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Wireless Networks: Information Theoretic Perspectives III

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 (20 September 2023) | Viewed by 4097

Special Issue Editors

Department of Electrical Engineering, New Jersey Institute of Technology (NJIT), Newark, NJ 07102, USA
Interests: multiuser information theory and estimation theory and their applications in wireless networks
Special Issues, Collections and Topics in MDPI journals
Department of Electronics, Polytechnic University of Milan, 20133 Milan, Italy
Interests: information theory and coding theory with applications to fiber-optic and wireless communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Network information theory is a framework for studying performance limits in communications over networks; as such, it is expected to continue to play an essential role in the future development of wireless networks, including 5G and beyond. This Special Issue aims to bring together the body of recent results in network information theory in order to bolster its value and emphasize its continued importance in the development of wireless communications. Previously unpublished contributions in the intersection network information theory and wireless networks are solicited, including (but not limited to) the following:

  • Emerging information theoretic models for wireless communications;
  • Gaussian networks;
  • Capacity scaling laws;
  • Massive networks;
  • Random access;
  • Interference mitigation schemes;
  • Relaying techniques;
  • MIMO channels;
  • Massive MIMO;
  • Low-latency communications;
  • Secure and private communications;
  • Low power communications;
  • Code design for networks;
  • Interactive communications and feedback;
  • Communication under channel uncertainty;
  • Mismatched network capacity;
  • Cloud and fog radio access networks;
  • Caching for wireless communications.

Dr. Alex Dytso
Dr. Luca Barletta
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 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.

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Published Papers (3 papers)

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Research

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35 pages, 818 KiB  
Article
Amplitude Constrained Vector Gaussian Wiretap Channel: Properties of the Secrecy-Capacity-Achieving Input Distribution
by Antonino Favano, Luca Barletta and Alex Dytso
Entropy 2023, 25(5), 741; https://doi.org/10.3390/e25050741 - 30 Apr 2023
Cited by 1 | Viewed by 1502
Abstract
This paper studies the secrecy capacity of an n-dimensional Gaussian wiretap channel under a peak power constraint. This work determines the largest peak power constraint R¯n, such that an input distribution uniformly distributed on a single sphere is optimal; [...] Read more.
This paper studies the secrecy capacity of an n-dimensional Gaussian wiretap channel under a peak power constraint. This work determines the largest peak power constraint R¯n, such that an input distribution uniformly distributed on a single sphere is optimal; this regime is termed the low-amplitude regime. The asymptotic value of R¯n as n goes to infinity is completely characterized as a function of noise variance at both receivers. Moreover, the secrecy capacity is also characterized in a form amenable to computation. Several numerical examples are provided, such as the example of the secrecy-capacity-achieving distribution beyond the low-amplitude regime. Furthermore, for the scalar case (n=1), we show that the secrecy-capacity-achieving input distribution is discrete with finitely many points at most at the order of R2σ12, where σ12 is the variance of the Gaussian noise over the legitimate channel. Full article
(This article belongs to the Special Issue Wireless Networks: Information Theoretic Perspectives III)
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86 pages, 1383 KiB  
Article
Information Rates for Channels with Fading, Side Information and Adaptive Codewords
by Gerhard Kramer
Entropy 2023, 25(5), 728; https://doi.org/10.3390/e25050728 - 27 Apr 2023
Cited by 2 | Viewed by 1237
Abstract
Generalized mutual information (GMI) is used to compute achievable rates for fading channels with various types of channel state information at the transmitter (CSIT) and receiver (CSIR). The GMI is based on variations of auxiliary channel models with additive white Gaussian noise (AWGN) [...] Read more.
Generalized mutual information (GMI) is used to compute achievable rates for fading channels with various types of channel state information at the transmitter (CSIT) and receiver (CSIR). The GMI is based on variations of auxiliary channel models with additive white Gaussian noise (AWGN) and circularly-symmetric complex Gaussian inputs. One variation uses reverse channel models with minimum mean square error (MMSE) estimates that give the largest rates but are challenging to optimize. A second variation uses forward channel models with linear MMSE estimates that are easier to optimize. Both model classes are applied to channels where the receiver is unaware of the CSIT and for which adaptive codewords achieve capacity. The forward model inputs are chosen as linear functions of the adaptive codeword’s entries to simplify the analysis. For scalar channels, the maximum GMI is then achieved by a conventional codebook, where the amplitude and phase of each channel symbol are modified based on the CSIT. The GMI increases by partitioning the channel output alphabet and using a different auxiliary model for each partition subset. The partitioning also helps to determine the capacity scaling at high and low signal-to-noise ratios. A class of power control policies is described for partial CSIR, including a MMSE policy for full CSIT. Several examples of fading channels with AWGN illustrate the theory, focusing on on-off fading and Rayleigh fading. The capacity results generalize to block fading channels with in-block feedback, including capacity expressions in terms of mutual and directed information. Full article
(This article belongs to the Special Issue Wireless Networks: Information Theoretic Perspectives III)
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Review

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31 pages, 1021 KiB  
Review
Overview of Tensor-Based Cooperative MIMO Communication Systems—Part 1: Tensor Modeling
by Gérard Favier and Danilo Sousa Rocha
Entropy 2023, 25(8), 1181; https://doi.org/10.3390/e25081181 - 08 Aug 2023
Cited by 1 | Viewed by 872
Abstract
Due to increasingly strong and varied performance requirements, cooperative wireless communication systems today occupy a prominent place in both academic research and industrial development. The technological and economic challenges for future sixth-generation (6G) wireless systems are considerable, with the objectives of improving coverage, [...] Read more.
Due to increasingly strong and varied performance requirements, cooperative wireless communication systems today occupy a prominent place in both academic research and industrial development. The technological and economic challenges for future sixth-generation (6G) wireless systems are considerable, with the objectives of improving coverage, data rate, latency, reliability, mobile connectivity and energy efficiency. Over the past decade, new technologies have emerged, such as massive multiple-input multiple-output (MIMO) relay systems, intelligent reflecting surfaces (IRS), unmanned aerial vehicular (UAV)-assisted communications, dual-polarized (DP) antenna arrays, three dimensional (3D) polarized channel modeling, and millimeter-wave (mmW) communication. The objective of this paper is to provide an overview of tensor-based MIMO cooperative communication systems. Indeed, during the last two decades, tensors have been the subject of many applications in signal processing, especially for digital communications, and more broadly for big data processing. After a brief reminder of basic tensor operations and decompositions, we present the main characteristics allowing to classify cooperative systems, illustrated by means of different architectures. A review of main codings used for cooperative systems is provided before a didactic and comprehensive presentation of two-hop systems, highlighting different tensor models. In a companion paper currently in preparation, we will show how these tensor models can be exploited to develop semi-blind receivers to jointly estimate transmitted information symbols and communication channels. Full article
(This article belongs to the Special Issue Wireless Networks: Information Theoretic Perspectives III)
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