References & Further Reading

References

1. G. Caire, On the Illumination and Sensing Model for RF Imaging, 2026

   Internal note unifying diffraction tomography models with MIMO radar models. Provides the unified forward model connecting channel estimation to imaging (Section 30.1) and the imaging-aware ISAC framework (Section 30.4).

2. M. Manzoni, S. Tebaldini, and G. Caire, Wavefield Networked Sensing: Principles, Algorithms, and Applications, 2025

   Introduces distributed multi-AP imaging using diffraction tomography per AP and Back-Projection Algorithm in Time. Core reference for Section 30.2.

3. G. Wunder and G. Caire, Hierarchically Sparse Compressive Channel Estimation for Massive MIMO, 2015

   Hierarchical sparsity framework exploiting the cluster structure of wireless channels via the mixed $\ell_{2,1}$ norm. Section 30.1.

4. K. Xu and G. Caire, 2D Markov Prior for Near-Field Visibility Region Detection in XL-MIMO, 2024

   2D Markov random field prior for near-field channel estimation, capturing the spatial continuity of visibility regions. Section 30.1.

5. J. Lee, G.-T. Gil, and Y. H. Lee, Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications, 2016

   Establishes the compressed sensing approach to mmWave channel estimation using OMP. Section 30.1.

6. H. Lu, Y. Zeng, C. You, Y. Han, J. Chen, and R. Zhang, A Tutorial on Near-Field XL-MIMO Communications Towards 6G, 2024

   Comprehensive tutorial on near-field channel modelling and estimation for XL-MIMO. Section 30.1.

7. M. Borgerding, P. Schniter, and S. Rangan, AMP-Inspired Deep Networks for Sparse Recovery, 2017

   Deep unfolding of AMP for sparse recovery (LISTA), adapted to channel estimation in the imaging-estimation duality framework.

8. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, 2011

   Foundational reference on ADMM including the consensus formulation used for distributed imaging in Section 30.2.

9. W. Shi, Q. Ling, K. Yuan, G. Wu, and W. Yin, On the Linear Convergence of the ADMM in Decentralized Consensus Optimization, 2014

   Convergence analysis of decentralised ADMM, providing the convergence rate bound in Theorem 30.2.

10. A. Alkhateeb, S. Jiang, and G. Charan, Real-Time Digital Twins for 6G Wireless Systems, 2023

    Framework for real-time RF digital twins using neural scene representations. Core reference for Section 30.3.

11. H. He, S. Jin, C.-K. Wen, F. Gao, G. Y. Li, and Z. Xu, Model-Driven Deep Learning for Physical Layer Communications, 2024

    Model-driven sensing-assisted communication with pilot reduction. Section 30.3.

12. M. B. Mashhadi, Q. Gao, and D. Gunduz, Channel Estimation via Digital Twins, 2024

    Digital twin-based channel estimation reducing pilot overhead by exploiting scene geometry. Section 30.3.

13. L. Dai, Z. Wang, and Z. Yang, Environment-Aware 6G Communications via Digital Twins, 2024

    Environment-aware communication system design using digital twins for proactive resource management. Section 30.3.

14. F. Liu, Y. Cui, C. Masouros, J. Xu, T. X. Han, Y. C. Eldar, and S. Buzzi, Integrated Sensing and Communications: Towards Dual-Functional Wireless Networks for 6G and Beyond, 2022

    Comprehensive ISAC framework with CRB-rate tradeoff and waveform design. Provides the baseline for the imaging-aware extension in Section 30.4.

15. M. Pesavento, A Compact Formulation for the L2,1 Mixed-Norm Minimization Problem, 2026

    Efficient formulation of the group LASSO problem, directly relevant to the hierarchical sparsity framework of Section 30.1.

16. H. Wymeersch et al., Integration of Communication and Radar Sensing in 5G and Beyond, 2020

    Overview of cooperative sensing in 5G networks. Historical context for Section 30.2.

17. E. J. Candes and T. Tao, Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?, 2006

    Foundational RIP-based compressed sensing result underlying the pilot reduction theorem in Section 30.1.

Further Reading

• Atomic norm for gridless channel estimation

  Z. Yang and L. Xie, 'Exact joint sparse frequency recovery via optimization methods,' IEEE Trans. Signal Processing, vol. 64, no. 19, 2016

  Gridless sparse recovery avoiding the basis mismatch issues of dictionary-based methods discussed in Pitfall 30.1.

• Cooperative perception for autonomous vehicles

  Q. Chen et al., 'F-Cooper: Feature based cooperative perception for autonomous vehicle edge computing system,' ACM SenSys, 2019

  Practical V2V cooperative perception using feature-level fusion, complementing the networked sensing framework of Section 30.2.

• Predictive beam management with multi-modal sensing

  G. Charan, T. Osman, A. Hredzak, N. Thawdar, and A. Alkhateeb, 'Vision-position multi-modal beam prediction using real-world datasets,' Proc. IEEE Asilomar, 2023

  Multi-modal beam prediction combining vision and position data, extending the DT-aided beam management of Section 30.3.

• End-to-end learning for ISAC

  C. Liu, W. Yuan, S. Li, X. Liu, H. Li, D. W. K. Ng, and Y. Li, 'Learning-based predictive beamforming for integrated sensing and communication in vehicular networks,' IEEE JSAC, 2022

  End-to-end learning framework for vehicular ISAC, providing practical instantiation of the JCSI concepts in Section 30.4.