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Found 9 papers, 0 papers with code
Optimal Mobility and Communication Strategy to Maximize the Value of Information in IoT Networks
In this work, we consider an industrial IoT network with a set of heterogeneous sensing devices and an intelligent mobile entity.
Performance Analysis of RIS-Assisted Large-Scale Wireless Networks Using Stochastic Geometry
Furthermore, it is also unveiled that the system performance is independent of the density of TXs with the nearest association strategy in the interference-limited scenario.
A Framework for Characterising the Value of Information in Hidden Markov Models
In this paper, a general framework is formalised to characterise the value of information (VoI) in hidden Markov models.
Information Theory Information Theory
Study of Intelligent Reflective Surface Assisted Communications with One-bit Phase Adjustments
For the imperfect phase configuration, we assume that each element of the IRS has a one-bit phase shifter (0{\deg}, 180{\deg}) and an expression for the outage probability is obtained in the form of an integral.
Chernoff Bounds and Saddlepoint Approximations for the Outage Probability in Intelligent Reflecting Surface Assisted Communication Systems
Furthermore, through an exact asymptotic (a large number of reflecting elements) analysis based on a saddlepoint approximation, we derive closed-form expressions of the outage probability for systems with and without a direct link and obtain the corresponding diversity orders.
Model-Based Detector for SSDs in the Presence of Inter-cell Interference
Our simulations showed that the iterative scheme can significantly improve the bit error rate even in the quantized case.
Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors
In this paper, the line spectral estimation (LSE) problem with multiple measurement vectors (MMVs) is studied utilizing the Bayesian methods.
Information Theory Information Theory
Variational Bayesian Inference of Line Spectra
We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i. e., not restricted to a grid; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection.
Sparse Estimation using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models
Motivated by the relative scarcity of formal tools for SBL in complex-valued models, this paper proposes a GSM model - the Bessel K model - that induces concave penalty functions for the estimation of complex sparse signals.
