pith. sign in

arxiv: 2606.17970 · v1 · pith:VA7VC6SMnew · submitted 2026-06-16 · 💻 cs.IT · math.IT

Auto-correlation Function Keying

Pith reviewed 2026-06-26 22:37 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords ACFKISACperiodic auto-correlation functionpeak sidelobe levelmutual informationmodulation design6G
0
0 comments X

The pith

Embedding data symbols on ACF sidelobes enables exact control of nominal periodic auto-correlation peaks in ISAC.

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

The paper develops auto-correlation function keying to create signals that carry data while controlling the peak sidelobes of their periodic auto-correlation function for sensing. It maximizes mutual information subject to peak sidelobe constraints and shows that a uniform ACF-domain construction is optimal at high SNR for flat channels. ACFK implements this by placing symbols on the sidelobes, giving exact nominal control that matches the actual function under spectral non-negativity. When the constraint fails, it quantifies the violation probability and bounds the sidelobe degradation, leading to better performance than probabilistic amplitude shaping in simulations.

Core claim

ACFK embeds data symbols directly onto the ACF-domain sidelobes to achieve exact control of the nominal P-ACF, which coincides with the actual P-ACF when the spectral non-negativity constraint is satisfied; otherwise the non-negativity violation probability is quantified and PSLR degradation bounded.

What carries the argument

Auto-correlation function keying (ACFK), a modulation architecture that embeds data symbols directly onto the ACF-domain sidelobes to control the periodic auto-correlation function.

If this is right

  • Stronger PSLR control than generalized PAS baseline
  • Improved weak-target detection performance under comparable settings
  • High-SNR approximate BER analysis for the ISAC transceiver over multipath channels
  • Exact nominal P-ACF control when spectral non-negativity holds

Where Pith is reading between the lines

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

  • The ACFK approach could be tested in frequency-selective channels to see if the control extends beyond flat fading assumptions.
  • Connecting ACFK to other correlation-based modulations might reveal broader applications in radar and communications.
  • The bounds on PSLR degradation could be tightened with more detailed spectral analysis.

Load-bearing premise

The spectral non-negativity constraint holds so that nominal P-ACF control matches the actual one, or the optimality applies only to quasi-static frequency-flat channels at high SNR.

What would settle it

Generate many ACFK signal realizations, compute their actual P-ACF peak sidelobe levels, and check if the degradation exceeds the derived bound when the spectrum goes negative in some realizations.

Figures

Figures reproduced from arXiv: 2606.17970 by Fan Liu, Jianhua Zhang, Shuangyang Li, Weijiang Zhao, Yifeng Xiong.

Figure 2
Figure 2. Figure 2: A single realization and the average squared nominal P-ACF of ACFK, [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The empirical CDF, theoretical lower bound and approximated CDF [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: A single realization of the squared nominal P-ACF and the zero [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A signal processing pipeline for a monostatic ISAC system employing ACFK over quasi-static multi-path channels. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The signal processing pipeline for the generalized PAS system over the quasi-static multi-path channels. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The original normalized 1024-APSK constellation and the probabilis [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: The BER performance of the uncoded ACFK system over Rician [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The BER performance comparison over AWGN and Rician channels [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 12
Figure 12. Figure 12: The empirical CDF of the PSLR of P-ACF for the ACFK and gener [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The target detection performance and range profiles of two targets [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
read the original abstract

Communication-centric ISAC is a promising paradigm for future 6G networks, in which data payload signals are expected to be reused for sensing to enhance time-frequency resource efficiency. For random payload signals, existing studies have mainly characterized the expected sidelobe level (ESL) of the periodic auto-correlation function (P-ACF). However, ESL only captures the average sidelobe behavior and does not control large spurious sidelobe peaks in individual payload realizations, which may deteriorate weak-target detection performance. This motivates the design of information-bearing signals whose random P-ACF satisfies stringent peak sidelobe level (PSL) constraints. In this paper, we formulate a mutual information maximization problem under PSL constraints and a power budget. For quasi-static frequency-flat channels, we show that a continuous auto-correlation function (ACF)-domain uniform construction provides an asymptotically optimal high-SNR design principle. Motivated by this principle, we propose auto-correlation function keying (ACFK), a finite-constellation modulation architecture that embeds data symbols directly onto the ACF-domain sidelobes. ACFK enables exact control of the nominal P-ACF, which coincides with the actual P-ACF when a spectral non-negativity constraint is met. When this is not the case, we quantify the non-negativity violation probability and bound the resulting peak sidelobe level ratio (PSLR) degradation. We further provide a reference ISAC transceiver design for ACFK over quasi-static multipath channels, together with high-SNR approximate BER analysis. Numerical results validate the theoretical analysis and show that, compared with a generalized probabilistic amplitude shaping (PAS) baseline, ACFK provides substantially stronger PSLR control and improved weak-target detection performance under comparable sensing and communication settings.

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 paper proposes auto-correlation function keying (ACFK) for communication-centric ISAC. It formulates mutual-information maximization under peak sidelobe level (PSL) constraints and power budget. For quasi-static frequency-flat channels, a continuous ACF-domain uniform construction is shown to be asymptotically optimal at high SNR. ACFK embeds data symbols directly onto ACF-domain sidelobes to achieve exact control of the nominal periodic auto-correlation function (P-ACF), which coincides with the actual P-ACF when a spectral non-negativity constraint holds; otherwise the violation probability is quantified and the resulting PSLR degradation is bounded. A reference transceiver design is given for quasi-static multipath channels together with high-SNR approximate BER analysis. Numerical comparisons against a generalized probabilistic amplitude shaping (PAS) baseline show stronger PSLR control and improved weak-target detection.

Significance. If the central claims hold, the work supplies a concrete mechanism for controlling per-realization peak sidelobes in random payload signals rather than only expected sidelobe levels, directly addressing a limitation in existing ISAC sensing analyses and offering measurable gains in weak-target detection under comparable rate and power constraints.

major comments (2)
  1. [Abstract] Abstract: the headline claim of 'exact control of the nominal P-ACF, which coincides with the actual P-ACF' is load-bearing for the reported performance advantage over generalized PAS, yet the manuscript only quantifies the non-negativity violation probability without demonstrating that this probability is vanishing (or sufficiently close to zero) for the ACF-domain uniform construction; if the violation rate remains non-vanishing, the exact-control guarantee reduces to the weaker probabilistic PSLR bound and the claimed 'substantially stronger PSLR control' is no longer supported.
  2. [Abstract] Abstract: the asymptotic optimality result is derived under the explicit assumption of quasi-static frequency-flat channels at high SNR; the finite-constellation ACFK inherits the same spectral non-negativity requirement, but no explicit verification is provided that the optimality principle continues to hold once the continuous construction is discretized and the non-negativity constraint is enforced only probabilistically.
minor comments (1)
  1. [Abstract] The abstract states that 'high-SNR approximate BER analysis' is provided but does not indicate the order of the approximation or the error term that is neglected.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract claims regarding exact P-ACF control and asymptotic optimality. We address both major comments point-by-point below with clarifications and proposed revisions to strengthen the manuscript.

read point-by-point responses
  1. Referee: [Abstract] Abstract: the headline claim of 'exact control of the nominal P-ACF, which coincides with the actual P-ACF' is load-bearing for the reported performance advantage over generalized PAS, yet the manuscript only quantifies the non-negativity violation probability without demonstrating that this probability is vanishing (or sufficiently close to zero) for the ACF-domain uniform construction; if the violation rate remains non-vanishing, the exact-control guarantee reduces to the weaker probabilistic PSLR bound and the claimed 'substantially stronger PSLR control' is no longer supported.

    Authors: We acknowledge the concern that stronger evidence of low violation probability would better support the 'substantially stronger PSLR control' claim. The manuscript already quantifies the non-negativity violation probability for the ACF-domain uniform construction and derives a bound on the resulting PSLR degradation. To directly address this point, we will add numerical evaluations in the revised results section demonstrating that the violation probability is small (e.g., below 0.01 for practical parameters at high SNR). This evidence will confirm that the nominal and actual P-ACF coincide with high probability, thereby preserving the exact-control advantage. We will also refine the abstract wording to more explicitly note the high-probability nature of the coincidence. revision: yes

  2. Referee: [Abstract] Abstract: the asymptotic optimality result is derived under the explicit assumption of quasi-static frequency-flat channels at high SNR; the finite-constellation ACFK inherits the same spectral non-negativity requirement, but no explicit verification is provided that the optimality principle continues to hold once the continuous construction is discretized and the non-negativity constraint is enforced only probabilistically.

    Authors: The asymptotic optimality result is presented strictly as the motivating design principle for the continuous ACF-domain uniform construction under the stated channel and SNR assumptions. ACFK discretizes this construction while handling the non-negativity constraint probabilistically, as analyzed in the paper. The high-SNR approximate BER analysis and numerical comparisons already show that the performance benefits extend to the finite case. We agree an explicit link would strengthen the presentation; we will add a short remark in the discussion of the design principle (Section III) explaining that discretization error vanishes asymptotically at high SNR and the probabilistic enforcement has bounded impact per the existing PSLR degradation bound. This clarifies the extension without changing the core claims. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained from stated optimization.

full rationale

The central claim derives the continuous ACF-domain uniform construction as asymptotically optimal directly from the mutual information maximization problem under PSL constraints for quasi-static frequency-flat channels at high SNR. ACFK is motivated by this principle and provides nominal P-ACF control, with explicit quantification of non-negativity violation probability and PSLR degradation bounds when the constraint fails. All performance claims are benchmarked against an external generalized PAS baseline rather than internal fitted quantities. No self-definitional reductions, fitted inputs renamed as predictions, or load-bearing self-citations appear in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The paper builds on standard mutual information maximization and correlation-function analysis from information theory and signal processing. The primary addition is the ACFK construction itself under conventional ISAC channel models.

axioms (2)
  • domain assumption Quasi-static frequency-flat channels allow the continuous ACF-domain uniform construction to be asymptotically optimal at high SNR
    Invoked to establish the high-SNR design principle for the mutual information maximization problem.
  • domain assumption Spectral non-negativity constraint ensures nominal P-ACF coincides with actual P-ACF
    Required for the claim of exact P-ACF control; violation probability is quantified separately.
invented entities (1)
  • ACFK modulation architecture no independent evidence
    purpose: Embed data symbols directly onto ACF-domain sidelobes to enforce PSL constraints
    Newly proposed finite-constellation scheme motivated by the continuous uniform construction.

pith-pipeline@v0.9.1-grok · 5841 in / 1592 out tokens · 43033 ms · 2026-06-26T22:37:32.893502+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

53 extracted references · 3 canonical work pages · 1 internal anchor

  1. [1]

    Draft New Recommendation ITU-R M. [IMT. Frame- work for 2030 and Beyond],

    ITU-R WP5D, “Draft New Recommendation ITU-R M. [IMT. Frame- work for 2030 and Beyond],” 2023

  2. [2]

    Twelve scientific challenges for 6G: Rethinking the foundations of communications theory,

    M. Chafii, L. Bariah, S. Muhaidat, and M. Debbah, “Twelve scientific challenges for 6G: Rethinking the foundations of communications theory,”IEEE Commun. Surv. Tutor., vol. 25, no. 2, pp. 868–904, 2023

  3. [3]

    A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

    W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,”IEEE Netw., vol. 34, no. 3, pp. 134–142, 2019

  4. [4]

    Integrating sensing and communi- cations for ubiquitous IoT: Applications, trends, and challenges,

    Y . Cui, F. Liu, X. Jing, and J. Mu, “Integrating sensing and communi- cations for ubiquitous IoT: Applications, trends, and challenges,”IEEE Netw., vol. 35, no. 5, pp. 158–167, 2021

  5. [5]

    Integrated sensing and communications: Toward dual-functional wire- less networks for 6G and beyond,

    F. Liu, Y . Cui, C. Masouros, J. Xu, T. X. Han, Y . C. Eldar, and S. Buzzi, “Integrated sensing and communications: Toward dual-functional wire- less networks for 6G and beyond,”IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1728–1767, 2022

  6. [6]

    An overview of signal processing techniques for joint communication and radar sensing,

    J. A. Zhang, F. Liu, C. Masouros, R. W. Heath, Z. Feng, L. Zheng, and A. Petropulu, “An overview of signal processing techniques for joint communication and radar sensing,”IEEE J. Sel. Top. Signal Process., vol. 15, no. 6, pp. 1295–1315, 2021

  7. [7]

    Multicarrier ISAC: Advances in waveform de- sign, signal processing, and learning under nonidealities,

    V . Koivunen, M. F. Keskin, H. Wymeersch, M. Valkama, and N. Gonz ´alez-Prelcic, “Multicarrier ISAC: Advances in waveform de- sign, signal processing, and learning under nonidealities,”IEEE Signal Process. Mag., vol. 41, no. 5, pp. 17–30, 2024

  8. [8]

    Integrated sensing and communication signals toward 5G-A and 6G: A survey,

    Z. Wei, H. Qu, Y . Wang, X. Yuan, H. Wu, Y . Du, K. Han, N. Zhang, and Z. Feng, “Integrated sensing and communication signals toward 5G-A and 6G: A survey,”IEEE Internet Things J., vol. 10, no. 13, pp. 11 068– 11 092, 2023

  9. [9]

    5G PRS-based sensing: A sensing reference signal approach for joint sensing and communication system,

    Z. Wei, Y . Wang, L. Ma, S. Yang, Z. Feng, C. Pan, Q. Zhang, Y . Wang, H. Wu, and P. Zhang, “5G PRS-based sensing: A sensing reference signal approach for joint sensing and communication system,”IEEE Trans. Veh. Technol., vol. 72, no. 3, pp. 3250–3263, 2023

  10. [10]

    Multiple reference signals collaborative sensing for integrated sensing and communication system towards 5G-A and 6G,

    Z. Wei, F. Li, H. Liu, X. Chen, H. Wu, K. Han, and Z. Feng, “Multiple reference signals collaborative sensing for integrated sensing and communication system towards 5G-A and 6G,”IEEE Trans. Veh. Technol., vol. 73, no. 10, pp. 15 185–15 199, 2024

  11. [11]

    Integrated sensing and communications via 5G NR waveform: Performance analysis,

    Y . Cui, X. Jing, and J. Mu, “Integrated sensing and communications via 5G NR waveform: Performance analysis,” inProc. Int. Conf. Acoust., Speech Signal Process. (ICASSP), Singapore, 2022, pp. 8747–8751

  12. [12]

    NR; physical channels and modulation,

    3GPP, “NR; physical channels and modulation,” 3rd Generation Partner- ship Project (3GPP), Technical Specification (TS) 38.211, 2023, version 17.4.0

  13. [13]

    On the fundamental tradeoff of integrated sensing and communications under Gaussian channels,

    Y . Xiong, F. Liu, Y . Cui, W. Yuan, T. X. Han, and G. Caire, “On the fundamental tradeoff of integrated sensing and communications under Gaussian channels,”IEEE Trans. Inf. Theory, vol. 69, no. 9, pp. 5723– 5751, 2023

  14. [14]

    From torch to projector: Fundamental tradeoff of integrated sensing and communications,

    Y . Xiong, F. Liu, K. Wan, W. Yuan, Y . Cui, and G. Caire, “From torch to projector: Fundamental tradeoff of integrated sensing and communications,”IEEE BITS Inf. Theory Mag., pp. 1–13, 2024

  15. [15]

    Deterministic- random tradeoff of integrated sensing and communications in Gaussian channels: A rate-distortion perspective,

    F. Liu, Y . Xiong, K. Wan, T. X. Han, and G. Caire, “Deterministic- random tradeoff of integrated sensing and communications in Gaussian channels: A rate-distortion perspective,” inProc. IEEE Int. Symp. Inf. Theory (ISIT), Taipei, Taiwan, 2023, pp. 2326–2331

  16. [16]

    Sensing with communication signals: From information theory to signal processing,

    F. Liu, Y .-F. Liu, Y . Cui, C. Masouros, J. Xu, T. Xiao Han, S. Buzzi, Y . C. Eldar, and S. Jin, “Sensing with communication signals: From information theory to signal processing,”IEEE J. Sel. Areas Commun., vol. 44, pp. 1–30, 2026

  17. [17]

    CP-OFDM achieves the lowest average ranging sidelobe under QAM/PSK constellations,

    F. Liu, Y . Zhang, Y . Xiong, S. Li, W. Yuan, F. Gao, S. Jin, and G. Caire, “CP-OFDM achieves the lowest average ranging sidelobe under QAM/PSK constellations,”IEEE Trans. Inf. Theory, vol. 71, no. 9, pp. 6950–6967, 2025

  18. [18]

    Uncovering the iceberg in the sea: Fundamentals of pulse shaping and modulation design for random ISAC signals,

    F. Liu, Y . Xiong, S. Lu, S. Li, W. Yuan, C. Masouros, S. Jin, and G. Caire, “Uncovering the iceberg in the sea: Fundamentals of pulse shaping and modulation design for random ISAC signals,”IEEE Trans. Signal Process., vol. 73, pp. 2511–2526, 2025

  19. [19]

    Reshaping the ISAC tradeoff under OFDM signaling: A probabilistic constellation shaping approach,

    Z. Du, F. Liu, Y . Xionget al., “Reshaping the ISAC tradeoff under OFDM signaling: A probabilistic constellation shaping approach,”IEEE Trans. Signal Process., vol. 72, pp. 4782–4797, 2024

  20. [20]

    Input distribution design for ranging-oriented OFDM-ISAC systems under frequency-selective fading,

    W. Zhao and Y . Xiong, “Input distribution design for ranging-oriented OFDM-ISAC systems under frequency-selective fading,”arXiv preprint,

  21. [21]
  22. [22]

    Constellation shaping for OFDM-ISAC Systems: From theoretical bounds to practical implementation,

    B. Geiger, F. Liu, S. Lu, A. Rode, D. G. Gaviria, C. Muth, and L. Schmalen, “Constellation shaping for OFDM-ISAC Systems: From theoretical bounds to practical implementation,”IEEE Trans. Commun., vol. 74, pp. 6025–6042, 2026

  23. [23]

    Exploiting both pilots and data payloads for integrated sensing and communications,

    C. Xu, X. Yu, F. Liu, and S. Jin, “Exploiting both pilots and data payloads for integrated sensing and communications,”IEEE Trans. Wireless. Commun., vol. 25, pp. 5573–5586, 2026

  24. [24]

    Rethinking signaling design for ISAC: From pilot-based to payload-based sensing,

    Y . Li, Y . Zhang, C. Masouros, S. Pollin, and F. Liu, “Rethinking signaling design for ISAC: From pilot-based to payload-based sensing,” IEEE Commun. Stand. Mag., pp. 1–9, 2025

  25. [25]

    On discrete ambiguity functions of random communication waveforms,

    Y . Zhang, F. Liu, Y . Xiong, W. Yuan, S. Li, L. Zheng, T. X. Han, C. Masouros, and S. Jin, “On discrete ambiguity functions of random communication waveforms,”arXiv preprint, 2025. [Online]. Available: https://arxiv.org/pdf/2512.08352

  26. [26]

    M. A. Richards,Fundamentals of radar signal processing. Mcgraw-hill New York, 2005

  27. [27]

    Lower bounds on the maximum cross correlation of signals (corresp.),

    L. Welch, “Lower bounds on the maximum cross correlation of signals (corresp.),”IEEE Trans. Inf. Theory, vol. 20, no. 3, pp. 397–399, 1974

  28. [28]

    Bounds on crosscorrelation and autocorrelation of se- quences (corresp.),

    D. Sarwate, “Bounds on crosscorrelation and autocorrelation of se- quences (corresp.),”IEEE Trans. Inf. Theory, vol. 25, no. 6, pp. 720–724, 1979

  29. [29]

    Class of binary sequences with zero correlation zone,

    P. Z. Fan, N. Suehiro, N. Kuroyanagi, and X. M. Deng, “Class of binary sequences with zero correlation zone,”Electron. Lett., vol. 35, no. 10, pp. 777–779, 1999

  30. [30]

    Lower bounds on correlation of spreading sequence set with low or zero correlation zone,

    X. H. Tang, P. Z. Fan, and S. Matsufuji, “Lower bounds on correlation of spreading sequence set with low or zero correlation zone,”Electron. Lett., vol. 36, no. 6, pp. 551–552, 2000

  31. [31]

    A new class of sequences with zero or low correlation zone based on interleaving technique,

    Z. Zhou, X. Tang, and G. Gong, “A new class of sequences with zero or low correlation zone based on interleaving technique,”IEEE Trans. Inf. Theory, vol. 54, no. 9, pp. 4267–4273, 2008

  32. [32]

    New constructions of zero- correlation zone sequences,

    Y .-C. Liu, C.-W. Chen, and Y . T. Su, “New constructions of zero- correlation zone sequences,”IEEE Trans. Inf. Theory, vol. 59, no. 8, pp. 4994–5007, 2013

  33. [33]

    Sequence design to minimize the weighted integrated and peak sidelobe levels,

    J. Song, P. Babu, and D. P. Palomar, “Sequence design to minimize the weighted integrated and peak sidelobe levels,”IEEE Trans. Signal Process., vol. 64, no. 8, pp. 2051–2064, 2016. 26

  34. [34]

    A coordinate-descent framework to design low PSL/ISL sequences,

    M. A. Kerahroodi, A. Aubry, A. De Maio, M. M. Naghsh, and M. Modarres-Hashemi, “A coordinate-descent framework to design low PSL/ISL sequences,”IEEE Trans. Signal Process., vol. 65, no. 22, pp. 5942–5956, 2017

  35. [35]

    Zak- transform-induced optimal sequences and their applications in OTFS,

    X. Peng, C. Wu, Z. Liu, C. Li, J. Zhang, X. Li, and P. Fan, “Zak- transform-induced optimal sequences and their applications in OTFS,” IEEE Trans. Commun., vol. 74, pp. 6008–6024, 2026

  36. [36]

    New design of sparse zero-correlation-zone sequence sets for optimal channel estimation in (generalized) spatial modulation systems,

    B. Shen, T. Yu, Z. Zhou, Y . Yang, and Z. Liu, “New design of sparse zero-correlation-zone sequence sets for optimal channel estimation in (generalized) spatial modulation systems,”IEEE Trans. Commun., vol. 74, pp. 3423–3436, 2026

  37. [37]

    Interference-avoidance pilot design using ZCZ sequences for multi-cell MIMO-OFDM systems,

    R. Zhang, X. Cheng, M. Ma, and B. Jiao, “Interference-avoidance pilot design using ZCZ sequences for multi-cell MIMO-OFDM systems,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Anaheim, Califor- nia, 2012, pp. 5056–5061

  38. [38]

    Zero correlation zone sequence pair sets for MIMO radar,

    L. Xu and Q. Liang, “Zero correlation zone sequence pair sets for MIMO radar,”IEEE Trans. Aerosp. Electron. Syst., vol. 48, no. 3, pp. 2100– 2113, 2012

  39. [39]

    Levanon and E

    N. Levanon and E. Mozeson,Radar signals. John Wiley & Sons, 2004

  40. [40]

    T. M. Cover,Elements of information theory. John Wiley & Sons, 1999

  41. [41]

    Constant composition distribution match- ing,

    P. Schulte and G. B ¨ocherer, “Constant composition distribution match- ing,”IEEE Trans. Inf. Theory, vol. 62, no. 1, pp. 430–434, 2016

  42. [42]

    Turbo equalization: Principles and new results,

    M. Tuchler, R. Koetter, and A. Singer, “Turbo equalization: Principles and new results,”IEEE Trans. Commun., vol. 50, no. 5, pp. 754–767, 2002

  43. [43]

    A zero-forcing approximate log-likelihood receiver for MIMO bit-interleaved coded modulation,

    M. Butler and I. Collings, “A zero-forcing approximate log-likelihood receiver for MIMO bit-interleaved coded modulation,”IEEE Commun. Lett., vol. 8, no. 2, pp. 105–107, 2004

  44. [44]

    ASIC implementation of soft- input soft-output MIMO detection using MMSE parallel interference cancellation,

    C. Studer, S. Fateh, and D. Seethaler, “ASIC implementation of soft- input soft-output MIMO detection using MMSE parallel interference cancellation,”IEEE J. Solid-State Circuits, vol. 46, no. 7, pp. 1754– 1765, 2011

  45. [45]

    J. G. Proakis and M. Salehi,Digital communications. McGraw-hill New York, 2001, vol. 4

  46. [46]

    Bandwidth efficient and rate-matched low-density parity-check coded modulation,

    G. B ¨ocherer, F. Steiner, and P. Schulte, “Bandwidth efficient and rate-matched low-density parity-check coded modulation,”IEEE Trans. Commun., vol. 63, no. 12, pp. 4651–4665, 2015

  47. [47]

    CVX: Matlab software for disciplined convex programming, version 2.1,

    M. Grant and S. Boyd, “CVX: Matlab software for disciplined convex programming, version 2.1,” 2014

  48. [48]

    On the distribution of the roots of certain symmetric matrices,

    E. P. Wigner, “On the distribution of the roots of certain symmetric matrices,”Annals of Mathematics, vol. 67, no. 2, pp. 325–327, 1958

  49. [49]

    On the sub-Gaussianity of the Beta and Dirichlet distributions,

    O. Marchal and J. Arbel, “On the sub-Gaussianity of the Beta and Dirichlet distributions,”Electronic Communications in Probability, vol. 22, no. none, pp. 1 – 14, 2017

  50. [50]

    Strictly subgaussian probabil- ity distributions,

    S. Bobkov, G. Chistyakov, and F. G ¨otze, “Strictly subgaussian probabil- ity distributions,”Electron. J. Prob., vol. 29, pp. 1–28, 2024

  51. [51]

    F. W. J. Olver, D. W. Lozier, R. F. Boisvert, and C. W. Clark, Eds., NIST Handbook of Mathematical Functions. Cambridge University Press, 2010

  52. [52]

    Optimal sub-Gaussian variance proxy for 3-mass distributions,

    S. Atouani, O. Marchal, and J. Arbel, “Optimal sub-Gaussian variance proxy for 3-mass distributions,”arXiv preprint, 2025. [Online]. Available: https://arxiv.org/abs/2510.06132

  53. [53]

    K. H. Rosen,Discrete Mathematics and Its Applications, 8th ed. New York, NY , USA: McGraw-Hill, 2019