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Entropy
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Joint Sensing, Communication, and Computation
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Joint Sensing, Communication, and Computation

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

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1416

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. Rafael Schaefer

E-Mail Website
Guest Editor
Chair of Information Theory and Machine Learning, Technische Universität Dresden, 01062 Dresden, Germany
Interests: information theory; communications; physical layer security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As we approach the era of 6G, integrated sensing and communication (ISAC) is emerging as a transformative paradigm that promises to redefine wireless networks. By merging advanced signal processing techniques with fundamental insights from information theory, future systems could address a multitude of challenges including reliability, efficiency, security, and latency. This Special Issue will assemble a collection of pioneering research contributions that explore the interplay between sensing and communication, as well as offer innovative solutions for the unresolved issues in 6G-ISAC.

Topics of interest include, but are not limited to, the following areas:

  • Fundamental limits for ISAC;
  • Security, privacy, and covertness for ISAC;
  • Machine learning for ISAC;
  • Waveform/coding/modulation/beamforming design for ISAC;
  • Cell-free ISAC networks;
  • Millimeter wave and THz technologies for ISAC.

Prof. Dr. Onur Günlü
Prof. Dr. Rafael F. Schaefer
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-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

  • integrated sensing and communication (ISAC)
  • future communication systems
  • trustworthy 6G
  • sensing-assisted communication
  • communication-assisted sensing

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Published Papers (2 papers)

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Research

20 pages, 597 KB  
Article
BeamNet: Unsupervised Beamforming for ISAC Systems Under Imperfect CSI
by Helitha Nimnaka, Samiru Gayan, Ruhui Zhang, Hazer Inaltekin and H. Vincent Poor
Entropy 2026, 28(2), 175; https://doi.org/10.3390/e28020175 - 3 Feb 2026
Viewed by 645
Abstract
Integrated sensing and communication (ISAC) is expected to be a key enabler for future wireless networks, improving spectral and hardware efficiency by jointly performing radar sensing and wireless communication within a unified framework. This paper proposes BeamNet, an unsupervised deep learning framework [...] Read more.
Integrated sensing and communication (ISAC) is expected to be a key enabler for future wireless networks, improving spectral and hardware efficiency by jointly performing radar sensing and wireless communication within a unified framework. This paper proposes BeamNet, an unsupervised deep learning framework for transmit beamforming in dual-function radar-communication systems operating over general fading with imperfect channel state information (CSI). BeamNet maps noisy estimates of the communication and sensing channels to a transmit beamforming vector and is trained end-to-end by maximizing a weighted sum of the communication rate (CR) and sensing rate (SR), thereby learning the CR–SR Pareto frontier without beamforming labels or embedded optimization solvers. Using Rayleigh fading with perfect CSI, we first show that BeamNet reproduces the analytical Pareto-optimal beamforming solutions. We then use BeamNet to characterize, for Nakagami-m and Rician fading, the CR–SR trade-off across a range of fading parameters, and to assess robustness under distribution mismatch between training and test channels. Finally, under imperfect CSI, we demonstrate that BeamNet yields CR–SR trade-offs that are consistently sandwiched between the perfect-CSI and mismatched analytical baselines, outperforming the closed-form beamformer applied to imperfect CSI and recovering part of the performance loss caused by channel estimation errors. These results indicate that unsupervised learning offers a flexible and robust approach to ISAC beamforming in fading environments with imperfect channel knowledge. Full article
(This article belongs to the Special Issue Joint Sensing, Communication, and Computation)
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16 pages, 496 KB  
Article
Low-Latency Realism Through Randomized Distributed Function Computations: A Shannon Theoretic Approach
by Onur Günlü, Maciej Skorski and H. Vincent Poor
Entropy 2026, 28(1), 86; https://doi.org/10.3390/e28010086 - 11 Jan 2026
Viewed by 385
Abstract
Semantic communication frameworks aim to convey the underlying significance of data rather than reproducing it exactly, a perspective that enables substantial efficiency gains in settings constrained by latency or bandwidth. Motivated by this shift, we study the rate–distortion–perception (RDP) trade-off for image compression, [...] Read more.
Semantic communication frameworks aim to convey the underlying significance of data rather than reproducing it exactly, a perspective that enables substantial efficiency gains in settings constrained by latency or bandwidth. Motivated by this shift, we study the rate–distortion–perception (RDP) trade-off for image compression, a setting in which reconstructions must be not only accurate but also perceptually faithful. Our analysis is carried out through the lens of randomized distributed function computation (RDFC) framework, which provides a principled means of synthesizing randomness and shaping output distributions. Leveraging this framework, we establish finite-blocklength characterizations of the RDP region, quantifying how communication rate, distortion, and perceptual fidelity interact in non-asymptotic regimes. We further broaden this characterization by incorporating two practically relevant extensions: (i) scenarios in which encoder and decoder share side information, and (ii) settings that require strong secrecy guarantees against adversaries, which might include those with quantum capabilities. Moreover, we identify the corresponding asymptotic region under a perfect realism constraint and examine how side information, finite blocklength effects, and secrecy demands influence achievable performance. The resulting insights provide actionable guidance for the development of low-latency, secure, and realism-aware image compression and generative modeling systems. Full article
(This article belongs to the Special Issue Joint Sensing, Communication, and Computation)
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