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Special Issue "Coding and Entropy"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 31 August 2023 | Viewed by 1839

Special Issue Editors

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: B5G technology; network coding; network information theory; machine learning and big data analysis
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: Shannon theory; information inequalities and entropy region; network coding
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Interests: information theory; channel coding and its applications

E-Mail Website
Guest Editor Assistant
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: semantic communications; information theory; image compression; machine learning and source coding

Special Issue Information

Dear Colleagues,

The Special Issue focuses on new developments in multi-type coding and entropies and their applications in communications, data processing and machine learning.

Shannon’s information theory answers two fundamental questions raised by communication theory: What is the ultimate data compression, and what is the ultimate transmission rate of communication? Entropy is the core concept of this framework, with coding beings its most significant technology, including source coding, channel coding, and network coding. In this context, a number of metrics, such as Shannon entropy, Rényi entropy, message importance measure, sample entropy, fuzzy entropy, and permutation entropy, are introduced to quantify the irregularity or uncertainty of signals and images. Various coding theories and methods have also been proposed to reduce the occupancy of communication and storage resources in order to improve the objective efficiency of the communication network and the subjective experience of clients.

With advances in intelligent vision algorithms and devices, data reprocessing and secondary propagation are becoming increasingly prevalent. the production of a large amount of similar data is becoming more rapid and widespread, resulting in a homogeneity and similarity in data such as images and videos and creating new challenges for information theory. Novel entropy and coding methods may play a significant role in the era of big data.

We invite authors to submit previously unpublished contributions in any field related to developments and applications of information theory in coding and entropy, including but not limited to, the following subtopics:

  • Mathematical extensions for entropy analysis;
  • Source coding and channel coding techniques;
  • Network coding and its related topics;
  • Two- and three-dimensional entropy methods for image analysis;
  • Entropy optimization and modeling for performance enhancement;
  • Entropy-based image, signal processing, and coding;
  • Network information theory and semantic information theory;
  • Compressed sensing and rate-distortion theory;
  • Application of entropy and coding in machine learning;
  • Application of machine learning method to developments of coding and entropy.

Prof. Dr. Pingyi Fan
Dr. Qi Chen
Dr. Suihua Cai
Guest Editors

Gangtao Xin
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 2000 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

  • information theory
  • entropy
  • data-driven entropy modeling
  • source coding
  • channel coding
  • network coding
  • coding techniques
  • information-theoretic methods
  • entropy-based methods
  • machine learning

Published Papers (3 papers)

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Research

Article
Transformer-Based Detection for Highly Mobile Coded OFDM Systems
Entropy 2023, 25(6), 852; https://doi.org/10.3390/e25060852 - 26 May 2023
Viewed by 219
Abstract
This paper is concerned with mobile coded orthogonal frequency division multiplexing (OFDM) systems. In the high-speed railway wireless communication system, an equalizer or detector should be used to mitigate the intercarrier interference (ICI) and deliver the soft message to the decoder with the [...] Read more.
This paper is concerned with mobile coded orthogonal frequency division multiplexing (OFDM) systems. In the high-speed railway wireless communication system, an equalizer or detector should be used to mitigate the intercarrier interference (ICI) and deliver the soft message to the decoder with the soft demapper. In this paper, a Transformer-based detector/demapper is proposed to improve the error performance of the mobile coded OFDM system. The soft modulated symbol probabilities are computed by the Transformer network, and are then used to calculate the mutual information to allocate the code rate. Then, the network computes the codeword soft bit probabilities, which are delivered to the classical belief propagation (BP) decoder. For comparison, a deep neural network (DNN)-based system is also presented. Numerical results show that the Transformer-based coded OFDM system outperforms both the DNN-based and the conventional system. Full article
(This article belongs to the Special Issue Coding and Entropy)
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Article
Adaptive Bit-Labeling Design for Probabilistic Shaping Based on Residual Source Redundancy
Entropy 2023, 25(4), 586; https://doi.org/10.3390/e25040586 - 29 Mar 2023
Viewed by 463
Abstract
By using the residual source redundancy to achieve the shaping gain, a joint source-channel coded modulation (JSCCM) system has been proposed as a new solution for probabilistic amplitude shaping (PAS). However, the source and channel codes in the JSCCM system should be designed [...] Read more.
By using the residual source redundancy to achieve the shaping gain, a joint source-channel coded modulation (JSCCM) system has been proposed as a new solution for probabilistic amplitude shaping (PAS). However, the source and channel codes in the JSCCM system should be designed specifically for a given source probability to ensure optimal PAS performance, which is undesirable for systems with dynamically changing source probabilities. In this paper, we propose a new shaping scheme by optimizing the bit-labeling of the JSCCM system. Instead of the conventional fixed labeling, the proposed bit-labelings are adaptively designed according to the source probability and the source code. Since it is simple to switch between different labelings according to the source probability and the source code, the proposed design can be considered as a promising low complexity alternative to obtain the shaping gain for sources with different probabilities. Numerical results show that the proposed bit-labelings can significantly improve the bit-error rate (BER) performance of the JSCCM system. Full article
(This article belongs to the Special Issue Coding and Entropy)
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Article
Design and Analysis of Joint Group Shuffled Scheduling Decoding Algorithm for Double LDPC Codes System
Entropy 2023, 25(2), 357; https://doi.org/10.3390/e25020357 - 15 Feb 2023
Cited by 1 | Viewed by 601
Abstract
In this paper, a joint group shuffled scheduling decoding (JGSSD) algorithm for a joint source-channel coding (JSCC) scheme based on double low-density parity-check (D-LDPC) codes is presented. The proposed algorithm considers the D-LDPC coding structure as a whole and applies shuffled scheduling to [...] Read more.
In this paper, a joint group shuffled scheduling decoding (JGSSD) algorithm for a joint source-channel coding (JSCC) scheme based on double low-density parity-check (D-LDPC) codes is presented. The proposed algorithm considers the D-LDPC coding structure as a whole and applies shuffled scheduling to each group; the grouping relies on the types or the length of the variable nodes (VNs). By comparison, the conventional shuffled scheduling decoding algorithm can be regarded as a special case of this proposed algorithm. A novel joint extrinsic information transfer (JEXIT) algorithm for the D-LDPC codes system with the JGSSD algorithm is proposed, by which the source and channel decoding are calculated with different grouping strategies to analyze the effects of the grouping strategy. Simulation results and comparisons verify the superiority of the JGSSD algorithm, which can adaptively trade off the decoding performance, complexity and latency. Full article
(This article belongs to the Special Issue Coding and Entropy)
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