References & Further Reading

References

1. T. J. O'Shea, J. Hoydis, An Introduction to Deep Learning for the Physical Layer, 2017

   Foundational paper on DL for wireless PHY. Reference throughout.

2. N. Samuel, T. Diskin, A. Wiesel, Learning to Detect, 2019

   DetNet framework for NN detection. Reference for §1.

3. W. Chen, X. Lin, C. Wang, H. Li, L. Hanzo, Deep Learning-Based Receiver Design for OTFS Systems, 2021

   CNN-based OTFS detector. Core reference for §1.

4. Y. Ma, S. Wang, G. Caire, Deep Learning for OTFS Channel Estimation and Pilot Design, 2020

   CommIT contribution on learned pilots. Foundation for §2.

5. V. Monga, Y. Li, Y. C. Eldar, Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing, 2021

   Deep unfolding framework. Foundation for §3.

6. M. Khani, M. Alizadeh, J. Hoydis, P. Fleming, Adaptive Neural Signal Detection for Massive MIMO, 2020

   Unfolded NN for signal detection. Reference for §3.

7. B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y Arcas, Communication-Efficient Learning of Deep Networks from Decentralized Data, 2017

   Federated averaging (FedAvg). Foundation for §4 federated learning.

8. I. J. Goodfellow, J. Shlens, C. Szegedy, Explaining and Harnessing Adversarial Examples, 2015

   Foundational paper on adversarial examples. Reference for §4 robustness.

9. P. Raviteja, K. T. Phan, Y. Hong, E. Viterbo, Interference Cancellation and Iterative Detection for Orthogonal Time Frequency Space Modulation, 2018

   Classical MP detector for OTFS. Comparison baseline for §§1, 3.

10. R. Hadani, S. Rakib, M. Tsatsanis, A. Monk, A. J. Goldsmith, A. F. Molisch, R. Calderbank, Orthogonal Time Frequency Space Modulation, 2017

    Original OTFS framework.

11. A. Balatsoukas-Stimming, C. Studer, Deep Unfolding for Communications Systems: A Survey and Some New Directions, 2019

    Survey of deep unfolding in wireless. Reference for §3.

12. Y. Sun, M. Peng, Y. Zhou, Y. Huang, S. Mao, Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues, 2019

    Survey of ML in wireless. Broad context for chapter.

13. 3GPP, 3GPP TR 38.917: Study on 6G Air Interface, 2024

    6G study item including AI/ML in PHY. Reference for §4 deployment.

14. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016

    Comprehensive deep learning textbook. Foundational reference.

Further Reading

• Deep learning for physical layer

  O'Shea, Hoydis (IEEE TCCN 2017); Gunduz et al., *Communicate to Learn at the Edge* (IEEE JSAC 2019)

  Surveys of deep learning in wireless PHY.

• Deep unfolding and algorithm unrolling

  Monga, Li, Eldar (IEEE SPM 2021); Khani et al. (IEEE TWC 2020)

  Deep unfolding theory and applications to wireless.

• Federated learning

  McMahan et al. (AISTATS 2017); Yang, Chen, Liu (Synthesis Lectures 2019)

  Privacy-preserving distributed learning.

• Adversarial machine learning

  Goodfellow, Shlens, Szegedy (ICLR 2015); Madry et al., *Towards Deep Learning Models Resistant to Adversarial Attacks* (ICLR 2018)

  Adversarial examples and defenses.

• 6G AI/ML in standardization

  3GPP TR 38.917; ETSI ISG OAI

  Standards work on AI/ML in 5G Advanced and 6G.

• OTFS-specific ML research

  Chen et al. (IEEE TWC 2021); Ma, Wang, Caire (IEEE TWC 2020); various 2022-2026 papers

  Direct OTFS-ML research. Search IEEE for latest developments.