Submit to Entropy Review for Entropy Propose a Special Issue

Journal Menu

Journal Browser

Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday

Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 May 2026) | Viewed by 3608

Share This Special Issue

Editors

Prof. Dr. Onur Günlü

E-Mail Website
Guest Editor

Institute of Communications Engineering, TU Dortmund University, 44227 Dortmund, Germany
Interests: information theoretic privacy and security; coding theory; private learning; secure function computation; physical layer security
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Holger Boche

E-Mail Website
Guest Editor

Institute of Theoretical Information Technology, Technical University of Munich, 80333 Munich, Germany
Interests: information theory; signal processing; communication theory
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Giuseppe Caire

E-Mail Website
Guest Editor

Electrical Engineering and Computer Science Department, Technische Universität Berlin, Berlin, Germany
Interests: communications theory; information theory; channel and source coding; wireless communications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shlomo Shamai (Shitz)

E-Mail Website
Guest Editor

Faculty of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel
Interests: multi-user information theory; modern communication networks (cloud and fog radio networks); information and signal processing (information–estimation); information bottleneck problems in communications and learning; sparse communications models and non-orthogonal (NOMA) systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Osvaldo Simeone

E-Mail Website
Guest Editor

King’s Communications, Learning and Information Processing Laboratory, Department of Engineering, King’s College London, London WC2R 2LS, UK
Interests: information theory; wireless communications; machine learning

Special Issue Information

Dear Colleagues,

Professor H. Vincent Poor’s transformative contributions have fundamentally shaped modern information theory, wireless communications, and signal processing. His pioneering research spans foundational theoretical frameworks, from network information theory and information–theoretic security to the unification of estimation theory and information–theoretic principles. His insights have revolutionized wireless communications, cellular architectures, cooperative networks, and the analysis of feedback systems, while his explorations of MIMO systems, fading channels, and cross-disciplinary frameworks continue to inspire breakthroughs at the intersection of theory and practice.

This Special Issue—on the occasion of Prof. Poor’s 75th birthday—will collect invitation-only high-quality papers exploring topics central to Prof. Poor’s legacy, including the following:

Information–theoretic security;
Network information theory;
Wireless systems;
Statistical signal processing;
Emerging intersections (such as smart grids, network security, efficient machine learning for engineering);
Unifying frameworks (such as information–theoretic perspectives on estimation, sensing, and communications).

Submissions should demonstrate explicit connections to information theory. We particularly seek high-quality tutorial/review papers synthesizing recent advances in the above areas, shaped also by Prof. Poor’s fundamental results. Original contributions presenting exceptionally significant novel results are also welcome.

Prof. Dr. Onur Günlü
Prof. Dr. Holger Boche
Prof. Dr. Giuseppe Caire
Prof. Dr. Shlomo Shamai (Shitz)
Prof. Dr. Osvaldo Simeone
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

information theory
wireless networks
network security
statistical signal processing
efficient machine learning for engineering

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

33 pages, 619 KB

Open AccessArticle

Polar Codes for Decomposed Multi-Input Multi-Output Gaussian Broadcast Channels

by Muhammed Yusuf Şener, Gerhard Kramer, Shlomo Shamai (Shitz), Ronald Böhnke and Wen Xu

Entropy 2026, 28(7), 798; https://doi.org/10.3390/e28070798 - 14 Jul 2026

Viewed by 195

Abstract

Dirty paper coding (DPC) is applied to multi-input multi-output (MIMO) broadcast channels with additive Gaussian noise and one message per receiver. The method decomposes each receiver MIMO channel into parallel scalar channels and applies modulo operators, amplitude-shift keying (ASK), and probabilistic shaping. The achievable rate tuples include all points inside the capacity region by choosing truncated Gaussian shaping, large ASK alphabets, and large modulo intervals. Simulations with short polar codes show significant rate and power gains from DPC compared to linear precoding, while maintaining similar encoding and decoding complexities. Full article

(This article belongs to the Special Issue Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday)

► Show Figures

Figure 1

19 pages, 754 KB

Open AccessArticle

Probabilistic Forecasting and Information-Theoretic Analysis of Multivariate fMRI Dynamics

by Arda Bayer, Zhiyao Zhang, Ahmet Emre Ipek, Rose Khavari and Behnaam Aazhang

Entropy 2026, 28(7), 738; https://doi.org/10.3390/e28070738 - 1 Jul 2026

Viewed by 258

Abstract

Functional magnetic resonance imaging (fMRI) signals exhibit complex temporal structure arising from multivariate neural dynamics, physiological variability, and measurement uncertainty. In this work, we formulate region-of-interest-level fMRI analysis as a probabilistic multi-step forecasting problem and investigate the predictability of blood-oxygen-level-dependent (BOLD) activity from an information-theoretic perspective. Using the Natural Scenes Dataset, we model multiregional BOLD activity as a stochastic process with finite memory and train multiple forecasting architectures, including linear regression, exponential smoothing, recurrent neural networks, and transformer-based models, to predict future BOLD samples from preceding temporal observations. Forecasting performance is analyzed together with entropy-based quantities, including marginal entropy, conditional entropy, and normalized predictive information measures estimated directly from model-derived predictive distributions without imposing restrictive Gaussian assumptions on the underlying BOLD dynamics. The transformer model achieved significant improvement over a naive persistence baseline (

p = 0.001

) while yielding a high predictive information fraction (

η = 75.49 %

). Post hoc directed information analysis revealed that short-horizon prediction was dominated primarily by autoregressive, within-ROI, temporal structure. Overall, the proposed framework demonstrates how probabilistic forecasting and information-theoretic analysis can be integrated to characterize the predictability, uncertainty structure, and directional organization of large-scale fMRI dynamics and may support future downstream neuroengineering and neural-state inference applications. Full article

(This article belongs to the Special Issue Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday)

► Show Figures

Figure 1

29 pages, 397 KB

Open AccessFeature PaperArticle

Convergence Guarantees for Time-Inhomogeneous Uniform-Rate Discrete Diffusion Models

by Yuchen Liang, Lifeng Lai, Ness Shroff and Yingbin Liang

Entropy 2026, 28(6), 675; https://doi.org/10.3390/e28060675 - 11 Jun 2026

Viewed by 269

Abstract

Discrete diffusion models have become an important class of generative models for categorical data, yet their theoretical understanding remains largely limited to time-homogeneous noise schedules. In this work, we study uniform-rate discrete diffusion models with time-inhomogeneous continuous-time Markov chain forward processes. We establish convergence guarantees for practical reverse-time samplers by directly controlling the total variation distance, avoiding the indirect route of first bounding KL divergence and then applying Pinsker’s inequality. Our analysis decomposes the sampling error into initialization, score-estimation, discretization, and early-stopping errors, and explicitly characterizes how each term depends on the accumulated noise, the local noise rate, and the smoothness of the noise schedule. Under suitable regularity conditions on the noise schedule, we further derive step-complexity guarantees that match the order of existing results for homogeneous samplers. Full article

(This article belongs to the Special Issue Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday)

17 pages, 337 KB

Open AccessFeature PaperArticle

Support Size of ε-Capacity-Achieving Inputs for the Amplitude-Constrained AWGN Channel

by Luca Barletta and Alex Dytso

Entropy 2026, 28(5), 500; https://doi.org/10.3390/e28050500 - 28 Apr 2026

Viewed by 481

Abstract

We study the discrete-time amplitude-constrained additive white Gaussian noise (AWGN) channel from the perspective of near-optimal input distributions in the high-SNR, or equivalently large-amplitude, regime. While it is known that the capacity-achieving input is discrete with finitely many mass points, the precise scaling of its support size as a function of the amplitude constraint remains an open problem. In this work, we instead consider the minimal support size required to achieve capacity up to an

ε

-gap. We introduce the quantity

K_{ε} (A)

, defined as the smallest support size among discrete inputs supported on

[- A, A]

that achieves mutual information within

ε

of capacity. We show that this relaxed formulation is significantly more tractable and admits sharp characterizations in several vanishing-gap regimes. In particular, for polynomially decaying gaps,

ε = A^{- β}

with

β \geq 1

, we establish that

K_{ε} (A) = Θ (A \sqrt{log A})

A \to \infty

. For exponentially small gaps, we obtain bounds of order between

A \sqrt{log A}

and

A^{3 / 2}

. Our approach combines approximation-theoretic bounds for Gaussian mixtures with information-theoretic control of entropy via

χ^{2}

-divergence, together with a wrapping argument that relates the problem to approximating the uniform distribution on a circle. Beyond the technical results, our framework provides a conceptual explanation for the variety of scaling laws observed in prior numerical studies, suggesting that these may correspond to different regimes of

ε

-optimality rather than intrinsic properties of the exact optimizer. Full article

(This article belongs to the Special Issue Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday)

20 pages, 1252 KB

Open AccessFeature PaperArticle

Tail-Latency-Aware Federated Learning with Pinching Antenna: Latency, Participation, and Placement

by Yushen Lin and Zhiguo Ding

Entropy 2026, 28(3), 341; https://doi.org/10.3390/e28030341 - 18 Mar 2026

Cited by 1 | Viewed by 520

Abstract

Straggler synchronization is a dominant wall-clock bottleneck in synchronous wireless federated learning (FL). Under non-IID data, however, aggressively sampling only fast clients may significantly slow convergence due to statistical heterogeneity. This paper studies PASS-enabled FL, where a radiating pinching antenna (PA) can be activated at an arbitrary position along a dielectric waveguide to reshape uplink latencies. We consider a joint optimization of PA placement and client participation to minimize a proxy for time-to-accuracy, coupling the exact expected maximum round latency via order statistics with a heterogeneity-aware statistical-efficiency proxy. We derive first-order optimality conditions that reveal an explicit tail-latency premium in the KKT recursion, quantifying how latency gaps are amplified by maximum-order-statistic synchronization. Under a latency-class structure, we obtain a within-class square-root sampling law and establish a two-class phase transition where slow-class participation collapses under an explicit heterogeneity-threshold condition as the per-round sample size grows. For PA placement, we prove a piecewise envelope-derivative characterization and provide an exact breakpoint-and-root candidate-enumeration procedure. Simulation results validate the structural findings and show that PASS enables more eligible participation, yielding higher wall-clock accuracy. Full article

(This article belongs to the Special Issue Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday)

► Show Figures

Figure 1

Review

Jump to: Research

31 pages, 933 KB

Open AccessReview

A Framework for Characterization of Optimal Decision Rules in Hypothesis-Testing Problems

by Emre Efendi, Berkan Dulek, Sinan Gezici and Yanglei Song

Entropy 2026, 28(6), 657; https://doi.org/10.3390/e28060657 - 9 Jun 2026

Viewed by 256

Abstract

In this review paper, we present a framework for the characterization of optimal decision rules in M-ary hypothesis-testing problems where the performance metric is defined as a function of pairwise error probabilities. This framework is based on the approaches developed in several recent studies in the literature, which are unified and presented in a tutorial fashion in this paper. A pairwise error probability represents the probability of selecting a specific hypothesis when a different hypothesis is true, and can be stacked into a pairwise probability vector for a given problem. In the considered framework, instead of optimizing the performance metric of interest over the infinite-dimensional set of all possible decision rules, the optimization is performed directly over the compact and convex set of all achievable pairwise probability vectors. We demonstrate that any pairwise probability vector within this feasible set can be realized via a randomization of at most two likelihood ratio quantizers (LRQs) with different sets of parameters. While one of these LRQs can always be selected as a deterministic LRQ, the other one is possibly a randomized LRQ, which can be written as a randomization of at most

M (M - 1)

deterministic LRQs, with M denoting the number of hypotheses. The main advantage of this framework is that it allows for the attainment of pairwise probability vectors that do not reside on the boundary of the feasible set and that are fundamentally inaccessible via LRQs, which are optimal for classical performance metrics such as the Bayes risk or the Neyman–Pearson criterion. Furthermore, we show that the characterization of decision rules with the presented framework is particularly advantageous for performance metrics based on prospect theory (PT), such as behavioral utility. Specifically, it is demonstrated that the optimal pairwise probability vector for a PT-based metric is not guaranteed to lie on the boundary of the feasible set of pairwise probability vectors. This results in suboptimal performance achieved by LRQs for such performance metrics. On the other hand, the randomized decision rules characterized in this paper can achieve pairwise probability vectors located in the interior of the feasible set, thereby yielding optimal performance. Numerical results corroborate these findings, demonstrating that the decision rules characterized within our framework yield optimal behavioral utility-based performance scores. Full article

(This article belongs to the Special Issue Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday)

► Show Figures

Figure 1

21 pages, 425 KB

Open AccessReview

Multi-Stream Quickest Change Detection: Foundations and Recent Advances

by Topi Halme and Visa Koivunen

Entropy 2026, 28(5), 566; https://doi.org/10.3390/e28050566 - 18 May 2026

Viewed by 354

Abstract

This paper provides an overview of recent developments in quickest change detection (QCD) for high-dimensional multi-sensor systems, with an emphasis on settings involving structural constraints and limited sensing resources. Classical QCD methodologies, while well understood in low-dimensional and fully observed regimes, face significant challenges when extended to modern applications characterized by large-scale data, constrained sampling or communication, and heterogeneous signal structures. We review key approaches for handling high dimensionality, including methods that exploit sparsity, and other forms of signal heterogeneity. Additionally, we discuss sampling constraints, where observations must be selected or acquired sequentially under resource limitations. Multi-stream applications can require making multiple detections, for example when detecting changes separately in different streams. The underlying assumptions on probability models, the types of changes taking place, commonly used decision-making criteria, performance indices, and error types are described. We also briefly discuss the application of machine learning in cases where the underlying probability models are not known, or there is a need to select which sensors should monitor the phenomena because of the large scale of the system. Full article

(This article belongs to the Special Issue Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday)

► Show Figures

Journal Menu

Journal Browser

Foundations and Frontiers of Information Theory—Dedicated to Professor H. Vincent Poor on the Occasion of His 75th Birthday

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI