, Nizar; Dziri, Ali

doi:10.1109/lsp.2019.2915000

Cited by 37 publications

(14 citation statements)

References 16 publications

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“…Despite not being directly implemented in modern software packages (such as Matlab, Mathematica, Maple, etc. ), their computation (efficiently performed by numerical calculation of the inverse Laplace transform, for example, exhaustively discussed in [47][48][49][50]), including truncation errors, and the required number of summands and achievable computational gain (relative to the numeric integration) are frequently discussed in the literature (see, for instance, [16,28,32,51,52]).…”

Section: Discussion and Further Generalizationmentioning

confidence: 99%

A Novel Unified Framework for Energy-Based Spectrum Sensing Analysis in the Presence of Fading

Gvozdarev

2022

Sensors

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This paper studies the performance of the energy-based sensing procedure in the presence of multipath fading and shadowing effects in terms of its average probability of detection (APD), average receiver operating characteristic (AROC) and the area under the AROC curve (AUC). A new generalization for the class of the fading channel moment generating functions (MGFs) (i.e., factorized power type (FPT) MGF) was proposed and applied for the construction of the unified framework for the analytical treatment of the formulated problem. The contiguity of the proposed model with the existing classical ones (Rayleigh, Nakagami-m, Hoyt, η−μ, κ−μ shadowed and Mixture-Gamma) was demonstrated. Within the assumed MGF representation, the novel closed-form solutions and computationally efficient approximation for APD and AUC are derived. The obtained general expressions were then applied for derivation of the new results for the recent generalized fading channel models: Fluctuating Beckmann and Beaulieu-Xie shadowed. For each of the models, high-SNR asymptotic expressions were obtained. Lastly, numeric simulation was performed to verify the correctness of the derived results, to establish the dependencies of the sensing performance quality from the channel parameters and to identify the specific ranges of their asymptotic behavior.

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Section: Discussion and Further Generalizationmentioning

confidence: 99%

A Novel Unified Framework for Energy-Based Spectrum Sensing Analysis in the Presence of Fading

Gvozdarev

2022

Sensors

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“…The variational approximation to the Bayesian posterior distribution on the weights is a feasible method. Variational learning finds the parameters

of a distribution on the weights

that minimizes the Kullback–Leibler (KL) divergence [ 63 ] with the true Bayesian posterior on the weights:

…”

Section: Distributed Deep Fusion Predictormentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.

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“…First, we need to build a Q value network and a policy network. The Q-value network outputs single-value Q through several layers of neural networks, and the policy network outputs a Gaussian distribution [29]. In this process, the neural network will be updated.…”

Section: Soft Actor-criticmentioning

confidence: 99%

Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game

Liu

Wang

et al. 2021

IEEE Access

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The rapid development of renewable energy power has improved global energy and environmental problems. However, with the high volatility of renewable energy, it is an important challenge to guarantee the consumption of renewable energy and the reliable operation of high percentage renewable energy power systems. To solve this problem, this paper proposes a tracking absorption strategy for renewable energy based on the interaction between the supply side and the demand side, which adjusts the charging process of electric vehicles (EVs) through electric vehicle aggregator (EVA) to realize the tracking absorption of renewable energy abandoned electricity. In view of this process, we analyze the interaction among power grid, EVA and renewable energy generation (REG) as well as their market characteristics. The master-slave game model of EVA and REG was constructed considering the charging behavior characteristics of EVs and the output characteristics of REGs. Then the model solving strategy based on soft actor-critic (SAC) algorithm is proposed, and the REG pricing strategy and EVA scheduling strategy are calculated to optimize the mutual benefits. The case analysis shows that, under the same scale of electric vehicles, the proposed method can promote about 93.89% of the power abandonment consumption of wind power system, 96.00% of the photovoltaic system, and 97.41% of the wind-solar system. This strategy reduces the electricity purchase cost of EVA, promotes the interaction among renewable energy , vehicles and power grid, and improves the utilization efficiency of renewable energy.

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Kullback–Leibler Divergence Between Multivariate Generalized Gaussian Distributions

Cited by 37 publications

References 16 publications

A Novel Unified Framework for Energy-Based Spectrum Sensing Analysis in the Presence of Fading

A Novel Unified Framework for Energy-Based Spectrum Sensing Analysis in the Presence of Fading

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game

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