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arxiv: 2510.00342 · v1 · submitted 2025-09-30 · 📡 eess.SP

Site-Specific Beam Learning for Full-Duplex Massive MIMO Wireless Systems

Samuel Li , Ian P. Roberts This is my paper

Pith reviewed 2026-05-18 10:50 UTC · model grok-4.3

classification 📡 eess.SP
keywords full-duplexbeamformingmassive MIMOdeep learningself-interferencemillimeter-wavewireless communications
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The pith

Deep learning designs low self-interference beams for full-duplex massive MIMO without explicit channel estimation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a beam learning framework for full-duplex wireless systems that avoids the high overhead of explicitly estimating the self-interference channel. Instead, it designs beam codebooks to gather implicit channel information and feeds this into a deep learning network to create suitable transmit and receive beams. This is particularly relevant for millimeter-wave and massive MIMO setups where traditional methods struggle with fast-fading conditions. Simulations show it maintains low interference and high signal quality while cutting measurements substantially.

Core claim

By using site-specific beam codebooks and a deep learning network to process implicit channel knowledge, the framework synthesizes transmit and receive beams that achieve low self-interference and high SNR without requiring explicit self-interference channel estimation.

What carries the argument

A deep neural network trained on implicit channel observations obtained from carefully designed site-specific beam codebooks to generate full-duplex serving beams.

Load-bearing premise

Ray-tracing simulations accurately capture the real propagation environments and channel statistics where the system would be deployed.

What would settle it

A field trial in an actual urban or indoor site comparing the achieved self-interference levels and SNR against those predicted by the ray-tracing model and learning framework.

Figures

Figures reproduced from arXiv: 2510.00342 by Ian P. Roberts, Samuel Li.

Figure 1
Figure 1. Figure 1: An in-band full-duplex base station transmits to a downlink user [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Timeline of the envisioned use of our proposed scheme, with one time slot defined as the time to collect a single probing measurement across [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Proposed end-to-end neural network model for full-duplex beam design. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Simulation scenario with a base station and multiple users. Users are [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sum spectral efficiency as a function of the self-interference channel [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: CDFs of (a) self-interference INRUL and (b) uplink SINRUL for different numbers of probing measurements M, where κ = 0 dB. in sum spectral efficiency. Further evaluating the impact of M on the model’s per￾formance, [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sum spectral efficiency as a function of the number of reflective rays [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
read the original abstract

Existing beamforming-based full-duplex solutions for multi-antenna wireless systems often rely on explicit estimation of the self-interference channel. The pilot overhead of such estimation, however, can be prohibitively high in millimeter-wave and massive MIMO systems, thus limiting the practicality of existing solutions, especially in fast-fading conditions. In this work, we present a novel beam learning framework that bypasses explicit self-interference channel estimation by designing beam codebooks to efficiently obtain implicit channel knowledge that can then be processed by a deep learning network to synthesize transmit and receive beams for full-duplex operation. Simulation results using ray-tracing illustrate that our proposed technique can allow a full-duplex base station to craft serving beams that couple low self-interference while delivering high SNR, with 75-97% fewer measurements than would be required for explicit estimation of the self-interference channel.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a site-specific beam learning framework for full-duplex massive MIMO systems. It trains a deep network on implicit beam measurements generated by ray-tracing to synthesize transmit and receive beams that achieve low self-interference and high SNR, bypassing explicit self-interference channel estimation and claiming 75-97% fewer measurements than conventional approaches.

Significance. If the results hold, the framework could meaningfully reduce pilot overhead for full-duplex operation in mmWave and massive MIMO deployments, particularly under fast fading. The site-specific ray-tracing training approach is a practical strength for tailoring to real environments, provided the learned mapping generalizes.

major comments (2)
  1. [Evaluation / Simulation Results] Evaluation section: the reported 75-97% measurement reduction and the low-SI/high-SNR operating points are supported solely by ray-tracing simulations; no error bars, Monte-Carlo run counts, or statistical significance tests are presented, which weakens in the quantitative claims.
  2. [Simulation Setup] Simulation setup and generalization discussion: the central claim that the learned beams transfer to the target deployment site rests on the unverified assumption that the ray-tracing multipath and angular statistics match real propagation; no cross-environment tests or sensitivity analysis to model mismatch are reported.
minor comments (1)
  1. [Abstract] Abstract and introduction: the phrase '75-97% fewer measurements' should be accompanied by a brief statement of the baseline explicit-estimation method and the exact measurement count used for comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and indicate the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Evaluation / Simulation Results] Evaluation section: the reported 75-97% measurement reduction and the low-SI/high-SNR operating points are supported solely by ray-tracing simulations; no error bars, Monte-Carlo run counts, or statistical significance tests are presented, which weakens in the quantitative claims.

    Authors: We agree that the manuscript would benefit from additional statistical detail. The reported gains are obtained from ray-tracing simulations across multiple user locations and channel realizations within the same site. In the revised version we will report the number of independent Monte-Carlo runs performed, include error bars on the key performance metrics, and state the observed variance to support the 75-97% measurement-reduction figures. revision: yes

  2. Referee: [Simulation Setup] Simulation setup and generalization discussion: the central claim that the learned beams transfer to the target deployment site rests on the unverified assumption that the ray-tracing multipath and angular statistics match real propagation; no cross-environment tests or sensitivity analysis to model mismatch are reported.

    Authors: The framework is explicitly site-specific: the deep network is trained on ray-tracing data generated for the target environment. We do not claim universal generalization across unrelated sites. In the revision we will add a dedicated paragraph discussing sensitivity of the learned beams to variations in ray-tracing parameters (number of paths, angular spread, material properties) and will note the limitations of relying on a single propagation model without real-world validation. revision: partial

Circularity Check

0 steps flagged

No significant circularity; claims rest on independent simulation evaluations

full rationale

The paper's core contribution is an empirical beam-learning framework that trains a deep network on implicit measurements generated via ray-tracing to produce full-duplex beams. The reported measurement reductions and performance metrics are direct outputs of those simulations rather than quantities defined by construction from fitted parameters or self-referential equations. No load-bearing step reduces to a self-citation chain, ansatz smuggled via prior work, or renaming of known results; the derivation chain is self-contained against the stated simulation benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Analysis is limited to the abstract; therefore the ledger reflects only assumptions visible at this level of detail.

free parameters (1)
  • Deep learning network architecture and training hyperparameters
    The network that maps implicit measurements to beams must be trained on site-specific data; exact architecture and fitting procedure are not described.
axioms (1)
  • domain assumption Ray-tracing provides a sufficiently accurate model of real propagation for training and evaluation
    All reported performance numbers derive from ray-tracing simulations.

pith-pipeline@v0.9.0 · 5671 in / 1332 out tokens · 37015 ms · 2026-05-18T10:50:30.790233+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages

  1. [1]

    Full-duplex wireless for 6G: Progress brings new opportunities and challenges,

    B. Smidaet al., “Full-duplex wireless for 6G: Progress brings new opportunities and challenges,”IEEE J. Sel. Areas Commun., vol. 41, no. 9, pp. 2729–2750, Sep. 2023

    work page 2023

  2. [2]

    In-band full-duplex wireless: Challenges and opportunities,

    A. Sabharwalet al., “In-band full-duplex wireless: Challenges and opportunities,”IEEE J. Sel. Areas Commun., vol. 32, no. 9, pp. 1637– 1652, Sep. 2014

    work page 2014

  3. [3]

    In-band full-duplex technology: Techniques and systems survey,

    K. E. Kolodziej, B. T. Perry, and J. S. Herd, “In-band full-duplex technology: Techniques and systems survey,”IEEE Trans. Microw. Theory Techn., vol. 67, no. 7, pp. 3025–3041, Feb. 2019

    work page 2019

  4. [4]

    On the learning of digital self-interference cancellation in full-duplex radios,

    J. Kimet al., “On the learning of digital self-interference cancellation in full-duplex radios,”IEEE Wireless Commun., vol. 31, no. 4, pp. 184– 191, Aug. 2024

    work page 2024

  5. [5]

    In-band full-duplex: The physical layer,

    B. Smidaet al., “In-band full-duplex: The physical layer,”Proc. IEEE, vol. 112, no. 5, pp. 433–462, May 2024

    work page 2024

  6. [6]

    Millimeter- wave full duplex radios: New challenges and techniques,

    I. P. Roberts, J. G. Andrews, H. B. Jain, and S. Vishwanath, “Millimeter- wave full duplex radios: New challenges and techniques,”IEEE Wireless Commun., vol. 28, no. 1, pp. 36–43, Feb. 2021

    work page 2021

  7. [7]

    Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems,

    S. Huang, Y . Ye, and M. Xiao, “Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems,”IEEE Trans. Cogn. Commun. Netw., vol. 7, no. 1, pp. 120–132, Mar. 2021

    work page 2021

  8. [8]

    LONESTAR: Analog beamforming codebooks for full-duplex millimeter wave systems,

    I. P. Roberts, S. Vishwanath, and J. G. Andrews, “LONESTAR: Analog beamforming codebooks for full-duplex millimeter wave systems,”IEEE Trans. Wireless Commun., vol. 22, no. 9, pp. 5754–5769, Sep. 2023

    work page 2023

  9. [9]

    CISSIR: Beam Codebooks with Self-Interference Reduction Guarantees for Integrated Sensing and Communication Beyond 5G,

    R. Hernangómez, J. Fink, R. L. G. Cavalcante, and S. Sta ´nczak, “CISSIR: Beam Codebooks with Self-Interference Reduction Guarantees for Integrated Sensing and Communication Beyond 5G,” Feb. 2025

    work page 2025

  10. [10]

    Deep Unfolding-Powered Analog Beamforming for In- Band Full-Duplex,

    I. Bilbaoet al., “Deep Unfolding-Powered Analog Beamforming for In- Band Full-Duplex,”IEEE Open J. Commun. Soc., vol. 5, pp. 3753–3761, 2024

    work page 2024

  11. [11]

    Full-duplex mmWave MIMO with finite-resolution phase shifters,

    R. López-Valcarce and M. Martínez-Cotelo, “Full-duplex mmWave MIMO with finite-resolution phase shifters,”IEEE Trans. Wireless Commun., vol. 21, no. 11, pp. 8979–8994, Nov. 2022

    work page 2022

  12. [12]

    STEER: Beam selection for full-duplex millimeter wave communication systems,

    I. P. Roberts, A. Chopra, T. Novlan, S. Vishwanath, and J. G. Andrews, “STEER: Beam selection for full-duplex millimeter wave communication systems,”IEEE Trans. Commun., vol. 70, no. 10, pp. 6902–6917, 2022

    work page 2022

  13. [13]

    STEER+: Robust beam refinement for full-duplex millimeter wave communication systems,

    I. P. Roberts, Y . Zhang, T. Osman, and A. Alkhateeb, “STEER+: Robust beam refinement for full-duplex millimeter wave communication systems,” inProc. Asilomar Conf. Signals, Sys., and Comput., Oct. 2023

    work page 2023

  14. [14]

    Active beam learning for full-duplex wireless systems,

    J. M. Kong and I. P. Roberts, “Active beam learning for full-duplex wireless systems,” inProc Asilomar Conf Signals Sys Comput, Oct. 2024

    work page 2024

  15. [15]

    Grid-Free MIMO Beam Alignment Through Site-Specific Deep Learning,

    Y . Heng and J. G. Andrews, “Grid-Free MIMO Beam Alignment Through Site-Specific Deep Learning,”IEEE Trans. Wireless Commun., vol. 23, no. 2, pp. 908–921, Feb. 2024

    work page 2024

  16. [16]

    Reinforcement learning of beam codebooks in millimeter wave and terahertz MIMO systems,

    Y . Zhang, M. Alrabeiah, and A. Alkhateeb, “Reinforcement learning of beam codebooks in millimeter wave and terahertz MIMO systems,” IEEE Trans. Commun., vol. 70, no. 2, pp. 904–919, Nov. 2022

    work page 2022

  17. [17]

    Hybrid precoding and combining for full-duplex millimeter wave communica- tion,

    J. Palacios, J. Rodríguez-Fernández, and N. González-Prelcic, “Hybrid precoding and combining for full-duplex millimeter wave communica- tion,” inProc. IEEE GLOBECOM, Dec. 2019, pp. 1–6

    work page 2019

  18. [18]

    Six key challenges for beam management in 5.5G and 6G systems,

    Y . Henget al., “Six key challenges for beam management in 5.5G and 6G systems,”IEEE Commun. Mag., vol. 59, no. 7, pp. 74–79, Jul. 2021

    work page 2021

  19. [19]

    Communications toolbox version: 24.2 (R2024b),

    T. M. Inc., “Communications toolbox version: 24.2 (R2024b),” The MathWorks Inc., 2024

    work page 2024

  20. [20]

    Spherical-wave model for short-range MIMO,

    J.-S. Jiang and M. A. Ingram, “Spherical-wave model for short-range MIMO,”IEEE Trans. Commun., vol. 53, no. 9, pp. 1534–1541, 2005

    work page 2005