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Entropy
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Entropy and Time–Frequency Signal Processing
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Entropy and Time–Frequency Signal Processing

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 1740

Special Issue Editors

Dr. Steven Sandoval

E-Mail Website
Guest Editor
Klipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: signal processing; audio/speech processing; time-frequency analysis; instantaneous spectral analysis; latent signal analysis; geometric algebra
Special Issues, Collections and Topics in MDPI journals
Dr. Moises Soto-Bajo

E-Mail Website
Guest Editor Assistant
CONAHCYT-Facultad de Ciencias Físico Matemáticas, Benemérita Universidad Autónoma de Puebla, Puebla, México
Interests: Fourier analysis; harmonic analysis; functional analysis; wavelet analysis; signal processing; mathematical epidemiology; mathematical modeling

Special Issue Information

Dear Colleagues,

The goal of time–frequency analysis is to represent a signal in a domain that allows for the simultaneous description of the temporal and spectral characteristics of that signal. However, the problem of time–frequency representation is generally not uniquely solvable due to being under-constrained, providing few general mathematical constraints and allowing for infinite degrees of freedom. Historically, this has led to many different approaches to signal representation. For instance, short-time Fourier transform (STFT) and other time–frequency distributions and time-scale methods have long been staples of time–frequency analysis. In recent years, AM-FM models and adaptive mode decompositions have made further advances in modeling signals with non-stationary frequency components, and several new developments in time–frequency reassignment and synchrosqueezing methods have been made.

This Special Issue aims to offer researchers the opportunity to share their original work and ideas around time–frequency analysis through papers providing theoretical insights, recent mathematical advances, and the latest applications of techniques in this field for solving various problems. Additionally, works that utilize entropy-based methodologies or other topics related to information theory, such as local entropy, time variation/evolution of entropy, and entropy cycles, are especially encouraged.

Dr. Steven Sandoval
Guest Editor

Dr. Moises Soto-Bajo
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • time-frequency analysis
  • instantaneous frequency
  • time-frequency representation
  • harmonic analysis
  • time-scale analysis
  • wavelet analysis
  • signal decompositions
  • local entropy
  • entropy evolution

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 3014 KiB  
Article
Beamforming Design for STAR-RIS-Assisted NOMA with Binary and Coupled Phase-Shifts
by Yongfei Liu, Yuhuan Wang and Weizhang Xu
Entropy 2025, 27(2), 210; https://doi.org/10.3390/e27020210 - 17 Feb 2025
Viewed by 535
Abstract
This paper investigates the joint optimization of active and passive beamforming in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted non-orthogonal multiple access (NOMA) systems, with the aim of maximizing system throughput and improving overall performance. To achieve this goal, we propose an [...] Read more.
This paper investigates the joint optimization of active and passive beamforming in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted non-orthogonal multiple access (NOMA) systems, with the aim of maximizing system throughput and improving overall performance. To achieve this goal, we propose an iterative and efficient algorithmic framework. For active beamforming optimization, the fractional programming (FP) method is employed to reformulate the non-convex optimization problem into a convex problem, making it more tractable. Additionally, Nesterov’s extrapolation technique is introduced to enhance the convergence rate and reduce computational overhead. For the phase optimization of the STAR-RIS, a binary phase design method is proposed, which reformulates the binary phase optimization problem as a segmentation problem on the unit circle. This approach enables a closed form solution that can be derived in linear time. Simulation results demonstrate that the proposed algorithmic framework outperforms existing benchmark algorithms in terms of both system throughput and computational efficiency, verifying its effectiveness and practicality in STAR-RIS-assisted NOMA systems. Full article
(This article belongs to the Special Issue Entropy and Time–Frequency Signal Processing)
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21 pages, 7174 KiB  
Article
Determination Method of Optimal Decomposition Level of Discrete Wavelet Based on Joint Jarque–Bera Test and Combination Weighting Method
by Zhanpeng Zhang, Changjian Liu, Min Wang, Shuang Sun and Zhao Zhan
Entropy 2025, 27(2), 108; https://doi.org/10.3390/e27020108 - 23 Jan 2025
Viewed by 897
Abstract
To overcome the limitations of traditional evaluation indicators in determining the optimal wavelet decomposition level, this paper proposes an adaptive method for selecting the best decomposition level by combining the Jarque–Bera test and a composite weighting approach. Firstly, in the noise extraction stage, [...] Read more.
To overcome the limitations of traditional evaluation indicators in determining the optimal wavelet decomposition level, this paper proposes an adaptive method for selecting the best decomposition level by combining the Jarque–Bera test and a composite weighting approach. Firstly, in the noise extraction stage, the Jarque–Bera test is employed to ensure that the extracted noise follows Gaussian white noise characteristics, thereby avoiding issues of insufficient denoising or signal distortion. Secondly, in the evaluation stage of the denoised signal, a comprehensive consideration of the geometric and physical meanings of various evaluation metrics, as well as the Pearson correlation coefficients between them, is undertaken. The RMSE and smoothness are selected as evaluation indicators for the denoising performance. Since these two metrics describe signal characteristics from different dimensions, a weighted combination approach is used to generate a single composite evaluation index. Additionally, to overcome the limitations of using a single weighting method, a composite weighting strategy is proposed by combining the entropy weight method and the coefficient of variation method. The composite coefficient between these two weighting methods is calculated using the variance coefficient method, yielding a new composite evaluation metric. A smaller value of this metric indicates better denoising performance, and the corresponding optimal decomposition level is more accurately determined. The simulation results demonstrate that the proposed comprehensive evaluation method can accurately determine the optimal wavelet decomposition level in both known and unknown truth-value cases, exhibiting a high accuracy and good applicability. Furthermore, the experimental results show that using the optimal decomposition level determined by the proposed method for wavelet denoising leads to smoother peak regions, more stable waveforms and significantly improved denoising performance. Full article
(This article belongs to the Special Issue Entropy and Time–Frequency Signal Processing)
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