Special Issue "Information Theory for Communication Systems"

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

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Tobias Koch
Website
Guest Editor
Signal Theory and Communications Department, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, 28911 Leganés, Spain
Interests: fading channels; information theory at finite blocklength; quantization and sampling; rate–distortion theory
Dr. Stefan M. Moser
Website
Guest Editor
ETH Zurich, Switzerland
Interests: optical communication; molecular communication; performance analysis of communication systems; connections between biology and information theory

Special Issue Information

Dear Collegaues,

The founding work of the field of information theory, Claude Shannon's 1948 article "A mathematical theory of communication", concerns the fundamental limits of communication systems. It is, therefore, not surprising that, since its origins, information theory has been very successful in providing performance benchmarks and design guidelines for numerous communication scenarios. This Special Issue aims to bring together recent research efforts that apply information theory to characterize and study the fundamental limits of communication systems. Possible topics include, but are not limited to the following:

  • Asymptotic performance characterizations, such as channel capacity, second-order rates, or error exponents, of communication channels
  • Nonasymptotic performance bounds for communication systems
  • Information-theoretic limits of delay- and energy-limited communication systems
  • Information-theoretic analyses of signal constellations and low-precision decoders
  • Error-correcting codes for communication systems

Dr. Tobias Koch
Dr. Stefan M. Moser
Guest Editors

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 papers will be 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 1600 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

  • channel capacity
  • communication systems
  • energy-limited communications
  • error-correcting codes
  • error exponents
  • low-latency communications
  • low-precision decoders
  • performance bounds
  • second-order rates
  • signal constellations

Published Papers (2 papers)

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Research

Open AccessFeature PaperArticle
Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
Entropy 2020, 22(5), 581; https://doi.org/10.3390/e22050581 - 21 May 2020
Abstract
In this paper, we provide a systematic comparison of distribution matching (DM) and sphere shaping (SpSh) algorithms for short blocklength probabilistic amplitude shaping. For asymptotically large blocklengths, constant composition distribution matching (CCDM) is known to generate the target capacity-achieving distribution. However, as the [...] Read more.
In this paper, we provide a systematic comparison of distribution matching (DM) and sphere shaping (SpSh) algorithms for short blocklength probabilistic amplitude shaping. For asymptotically large blocklengths, constant composition distribution matching (CCDM) is known to generate the target capacity-achieving distribution. However, as the blocklength decreases, the resulting rate loss diminishes the efficiency of CCDM. We claim that for such short blocklengths over the additive white Gaussian noise (AWGN) channel, the objective of shaping should be reformulated as obtaining the most energy-efficient signal space for a given rate (rather than matching distributions). In light of this interpretation, multiset-partition DM (MPDM) and SpSh are reviewed as energy-efficient shaping techniques. Numerical results show that both have smaller rate losses than CCDM. SpSh—whose sole objective is to maximize the energy efficiency—is shown to have the minimum rate loss amongst all, which is particularly apparent for ultra short blocklengths. We provide simulation results of the end-to-end decoding performance showing that up to 1 dB improvement in power efficiency over uniform signaling can be obtained with MPDM and SpSh at blocklengths around 200. Finally, we present a discussion on the complexity of these algorithms from the perspectives of latency, storage and computations. Full article
(This article belongs to the Special Issue Information Theory for Communication Systems)
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Open AccessArticle
On Training Neural Network Decoders of Rate Compatible Polar Codes via Transfer Learning
Entropy 2020, 22(5), 496; https://doi.org/10.3390/e22050496 - 25 Apr 2020
Abstract
Neural network decoders (NNDs) for rate-compatible polar codes are studied in this paper. We consider a family of rate-compatible polar codes which are constructed from a single polar coding sequence as defined by 5G new radios. We propose a transfer learning technique for [...] Read more.
Neural network decoders (NNDs) for rate-compatible polar codes are studied in this paper. We consider a family of rate-compatible polar codes which are constructed from a single polar coding sequence as defined by 5G new radios. We propose a transfer learning technique for training multiple NNDs of the rate-compatible polar codes utilizing their inclusion property. The trained NND for a low rate code is taken as the initial state of NND training for the next smallest rate code. The proposed method provides quicker training as compared to separate learning of the NNDs according to numerical results. We additionally show that an underfitting problem of NND training due to low model complexity can be solved by transfer learning techniques. Full article
(This article belongs to the Special Issue Information Theory for Communication Systems)
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