Results 21 to 30 of about 1,446,045 (274)
Advanced channel estimation in OTFS and NOMA using deep bayesian gaussian processes and compressive sensing [PDF]
Scientific ReportsFor high-mobility wireless communications in Orthogonal Time Frequency Space (OTFS) and Non-Orthogonal Multiple Access (NOMA) systems, accurate Channel Estimation (CE) is mandatory. Conventional pilot-based methods, such as Least Squares (LS) and Minimum
Nitha Anilkumar, Sudhakar Sengan
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Heterogeneous Bayesian compressive sensing for sparse signal recovery
IET Signal Processing, 2014 This study focuses on the issue of sparse signal recovery with sparse Bayesian learning in the context of a heterogeneous noise model, called by the heterogeneous Bayesian compressive sensing. The main contribution is to exploit the capability of noise variance learning in performance improvement and applicability enhancement.
Kaide Huang, Guoli Wang
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Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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Bayesian online compressed sensing [PDF]
Physical Review E, 2016 In this paper, we explore the possibilities and limitations of recovering sparse signals in an online fashion. Employing a mean field approximation to the Bayes recursion formula yields an online signal recovery algorithm that can be performed with a computational cost that is linearly proportional to the signal length per update.
Paulo V. Rossi +2 more
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Compressed sensing and Bayesian experimental design [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 We relate compressed sensing (CS) with Bayesian experimental design and provide a novel efficient approximate method for the latter, based on expectation propagation. In a large comparative study about linearly measuring natural images, we show that the simple standard heuristic of measuring wavelet coefficients top-down systematically outperforms CS ...
Seeger, Matthias, Nickisch, Hannes
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Bayesian compressive sensing and projection optimization [PDF]
Proceedings of the 24th international conference on Machine learning, 2007 This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on ...
Shihao Ji 0001, Lawrence Carin
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Bayesian compressive sensing for phonetic classification [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 In this paper, we introduce a novel bayesian compressive sensing (CS) technique for phonetic classification. CS is often used to characterize a signal from a few support training examples, similar to k-nearest neighbor (kNN) and Support Vector Machines (SVMs). However, unlike SVMs and kNNs, CS allows the number of supports to be adapted to the specific
Tara N. Sainath +3 more
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Bayesian compressed sensing: Improving inference [PDF]
2013 IEEE China Summit and International Conference on Signal and Information Processing, 2013 In this paper we present a set of theoretical results regarding inference algorithms for hierarchical Bayesian networks. More specifically we focus on a specific type of networks which result in highly sparse models for the input. Bayesian inference in these networks usually is based on optimising a non-convex cost function of the model parameters.
Evripidis Karseras +2 more
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Complex multitask compressive sensing using Laplace priors
Electronics Letters, 2021 Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex‐valued problems.
Qilei Zhang, Zhen Dong, Yongsheng Zhang
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Complex multitask Bayesian compressive sensing [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values.
Qisong Wu +3 more
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