References & Further Reading

References

  1. C. E. Shannon, A Mathematical Theory of Communication, 1948

    The paper that started it all. Shannon's source–channel separation theorem appears here alongside the source coding theorem and channel coding theorem. Essential for understanding the philosophical foundation of modular system design.

  2. T. M. Cover, A. El Gamal, and M. Salehi, Multiple Access Channels with Arbitrarily Correlated Sources, 1980

    The foundational paper on sending correlated sources over a MAC. Establishes sufficient conditions for transmissibility that go beyond the separation approach, showing that source correlation provides implicit cooperation.

  3. M. Gastpar, B. Rimoldi, and M. Vetterli, To Code, or Not to Code: Lossy Source–Channel Communication Revisited, 2003

    Systematic study of when uncoded (analog) transmission is optimal. Shows that for Gaussian sources over Gaussian channels at matched bandwidth, uncoded is optimal, and provides a framework for understanding the gap between coded and uncoded approaches.

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

    Chapter 15 covers the separation theorem for point-to-point channels. Chapter 15.7 discusses joint source–channel coding and the conditions under which separation holds for multi-terminal settings.

  5. A. El Gamal and Y.-H. Kim, Network Information Theory, Cambridge University Press, 2011

    Part IV provides the most comprehensive treatment of joint source–channel coding for multi-terminal networks. Chapters 21–22 cover the MAC and BC cases in detail with full proofs of both achievability and converse.

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

    Pioneering work on deep learning-based JSCC. Demonstrates that learned joint source–channel codes can outperform separate coding for image transmission at short blocklengths and under channel mismatch.

  7. Y. Polyanskiy, H. V. Poor, and S. Verdú, Channel Coding Rate in the Finite Blocklength Regime, 2010

    Establishes the finite-blocklength analysis that quantifies the separation penalty at short blocklengths. Essential for understanding why JSCC matters for URLLC applications.

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

    Comprehensive treatment of joint source–channel coding for correlated sources over multi-access and broadcast channels, with both inner and outer bounds.

  9. C. E. Shannon, Coding Theorems for a Discrete Source with a Fidelity Criterion, 1959

    Shannon's original treatment of lossy source coding and the rate-distortion function. Establishes the lossy separation theorem.

Further Reading

For readers who want to explore joint source–channel coding beyond the foundational results in this chapter.

  • Deep joint source–channel coding (DeepJSCC)

    D. B. Kurka and D. Gündüz, 'Bandwidth-Agile Image Transmission with Deep Joint Source–Channel Coding,' IEEE Trans. Wireless Commun., 2021

    Extends DeepJSCC to variable bandwidth ratios, addressing a key practical limitation. Shows that learned codes can adapt to different channel conditions without retraining — a capability that separate coding achieves naturally through adaptive modulation and coding.

  • Separation in networks with feedback

    A. El Gamal and Y.-H. Kim, *Network Information Theory*, Ch. 22, Cambridge University Press, 2011

    Feedback can enlarge the capacity region of the MAC (unlike the point-to-point case), which means the separation theorem takes a different form. This chapter provides the complete treatment with full proofs.

  • Finite-blocklength joint source–channel coding

    V. Kostina and S. Verdú, 'Lossy Joint Source–Channel Coding in the Finite Blocklength Regime,' IEEE Trans. Inform. Theory, vol. 59, no. 5, 2013

    Provides tight second-order bounds for joint source–channel coding at finite blocklength, quantifying exactly how much separation costs for a given blocklength and error probability. Essential for URLLC system design.

  • Semantic communication and task-oriented coding

    E. C. Strinati et al., '6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications,' Computer Networks, 2021

    Explores how the separation paradigm breaks down when the communication goal is task completion rather than source reconstruction. Connects the theory of this chapter to the 6G vision discussed in Chapter 29.

  • Hybrid digital–analog coding

    U. Mittal and N. Phamdo, 'Hybrid Digital–Analog Joint Source–Channel Codes for Broadcasting and Robust Communications,' IEEE Trans. Inform. Theory, vol. 48, no. 5, 2002

    The original systematic treatment of hybrid coding that combines the robustness of analog with the efficiency of digital. Provides achievable distortion regions for broadcasting scenarios.

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