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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 October 2025 | Viewed by 3430

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Special Issue Editors

Prof. Dr. Yanlin Geng

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Guest Editor

State Key Laboratory of ISN, Xidian University, Xi’an, China
Interests: broadcast channel; capacity region; Gaussian noise; information inequality; Blahut–Arimoto algorithm
Special Issues, Collections and Topics in MDPI journals

Dr. Youlong Wu

E-Mail Website
Guest Editor

School of Information Science and Technology, Shanghai Tech University, Shanghai, China
Interests: coded caching; distributed computing; federated learning; joint source–channel coding; communication reliability
Special Issues, Collections and Topics in MDPI journals

Dr. Ling Liu

E-Mail Website
Guest Editor

Guangzhou Institute of Technology, Xidian University, Guangzhou, China
Interests: channel coding; source coding; network information theory; lattice theory; cryptography

Special Issue Information

Dear Colleagues,

With the development of information technologies in fields such as communication and artificial intelligence, scenarios involving multiple users and even complex networks dominate scientific research. For the core performance metrics of these scenarios, it is urgent to study their fundamental limits, namely network information theory. The research progress will also in turn promote technological advancements in practical applications.

In this Special Issue, we focus on (but are not limited to) characterizing the fundamental limits of core performance metrics related to multiuser and even networked scenarios, and designing schemes to approach these limits. According to different scenarios, these metrics include channel capacity, latency, reliability, complexity, secrecy, etc.

This Special Issue will accept unpublished original papers and comprehensive reviews focused on network information theory and its application.

Prof. Dr. Yanlin Geng
Dr. Youlong Wu
Dr. Ling Liu
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.

Keywords

network information theory
capacity region
Gaussian noise
information inequality
distributed system
federated learning
channel coding
source coding
randomness generation
integrated sensing and communication
coded caching and computing
communication for computing
network coding

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Published Papers (5 papers)

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Research

13 pages, 528 KiB

Open AccessArticle

On Vector Random Linear Network Coding in Wireless Broadcasts

by Rina Su, Chengji Zhao, Qifu Sun and Zhongshan Zhang

Entropy 2025, 27(6), 559; https://doi.org/10.3390/e27060559 - 26 May 2025

Viewed by 184

Abstract

Compared with scalar linear network coding (LNC) formulated over the finite field GF(

2^{L}

), vector LNC offers enhanced flexibility in the code design by enabling linear operations over the vector space

GF {(2)}^{L}

and demonstrates a number of [...] Read more.

Compared with scalar linear network coding (LNC) formulated over the finite field GF(

2^{L}

), vector LNC offers enhanced flexibility in the code design by enabling linear operations over the vector space

GF {(2)}^{L}

and demonstrates a number of advantages over scalar LNC. While random LNC (RLNC) has shown significant potential to improve the completion delay performance in wireless broadcasts, most prior studies focus on scalar RLNC. In particular, it is well known that, with increasing L, primitive scalar RLNC over GF(

2^{L}

) asymptotically achieves the optimal completion delay. However, the completion delay performance of primitive vector RLNC remains unexplored. This work aims to fill in this blank. We derive closed-form expressions for the probability distribution and the expected value of both the completion delay at a single receiver and the system completion delay. We further unveil a fundamental limitation that is different from scalar RLNC: even for large enough L, primitive vector RLNC over

GF {(2)}^{L}

inherently fails to reach optimal completion delay. In spite of this, the gap between the expected completion delay at a receiver and the optimal one is shown to be a constant smaller than

0.714

, which implies that the expected completion delay normalized by the number P of original packets is asymptotically optimal with increasing P. We also validate our theoretical characterization through numerical simulations. Our theoretical characterization establishes primitive vector RLNC as a performance baseline for the future design of practical vector RLNC schemes with different design goals. Full article

(This article belongs to the Special Issue Network Information Theory and Its Applications)

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15 pages, 548 KiB

Open AccessArticle

Centralized Hierarchical Coded Caching Scheme for Two-Layer Network

by Kun Zhao, Jinyu Wang and Minquan Cheng

Entropy 2025, 27(3), 316; https://doi.org/10.3390/e27030316 - 18 Mar 2025

Viewed by 334

Abstract

This paper considers a two-layer hierarchical network, where a server containing N files is connected to

K_{1}

mirrors and each mirror is connected to

K_{2}

users. Each mirror and each user has a cache memory of size

M_{1}

and [...] Read more.

This paper considers a two-layer hierarchical network, where a server containing N files is connected to

K_{1}

mirrors and each mirror is connected to

K_{2}

users. Each mirror and each user has a cache memory of size

M_{1}

and

M_{2}

files, respectively. The server can only broadcast to the mirrors, and each mirror can only broadcast to its connected users. For such a network, we propose a novel coded caching scheme based on two known placement delivery arrays (PDAs). To fully utilize the cache memory of both the mirrors and users, we first treat the mirrors and users as cache nodes of the same type; i.e., the cache memory of each mirror is regarded as an additional part of the connected users’ cache, then the server broadcasts messages to all mirrors according to a

K_{1} K_{2}

-user PDA in the first layer. In the second layer, each mirror first cancels useless file packets (if any) in the received useful messages and forwards them to the connected users, such that each user can decode the requested packets not cached by the mirror, then broadcasts coded subpackets to the connected users according to a

K_{2}

-user PDA, such that each user can decode the requested packets cached by the mirror. The proposed scheme is extended to a heterogeneous two-layer hierarchical network, where the number of users connected to different mirrors may be different. Numerical comparison showed that the proposed scheme achieved lower coding delays compared to existing hierarchical coded caching schemes at most memory ratio points. Full article

(This article belongs to the Special Issue Network Information Theory and Its Applications)

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19 pages, 1196 KiB

Open AccessArticle

Clustered Distributed Data Storage Repairing Multiple Failures

by Shiqiu Liu, Fangwei Ye and Qihui Wu

Entropy 2025, 27(3), 313; https://doi.org/10.3390/e27030313 - 17 Mar 2025

Viewed by 330

Abstract

A clustered distributed storage system (DSS), also called a rack-aware storage system, is a distributed storage system in which the nodes are grouped into several clusters. The communication between two clusters may be restricted by their connectivity; that is to say, the communication cost between nodes differs depending on their location. As such, when repairing a failed node, downloading data from nodes that are in the same cluster is much cheaper and more efficient than downloading data from nodes in another cluster. In this article, we consider a scenario in which the failed nodes only download data from nodes in the same cluster, which is an extreme and important case that leverages the fact that the intra-cluster bandwidth is much cheaper than the cross-cluster repair bandwidth. Also, we study the problem of repairing multiple failures in this article, which allows for collaboration within the same cluster, i.e., failed nodes in the same cluster can exchange data with each other. We derive the trade-off between the storage and repair bandwidth for the clustered DSSs and provide explicit code constructions achieving two extreme points in the trade-off, namely the minimum storage clustered collaborative repair (MSCCR) point and the minimum bandwidth clustered collaborative repair (MBCCR) point, respectively. Full article

(This article belongs to the Special Issue Network Information Theory and Its Applications)

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12 pages, 284 KiB

Open AccessArticle

Coded Distributed Computing Under Combination Networks

by Yongcheng Yang, Yifei Huang, Xiaohuan Qin and Shenglian Lu

Entropy 2025, 27(3), 311; https://doi.org/10.3390/e27030311 - 16 Mar 2025

Viewed by 501

Abstract

Coded distributed computing (CDC) is a powerful approach to reduce the communication overhead in distributed computing frameworks by utilizing coding techniques. In this paper, we focus on the CDC problem in

(H, L)

-combination networks, where H APs act as [...] Read more.

(H, L)

-combination networks, where H APs act as intermediate pivots and

K = (\binom{H}{L})

workers are connected to different subsets of L APs. Each worker processes a subset of the input file and computes intermediate values (IVs) locally, which are then exchanged via uplink and downlink transmissions through the AP station to ensure that all workers compute their assigned output functions. In this paper, we first novelly characterize the transmission scheme for the shuffle phase from the view point of the coefficient matrix and then obtain the scheme by using the Combined Placement Delivery Array (CPDA). Compared with the baseline scheme, our scheme significantly improves the uplink and downlink communication loads while maintaining the robustness and efficiency of the combined multi-AP network. Full article

(This article belongs to the Special Issue Network Information Theory and Its Applications)

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12 pages, 834 KiB

Open AccessArticle

A Post-Processing Method for Quantum Random Number Generator Based on Zero-Phase Component Analysis Whitening

by Longju Liu, Jie Yang, Mei Wu, Jinlu Liu, Wei Huang, Yang Li and Bingjie Xu

Entropy 2025, 27(1), 68; https://doi.org/10.3390/e27010068 - 14 Jan 2025

Cited by 2 | Viewed by 1478

Abstract

Quantum Random Number Generators (QRNGs) have been theoretically proven to be able to generate completely unpredictable random sequences, and have important applications in many fields. However, the practical implementation of QRNG is always susceptible to the unwanted classical noise or device imperfections, which inevitably diminishes the quality of the generated random bits. It is necessary to perform the post-processing to extract the true quantum randomness contained in raw data generated by the entropy source of QRNG. In this work, a novel post-processing method for QRNG based on Zero-phase Component Analysis (ZCA) whitening is proposed and experimentally verified through both time and spectral domain analysis, which can effectively reduce auto-correlations and flatten the spectrum of the raw data, and enhance the random number generation rate of QRNG. Furthermore, the randomness extraction is performed after ZCA whitening, after which the final random bits can pass the NIST test. Full article

(This article belongs to the Special Issue Network Information Theory and Its Applications)

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