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arxiv: 2605.16437 · v1 · pith:YMMN74TZnew · submitted 2026-05-14 · 📡 eess.SP

One-hot Coding-based URA with RFFI-Enabled Message Authentication

Wenbo Fan , Zeping Sui , Yuhei Takahashi , Jun Cheng , Zilong Liu , Pingzhi Fan This is my paper

Pith reviewed 2026-05-20 19:49 UTC · model grok-4.3

classification 📡 eess.SP
keywords unsourced random accessone-hot codingradio frequency fingerprint identificationmessage authenticationInternet of Thingsultra-short payloadon-off keyingorthogonal channel structure
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The pith

One-hot coding maps messages to orthogonal channels for radio-fingerprint authentication in unsourced random access.

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

The paper proposes a one-hot coding based framework for unsourced random access in IoT networks. It addresses the security issue where the receiver cannot associate decoded messages with devices, allowing forged messages. By using an OHC-based common codebook and on-off keying, messages are mapped to orthogonal channel uses. This structure allows radio-frequency fingerprint identification to authenticate the signals based on device-specific hardware impairments. The approach authenticates messages without adding extra payload while keeping communication reliable for ultra-short payloads.

Core claim

The OHC-based URA framework enables message authentication by exploiting the orthogonal channel structure created by mapping distinct messages to orthogonal channel uses via a common codebook and on-off keying modulation, allowing RFFI to verify the originating device through hardware impairments without an additional authentication payload.

What carries the argument

The OHC-based common codebook combined with on-off keying modulation, which produces an orthogonal channel structure that supports radio-frequency fingerprint identification for authentication.

If this is right

  • Analytical expressions can be derived for the per-user probability of error in the proposed scheme.
  • The probability of successful spoofing can be calculated and is reduced compared to standard URA.
  • The scheme maintains reliable communication performance in ultra-short-payload IoT scenarios.
  • Secure URA transmission is enabled without compromising the unsourced principle.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar orthogonal mapping techniques could be applied to other unsourced or grant-free access schemes to add security features.
  • Real-world deployment would require calibration for varying channel conditions to maintain fingerprint stability.
  • If hardware impairments change over time, periodic re-authentication or adaptive methods might be needed.
  • Extending to multi-antenna receivers could improve fingerprint detection accuracy.

Load-bearing premise

Device-specific hardware impairments provide sufficiently unique, stable, and detectable radio-frequency fingerprints that can reliably distinguish legitimate devices from spoofers under the assumed channel and modulation conditions.

What would settle it

An experiment showing that the probability of successful spoofing does not decrease or that message authentication fails frequently when using real devices with similar hardware would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.16437 by Jun Cheng, Pingzhi Fan, Wenbo Fan, Yuhei Takahashi, Zeping Sui, Zilong Liu.

Figure 2
Figure 2. Figure 2: The overall system block diagram for an uplink transmission round. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The proposed OHC-based URA scheme for B = 3. transmitted signal is sk(t) = A PN−1 n=0 xk,ng(t − nTs). Here, xk,n is the n-th element of the codeword xk, representing the binary symbol transmitted by device k during the n￾th channel use, assuming perfect symbol- and block-level synchronization. ψk(·) is device-specific due to hardware impairments. Each transmission block consists of N channel uses. An examp… view at source ↗
Figure 4
Figure 4. Figure 4: presents the analytical results in terms of the minimum required Eb/N0 of the proposed scheme, under a target PUPE of 0.05 and the assumption of perfect message authentication (i.e., Pmd = Pfa = 0). In addition, the results of [2] are presented as a baseline at the same code rate B/2 B under the same target PUPE and authentication assumptions. It is observed that the proposed scheme requires a lower Eb/N0 … view at source ↗
Figure 5
Figure 5. Figure 5: Required Eb/N0 and PI for PUPE = 0.05 vs. the number of active legitimate devices DL for B = 12, DI = 10, and N = 2B. signals corresponding to each message are spread over all channel uses, causing significant interference among distinct messages. In contrast, the proposed scheme transmits signals associated with distinct messages over different channel uses, thereby enhancing symbol-level SNR and avoiding… view at source ↗
read the original abstract

Unsourced random access (URA) has emerged as a promising paradigm for enabling massive connectivity in Internet-of-Things (IoT) networks. However, since URA transmissions do not contain device identifiers, the receiver may not associate decoded messages with their originating devices, introducing a security vulnerability: forged messages may be decoded as legitimate. To address this problem, this paper proposes a one-hot coding (OHC)-based URA framework that enables message authentication while preserving the unsourced transmission principle. Specifically, distinct messages are mapped onto orthogonal channel uses via an OHC-based common codebook and transmitted using on-off keying modulation. The resulting orthogonal channel structure enables radio-frequency fingerprint identification to authenticate received signals by exploiting device-specific hardware impairments, thereby authenticating decoded messages without introducing an additional authentication payload. Analytical expressions for the per-user probability of error and the probability of successful spoofing are derived. Numerical results demonstrate that the proposed scheme enables secure URA transmission while maintaining reliable communication performance in ultra-short-payload IoT scenarios.

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

1 major / 1 minor

Summary. The manuscript proposes a one-hot coding (OHC)-based unsourced random access (URA) framework for massive IoT connectivity. Distinct messages are mapped to orthogonal channel uses via an OHC common codebook and transmitted with on-off keying (OOK). The resulting orthogonal structure enables radio-frequency fingerprint identification (RFFI) to authenticate decoded messages by exploiting device-specific hardware impairments, without adding authentication payload. Analytical expressions are derived for per-user error probability and probability of successful spoofing; numerical results are presented for ultra-short-payload scenarios.

Significance. If the central derivations hold, the work offers a low-overhead method to close the authentication gap in URA while preserving the unsourced principle, which is relevant for secure massive access in IoT. The explicit use of OHC-induced orthogonality to support RFFI is a concrete technical contribution, and the provision of closed-form probability expressions strengthens the analysis.

major comments (1)
  1. [§4] §4 (Analytical Performance Analysis), specifically the derivation of the probability of successful spoofing: the expression conditions on ideal RFFI classification and does not incorporate the finite error rate of the RFFI detector when applied to OOK-modulated signals subject to device-specific impairment variance and residual multi-user interference after OHC despreading. Because this probability is load-bearing for the security claim, the analysis must be revised to include the classifier error under the stated channel and modulation conditions.
minor comments (1)
  1. [§2] Notation for the OHC mapping and the RFFI decision threshold should be introduced with explicit definitions in the system model section to avoid ambiguity when reading the probability derivations.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive review of our manuscript. The major comment on the analytical derivation in Section 4 is addressed point by point below. We agree that strengthening the security analysis will improve the paper and will incorporate the suggested revision.

read point-by-point responses

  1. Referee: [§4] §4 (Analytical Performance Analysis), specifically the derivation of the probability of successful spoofing: the expression conditions on ideal RFFI classification and does not incorporate the finite error rate of the RFFI detector when applied to OOK-modulated signals subject to device-specific impairment variance and residual multi-user interference after OHC despreading. Because this probability is load-bearing for the security claim, the analysis must be revised to include the classifier error under the stated channel and modulation conditions.

    Authors: We thank the referee for this observation. The current closed-form expression for the probability of successful spoofing in Section 4 is indeed derived under the assumption of ideal (error-free) RFFI classification. This was done to isolate and highlight the benefit of the OHC-induced orthogonality for authentication in the absence of classifier errors. We agree that, for a complete security evaluation, the finite error rate of the RFFI detector must be incorporated, taking into account OOK modulation, device-specific impairments, and residual multi-user interference after despreading. We will revise the analysis to obtain the overall spoofing probability by conditioning on both correct and erroneous RFFI decisions (via the law of total probability) and will provide either an exact expression or a tight upper bound that accounts for the classifier performance under the system model. The revised expressions will be added to Section 4, with corresponding updates to the numerical results if needed. revision: yes

Circularity Check

0 steps flagged

Derivation chain is self-contained with new analytical expressions

full rationale

The paper introduces an OHC-based URA framework that maps messages to orthogonal resources via on-off keying and then derives fresh closed-form expressions for per-user error probability and spoofing probability under RFFI authentication. These expressions are constructed from standard channel models, modulation assumptions, and the orthogonal structure itself rather than from any fitted parameter, self-referential definition, or load-bearing self-citation. No step reduces the claimed security or reliability result to a prior result by the same authors that is itself unverified; the derivations remain externally falsifiable via the stated channel and impairment models.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard wireless communication assumptions plus the domain-specific premise that RF fingerprints are usable for authentication; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Device hardware impairments yield unique and stable radio-frequency fingerprints suitable for identification
    Invoked to enable RFFI-based authentication without payload.

pith-pipeline@v0.9.0 · 5719 in / 1107 out tokens · 41221 ms · 2026-05-20T19:49:27.762165+00:00 · methodology

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

  1. [1]

    Wireless 6G connectivity for massive number of devices and critical services,

    A. E. Kalør et al. , “Wireless 6G connectivity for massive number of devices and critical services,” Proc. IEEE, vol. 113, no. 9, pp. 826–848, Sep. 2025

    work page 2025

  2. [2]

    A perspective on massive random-access,

    Y . Polyanskiy, “A perspective on massive random-access,” in Proc. Int. Symp. Inf. Theory (ISIT) , 2017, pp. 2523–2527

    work page 2017

  3. [3]

    Unsourced random access: A comprehensive survey,

    M. Ozates, M. Javad Ahmadi, M. Kazemi, D. G ¨und¨uz, and T. M. Duman, “Unsourced random access: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 28, pp. 955–984, 2026

    work page 2026

  4. [4]

    Low complexity schemes for the random access Gaussian channel,

    O. Ordentlich and Y . Polyanskiy, “Low complexity schemes for the random access Gaussian channel,” in Proc. Int. Symp. Inf. Theory (ISIT), 2017, pp. 2528–2532

    work page 2017

  5. [5]

    Irregular repetition slotted ALOHA over rayleigh block fading channels: Bounds and threshold saturation via spatial coupling,

    Y . Takahashi, G. Song, T. Kimura, and J. Cheng, “Irregular repetition slotted ALOHA over rayleigh block fading channels: Bounds and threshold saturation via spatial coupling,” IEEE Access , vol. 11, pp. 106 528–106 543, 2023

    work page 2023

  6. [6]

    A user- independent successive interference cancellation based coding scheme for the unsourced random access Gaussian channel,

    A. Vem, K. R. Narayanan, J.-F. Chamberland, and J. Cheng, “A user- independent successive interference cancellation based coding scheme for the unsourced random access Gaussian channel,” IEEE Trans. Commun., vol. 67, no. 12, pp. 8258–8272, Dec. 2019

    work page 2019

  7. [7]

    A coded compressed sensing scheme for unsourced multiple access,

    V . K. Amalladinne, J.-F. Chamberland, and K. R. Narayanan, “A coded compressed sensing scheme for unsourced multiple access,” IEEE Trans. Inf. Theory, vol. 66, no. 10, pp. 6509–6533, Oct. 2020

    work page 2020

  8. [8]

    Unsourced random access with coded compressed sensing: Integrating AMP and belief propagation,

    V . K. Amalladinne, A. K. Pradhan, C. Rush, J.-F. Chamberland, and K. R. Narayanan, “Unsourced random access with coded compressed sensing: Integrating AMP and belief propagation,” IEEE Trans. Inf. Theory, vol. 68, no. 4, pp. 2384–2409, Apr. 2022

    work page 2022

  9. [9]

    Dynamic compressed sensing approach for unsourced random access,

    E. Nassaji and D. Truhachev, “Dynamic compressed sensing approach for unsourced random access,” IEEE Commun. Lett. , vol. 28, no. 7, pp. 1644–1648, July 2024

    work page 2024

  10. [10]

    Sparse IDMA: A joint graph-based coding scheme for unsourced random access,

    A. K. Pradhan, V . K. Amalladinne, A. Vem, K. R. Narayanan, and J.-F. Chamberland, “Sparse IDMA: A joint graph-based coding scheme for unsourced random access,” IEEE Trans. Commun. , vol. 70, no. 11, pp. 7124–7133, Nov. 2022

    work page 2022

  11. [11]

    Enhanced ODMA with pattern collision resolution and parameter design for unsourced multiple access,

    J. Yan, Y . Li, G. Song, and Z. Zhang, “Enhanced ODMA with pattern collision resolution and parameter design for unsourced multiple access,” IEEE Trans. Commun. , vol. 73, no. 11, pp. 12 279–12 293, Nov. 2025

    work page 2025

  12. [12]

    Unsourced random access using ODMA and polar codes,

    M. Ozates, M. Kazemi, and T. M. Duman, “Unsourced random access using ODMA and polar codes,” IEEE Wireless Commun. Lett. , vol. 13, no. 4, pp. 1044–1047, Apr. 2024

    work page 2024

  13. [13]

    Super-sparse on-off division multiple access: Replacing repetition with idling,

    G. Song, K. Cai, Y . Chi, J. Guo, and J. Cheng, “Super-sparse on-off division multiple access: Replacing repetition with idling,” IEEE Trans. Commun., vol. 68, no. 4, pp. 2251–2263, Apr. 2020

    work page 2020

  14. [14]

    How to identify and authenticate users in massive unsourced random access,

    R. Kotaba et al. , “How to identify and authenticate users in massive unsourced random access,” IEEE Commun. Lett. , vol. 25, no. 12, pp. 3795–3799, Dec. 2021

    work page 2021

  15. [15]

    Radio frequency fingerprint identification for narrowband systems, modelling and classification,

    J. Zhang, R. Woods, M. Sandell, M. Valkama, A. Marshall, and J. Cavallaro, “Radio frequency fingerprint identification for narrowband systems, modelling and classification,” IEEE Trans. Inf. Forensics Security, vol. 16, pp. 3974–3987, 2021

    work page 2021

  16. [16]

    Radio-frequency fingerprint-tag-based device authentication for massive random access,

    Y . Kitagawa et al. , “Radio-frequency fingerprint-tag-based device authentication for massive random access,” IEEE Access , vol. 14, pp. 17 091–17 102, 2026

    work page 2026

  17. [17]

    MIMO-AFDM outperforms MIMO-OFDM in the face of hardware impairments,

    Z. Sui, Z. Liu, L. Musavian, Y . L. Guan, L.-L. Yang, and L. Hanzo, “MIMO-AFDM outperforms MIMO-OFDM in the face of hardware impairments,” arXiv preprint arXiv:2601.00502 , 2026

    work page arXiv 2026

  18. [18]

    Open set RF fingerprinting identification: A joint prediction and siamese comparison framework,

    D. Cai et al. , “Open set RF fingerprinting identification: A joint prediction and siamese comparison framework,” inProc. IEEE Int. Conf. Commun. (ICC), 2025, pp. 1007–1012

    work page 2025

  19. [19]

    Radio-frequency impairments compensation in ultra high- throughput satellite systems,

    B. F. Beidas, “Radio-frequency impairments compensation in ultra high- throughput satellite systems,” IEEE Trans. Commun., vol. 67, no. 9, pp. 6025–6038, Sep. 2019

    work page 2019

  20. [20]

    L. W. Couch, Digital and Analog Communication Systems , 3rd ed. USA: Prentice Hall PTR, 1990

    work page 1990

  21. [21]

    Physical-layer identification of RFID devices,

    B. Danev, T. S. Heydt-Benjamin, and S. ˇCapkun, “Physical-layer identification of RFID devices,” in Proc. 18th USENIX Security Symp. , 2009, pp. 199–214

    work page 2009

  22. [22]

    Deep learning based transmitter identification using power amplifier nonlinearity,

    S. S. Hanna and D. Cabric, “Deep learning based transmitter identification using power amplifier nonlinearity,” in Proc. Int. Conf. Comput. Netw. Commun. (ICNC) , 2019, pp. 674–680

    work page 2019

  23. [23]

    Generalized tensor-aided channel estimation for hardware impaired device identifi- cation,

    Q. Wu, Z. Sui, H. Q. Ngo, Q. Wan, and M. Matthaiou, “Generalized tensor-aided channel estimation for hardware impaired device identifi- cation,” IEEE Trans. Veh. Technol. , vol. 74, no. 8, pp. 13 115–13 120, Aug. 2025

    work page 2025