References & Further Reading

References

  1. C. E. Shannon and W. Weaver, The Mathematical Theory of Communication, University of Illinois Press, 1949

    The foundational work of information theory. Weaver's preface introduces the three levels of communication (technical, semantic, effectiveness) that motivate this chapter.

  2. D. Gündüz, E. Erkip, A. Goldsmith, and H. V. Poor, Source and Channel Coding for Correlated Sources over Multiuser Channels, 2009

    Analysis of joint source-channel coding for multi-user systems, showing when separation is suboptimal. Foundational for the DeepJSCC motivation.

  3. E. Bourtsoulatze, D. B. Kurka, and D. Gündüz, Deep Joint Source-Channel Coding for Wireless Image Transmission, 2019

    One of the first papers demonstrating learned JSCC for image transmission, showing graceful degradation and competitive performance with separate coding.

  4. Y. Blau and T. Michaeli, Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff, 2019

    Establishes the fundamental tradeoff between reconstruction fidelity and perceptual quality, showing they cannot be simultaneously optimized.

  5. P. A. Stavrou and M. Kountouris, The Role of Fidelity in Goal-Oriented Semantic Communication, 2023

    Formalizes the rate-utility tradeoff and connects it to the information bottleneck, providing the theoretical foundation for goal-oriented communication.

  6. Y. Shao, S. C. Liew, and D. Gündüz, Learning Task-Oriented Communication for Edge Inference, 2021

    Practical task-oriented communication system for edge inference, showing significant bandwidth savings over reconstruction-based approaches.

  7. T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley, 2nd ed., 2006

    Chapter 10 on rate-distortion theory and Chapter 13 on joint source-channel coding provide the classical foundation for the semantic communication extensions.

Further Reading

Semantic communication is a rapidly evolving field. These resources provide broader context and deeper coverage.

  • Comprehensive survey of semantic communication

    H. Xie et al., "A Lite Distributed Semantic Communication System for Internet of Things," IEEE J. Selected Areas in Communications, 2021.

    Practical semantic communication system for IoT with lightweight encoder design, demonstrating that semantic gains are achievable even on resource-constrained devices.

  • Rate-distortion-perception theory

    Y. Blau and T. Michaeli, "Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff," ICML 2019.

    Foundational paper on the three-way tradeoff between rate, distortion, and perceptual quality. Essential for understanding why generative models and MMSE estimators produce qualitatively different reconstructions.

  • DeepJSCC with attention and SNR adaptation

    D. B. Kurka and D. Gündüz, "DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback," IEEE J. Selected Areas in Information Theory, 2020.

    Extends DeepJSCC with channel output feedback, achieving near-optimal performance for Gaussian sources and competitive results for images.

  • Semantic communication for 6G

    Z. Qin et al., "Semantic Communications: Principles and Challenges," arXiv:2201.01389, 2022.

    Overview of semantic communication in the context of 6G standardization, covering the system architecture, metrics, and open challenges.

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