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Entropy
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Intelligent Information Processing and Coding for B5G Communications
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Intelligent Information Processing and Coding for B5G Communications

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 6401

Special Issue Editors

Prof. Dr. Pingyi Fan

E-Mail Website
Guest Editor
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
Prof. Dr. Zhanjie Song

E-Mail Website
Guest Editor
School of Mathematics, Tianjin University, Tianjin 300354, China
Interests: sampling; approximation and reconstruction of random signals; statistical analysis of random processes; and big date processing on random fields
Dr. Qi Chen

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: Shannon theory; information inequalities and entropy region; network coding
Special Issues, Collections and Topics in MDPI journals
Dr. Suihua Cai

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Interests: information theory; channel coding and its applications
Special Issues, Collections and Topics in MDPI journals
Gangtao Xin

E-Mail
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,

With the rapid development of communication technologies, Beyond 5G (B5G) communications have emerged as the next frontier in wireless communication systems. B5G aims to provide ultra-reliable and low-latency communications, massive AI-aided communication, and unprecedented levels of connectivity. One of the key pillars in achieving these goals is intelligent information processing and coding. With immense excitement, we present our Special Issue, which is focused on gathering cutting-edge research on intelligent techniques for information processing and coding in the context of B5G communications.

This Special Issue aims to provide a platform for researchers, scholars, and practitioners to showcase their latest research findings and innovations in the field of intelligent information processing and coding for B5G communications. We invite contributions that cover a wide range of topics including, but not limited to, the following:

  • Next-generation communication technology;
  • Machine learning and deep learning in wireless communications;
  • Satellite and space communications;
  • Novel coding schemes for B5G;
  • Intelligent data compression and processing techniques;
  • Applications of artificial intelligence in B5G networks.

Prof. Dr. Pingyi Fan
Prof. Dr. Zhanjie Song
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 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

  • B5G
  • information theory
  • 6G
  • wireless communications
  • coding techniques
  • information-theoretic methods
  • large model
  • machine learning
  • semantic information processing

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

Published Papers (3 papers)

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Research

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26 pages, 728 KiB  
Article
Adaptive Privacy-Preserving Coded Computing with Hierarchical Task Partitioning
by Qicheng Zeng, Zhaojun Nan and Sheng Zhou
Entropy 2024, 26(10), 881; https://doi.org/10.3390/e26100881 - 21 Oct 2024
Viewed by 544
Abstract
Coded computing is recognized as a promising solution to address the privacy leakage problem and the straggling effect in distributed computing. This technique leverages coding theory to recover computation tasks using results from a subset of workers. In this paper, we propose the [...] Read more.
Coded computing is recognized as a promising solution to address the privacy leakage problem and the straggling effect in distributed computing. This technique leverages coding theory to recover computation tasks using results from a subset of workers. In this paper, we propose the adaptive privacy-preserving coded computing (APCC) strategy, designed to be applicable to various types of computation tasks, including polynomial and non-polynomial functions, and to adaptively provide accurate or approximated results. We prove the optimality of APCC in terms of encoding rate, defined as the ratio between the computation loads of tasks before and after encoding, based on the optimal recovery threshold of Lagrange Coded Computing. We demonstrate that APCC guarantees information-theoretical data privacy preservation. Mitigation of the straggling effect in APCC is achieved through hierarchical task partitioning and task cancellation, which further reduces computation delays by enabling straggling workers to return partial results of assigned tasks, compared to conventional coded computing strategies. The hierarchical task partitioning problems are formulated as mixed-integer nonlinear programming (MINLP) problems with the objective of minimizing task completion delay. We propose a low-complexity maximum value descent (MVD) algorithm to optimally solve these problems. The simulation results show that APCC can reduce the task completion delay by a range of 20.3% to 47.5% when compared to other state-of-the-art benchmarks. Full article
(This article belongs to the Special Issue Intelligent Information Processing and Coding for B5G Communications)
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11 pages, 3522 KiB  
Article
High-Throughput Polar Code Decoders with Information Bottleneck Quantization
by Claus Kestel, Lucas Johannsen and Norbert Wehn
Entropy 2024, 26(6), 462; https://doi.org/10.3390/e26060462 - 28 May 2024
Viewed by 715
Abstract
In digital baseband processing, the forward error correction (FEC) unit belongs to the most demanding components in terms of computational complexity and power consumption. Hence, efficient implementation of FEC decoders is crucial for next-generation mobile broadband standards and an ongoing research topic. Quantization [...] Read more.
In digital baseband processing, the forward error correction (FEC) unit belongs to the most demanding components in terms of computational complexity and power consumption. Hence, efficient implementation of FEC decoders is crucial for next-generation mobile broadband standards and an ongoing research topic. Quantization has a significant impact on the decoder area, power consumption and throughput. Thus, lower bit widths are preferred for efficient implementations but degrade the error correction capability. To address this issue, a non-uniform quantization based on the Information Bottleneck (IB) method is proposed that enables a low bit width while maintaining the essential information. Many investigations on the use of the IB method for Low-density parity-check code) LDPC decoders exist and have shown its advantages from an implementation perspective. However, for polar code decoder implementations, there exists only one publication that is not based on the state-of-the-art Fast Simplified Successive-Cancellation (Fast-SSC) decoding algorithm, and only synthesis implementation results without energy estimation are shown. In contrast, our paper presents several optimized Fast-SSC polar code decoder implementations using IB-based quantization with placement and routing results using advanced 12 nm FinFET technology. Gains of up to 16% in area and 13% in energy efficiency are achieved with IB-based quantization at a Frame Error Rate (FER) of 10−7 and a polar code of N=1024,R=0.5 compared to state-of-the-art decoders. Full article
(This article belongs to the Special Issue Intelligent Information Processing and Coding for B5G Communications)
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Review

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29 pages, 1445 KiB  
Review
Semantic Communication: A Survey of Its Theoretical Development
by Gangtao Xin, Pingyi Fan and Khaled B. Letaief
Entropy 2024, 26(2), 102; https://doi.org/10.3390/e26020102 - 24 Jan 2024
Cited by 2 | Viewed by 4664
Abstract
In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be [...] Read more.
In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory. Full article
(This article belongs to the Special Issue Intelligent Information Processing and Coding for B5G Communications)
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