References & Further Reading

References

  1. Q. Wu and R. Zhang, Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming, 2019

    The original statement of the joint BF problem. Section II.B discusses the CSI assumption on which all downstream results rest.

  2. D. Mishra and H. Johansson, Channel Estimation and Low-complexity Beamforming Design for Passive Intelligent Surface Assisted MISO Wireless Energy Transfer, 2019

    The first paper to formulate RIS channel estimation with an orthogonal codebook. Uses the Hadamard matrix as codebook; the DFT generalization is a one-line modification.

  3. T. L. Jensen and E. de Carvalho, An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces Based on a Minimum Variance Unbiased Estimator, 2020

    Gives the ON/OFF protocol a rigorous MVU-estimator framing and explains its $N$-factor loss relative to DFT codebook. Clean early paper.

  4. B. Zheng and R. Zhang, Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization, 2020

    DFT-based estimation for wideband OFDM-RIS. Establishes the standard analytical framework used in most subsequent papers.

  5. J. Chen, Y.-C. Liang, H. V. Cheng, and W. Yu, Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User MIMO Systems, 2019

    Pioneering paper exploiting angular sparsity for compressed-sensing RIS channel estimation. Introduces the atomic-norm framework.

  6. Z. Wang, L. Liu, and S. Cui, Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis, 2020

    The most cited paper on multi-user RIS estimation. Introduces a three-phase protocol that amortizes the BS-RIS channel estimation across users.

  7. Z.-Q. He and X. Yuan, Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO, 2020

    Matrix-factorization approach exploiting both the cascaded and angular structure. A useful middle ground between DFT codebook and pure CS.

  8. B. Zheng, C. You, W. Mei, and R. Zhang, A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications, 2022

    Comprehensive survey. The primary reference for the optimal-$\tau_p$ analysis and practical deployment guidance developed in Section 4.5.

  9. A. L. Swindlehurst, G. Zhou, R. Liu, C. Pan, and M. Li, Channel Estimation With Reconfigurable Intelligent Surfaces — A General Framework, 2022

    Tutorial-style review from a leading array-processing group. Section III on overhead-accuracy tradeoffs is particularly clear.

  10. G. Caire and I. Atzeni, CSI Acquisition for RIS-Aided Communications with Limited Pilot Overhead, 2022

    CommIT contribution. Hierarchical codebook search for the array-fed RIS architecture — exploits the near-field slowly-varying BS-RIS channel to amortize pilot overhead across users.

  11. S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing, Birkhäuser, 2013

    The standard reference for compressed-sensing theory. Chapters 4–8 cover the RIP analysis and LASSO recovery used in Section 4.4.

  12. B. Hassibi and B. M. Hochwald, How much training is needed in multiple-antenna wireless links?, 2003

    Classic pre-RIS paper on training overhead for multi-antenna channels. The $\sqrt{T/\text{SNR}}$ scaling of the optimal pilot length originates here. The RIS problem inherits the same structure.

Further Reading

Deeper dives for readers interested in specific RIS channel-estimation topics.

  • Atomic-norm minimization for off-grid angular recovery

    G. Tang, B. N. Bhaskar, P. Shah, and B. Recht, 'Compressed sensing off the grid,' IEEE TIT, 2013

    The theoretical foundation of continuous-angle sparse recovery, directly applicable to off-grid RIS channel estimation.

  • Two-stage estimation: slow BS-RIS + fast RIS-UE

    C. You, B. Zheng, and R. Zhang, 'Channel estimation and passive beamforming for intelligent reflecting surface: Discrete phase shift and progressive refinement,' IEEE JSAC, 2020

    Practical multi-timescale estimation — exploit the fact that the BS-RIS link changes slowly while UE channels change fast.

  • Deep-learning-based estimators

    A. M. Elbir and S. Coleri, 'Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO,' IEEE TWC, 2022

    Neural networks for CS recovery offer faster runtime than LASSO at comparable accuracy, promising for real-time RIS adaptation.

  • Experimental CSI measurements on prototype RIS

    Tang et al. (2021), 'Wireless Communications With Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurements' (Ch. 3 reference)

    Real experimental data to ground the theoretical MSE predictions against hardware noise and calibration effects.

  • CSI in multi-RIS and cell-free deployments

    Chapter 12 of this book + H. Guo et al., 'Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks,' IEEE TWC, 2020

    When multiple RIS panels are present, the estimation problem grows; smart pilot sharing across panels is an active research area.

ExercisesCh 5 →