Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Random ISAC signals have a hidden deterministic 'iceberg' in their autocorrelation, and OFDM is the unique modulation that keeps it lowest for QAM/PSK under Nyquist pulse shaping.

desk verdict Solid new decomposition and pulse-design tool for random ISAC waveforms, but the proof that OFDM is the unique optimal modulation basis has a real gap in Appendix B that should be fixed before publication. read the letter →

arxiv 2501.01721 v1 pith:US77FY4D submitted 2025-01-03 eess.SP cs.ITmath.IT

Fan Liu , Yifeng Xiong , Shihang Lu , Shuangyang Li , Weijie Yuan , Christos Masouros , Shi Jin , Giuseppe Caire This is my paper · ORCID
classification eess.SPcs.ITmath.IT
keywords integratedsensingandcommunicationsrandomISACsignalsauto-correlationfunctionrangingsidelobeOFDMpulseshapingcoherentintegrationkurtosis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that the ranging performance of a communication signal pressed into sensing service is not random at all in expectation: the average squared autocorrelation decomposes into a deterministic 'iceberg' set by the pulse-shaping filter and a 'sea level' set by the randomness of the data symbols. For zero-mean, zero-pseudo-variance constellations (which covers most QAM/PSK but not BPSK or 8-QAM) under Nyquist pulse shaping, the paper proves that OFDM is the unique modulation basis achieving the lowest ranging sidelobe at every lag, while super-Gaussian constellations make single-carrier modulation optimal. Because coherent integration suppresses the sea level by a factor of M, the paper argues the long-run sensing limit is set by the pulse's own autocorrelation, and it proposes a convex 'iceberg shaping' design for Nyquist pulses that lowers sidelobes in a chosen delay region. The practical stake is that 6G systems can reuse OFDM data payloads for radar-like ranging without a dedicated sensing waveform, and can tune the pulse to where sidelobes matter.

What carries the argument

The load-bearing object is the frequency-domain representation of the autocorrelation: after oversampling by $L$, the pulse enters through its squared spectrum $g_n$, and the Nyquist (folded-spectrum) condition pins $g_{(L-1)N+n}=1-g_n$. The modulation basis enters only through the unistochastic matrix $\tilde{\mathbf{V}}=\mathbf{V}\odot \mathbf{V}^*$ (entrywise squared modulus of a unitary), and the Iceberg Theorem expresses the sea level in terms of how $\tilde{\mathbf{V}}$ mixes the vector $\tilde{\mathbf{g}}_k\odot\tilde{\mathbf{f}}_{k+1}^*$. The optimality proofs use majorization: because the $\ell^2$ norm is Schur-convex, any bistochastic averaging can only shrink (sub-Gaussian) or grow (super-Gaussian) the sea level, and equality holds only for permutation-like $\tilde{\mathbf{V}}$, which is exactly OFDM up to phase and permutation.

What would settle it

Simulate the average squared autocorrelation for OFDM and single-carrier signals with BPSK (or 8-QAM) symbols, using $N=128$, oversampling $L=10$, and an RRC pulse with $\alpha=0.35$: if at any nonzero lag the single-carrier sidelobe is below the OFDM sidelobe, the 'OFDM is uniquely optimal for QAM/PSK' claim fails for those constellations; the same simulation with 16-QAM should reproduce the theorem and validate the pipeline.

Watch

Extended reading notes

Core claim

The central discovery is the Iceberg Theorem: for i.i.d. symbols drawn from a unit-power, zero-mean, zero-pseudo-variance constellation, sent through an orthonormal modulation basis and a Nyquist pulse, the expected squared autocorrelation at lag $k$ is $$\mathbb{E}(|R_k|^2)=N|\tilde{\mathbf{f}}_{k+1}^H \tilde{\mathbf{g}}_k|^2+\|\tilde{\mathbf{g}}_k\|^2+(\mu_4-2)N\|\tilde{\mathbf{V}}(\tilde{\mathbf{g}}_k\odot \tilde{\mathbf{f}}_{k+1}^*)\|^2,$$ where $\tilde{\mathbf{g}}_k$ encodes the folded spectrum of the pulse, $\tilde{\mathbf{V}}$ is the entrywise-squared unitary that carries the modulation basis, and $\mu_4$ is the constellation kurtosis. The first term is the squared mean of $R_k$ and equals the squared autocorrelation of the pulse alone, the iceberg; the remaining terms are the variance from random data, the sea level. From this identity the paper proves that for sub-Gaussian constellations (kurtosis below 2, which includes PSK and QAM), OFDM is the only basis that minimizes the sea level at every lag, hence the lowest ranging sidelobes; for super-Gaussian constellations the single-carrier basis is optimal. Coherent integration over $M$ independent symbol blocks reduces the sea level by $1/M$, so after enough integrations the iceberg alone governs ranging, which motivates shaping the pulse's autocorrelation directly.

Load-bearing premise

The whole proof rests on the data symbols having zero mean and zero pseudo-variance, $\mathbb{E}(s)=\mathbb{E}(s^2)=0$, which is false for BPSK and 8-QAM; without that assumption the closed-form and the OFDM optimality claim are not established.

Editorial extensions

If this is right

  • For QAM/PSK payloads and any Nyquist pulse, OFDM gives the lowest average ranging sidelobe among all orthonormal bases, so existing OFDM-based 6G waveforms need no change to get the best sensing sidelobe floor.
  • Coherently combining M matched-filter outputs cuts the variance ('sea level') by a factor M, so the ranging sidelobe floor after many transmissions is set by the pulse autocorrelation, not the random data.
  • When OFDM carries PSK symbols the sea level vanishes, and the average squared autocorrelation is exactly the autocorrelation of the pulse, meaning the sensing response is fully deterministic.
  • For super-Gaussian constellations the ordering reverses, and single-carrier modulation becomes the best basis, so the optimal waveform depends only on whether the excess kurtosis is negative or positive.
  • Nyquist pulses with larger roll-off generate stronger periodic ripples in the sea level, while a sinc pulse gives a flat sea level, and the mainlobe width is $(1+\alpha)/B$.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The theorem's domain excludes BPSK and 8-QAM because they violate the zero-pseudo-variance assumption; a direct corollary of the proof structure is that one should derive a generalized fourth-moment formula for these constellations before claiming OFDM optimality for all PSK/QAM.
  • The iceberg-shaping optimization moves sidelobe energy out of a chosen delay window, so a natural extension is a region-adaptive pulse that trades a protected range interval against an allowed sidelobe budget elsewhere.
  • Because the sea level is the only component that depends on symbol randomness, another testable extension is to randomize only the phases of a constant-envelope waveform: the theorem predicts this should behave like PSK and drain the sea entirely under OFDM.
  • If a future standard uses a modulation basis that is only approximately orthogonal, the unistochastic structure of $\tilde{\mathbf{V}}$ suggests the sea level will be raised by the amount of cross-talk among basis vectors; quantifying that sensitivity is outside this paper but follows from the same expression.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper analyzes the sensing performance of random ISAC signals, characterized by the expected squared periodic ACF under an arbitrary orthonormal modulation basis and a Nyquist pulse-shaping filter. The main result, Theorem 1, expresses E(|R_k|^2) as an 'iceberg' term (the squared ACF of the pulse) plus a 'sea-level' term governed by the constellation kurtosis and the entrywise-squared unitary modulation matrix. Corollaries 1-3 give the mainlobe level and the M-fold variance reduction under coherent integration. The paper then claims in Theorem 2 that for sub-Gaussian constellations (e.g., QAM/PSK satisfying the zero-pseudo-variance assumption), OFDM is the unique modulation basis that minimizes the ranging sidelobe at every lag, and in Theorem 3 that SC is optimal for super-Gaussian constellations. Based on the iceberg geometry, the authors propose a convex 'iceberg shaping' pulse design. Numerical examples with RRC pulses and ranging simulations are used to validate the analysis.

Significance. If the optimality result were rigorously established, Theorem 2 would be an important and somewhat surprising design guideline: within the considered class of unitarily equivalent modulation bases, OFDM is singled out as the best (and only) choice for ranging sidelobe suppression, independent of the specific Nyquist pulse. The closed-form decomposition in Theorem 1 is the paper's strongest contribution; it is plausible, clearly structured, and consistent with the numerical curves. The convex pulse-shaping formulation is a useful practical outcome. However, the uniqueness proof for Theorem 2 rests on an equality condition in Appendix B that is false in general (see Major Comment 1), so the central optimality claim is not yet established. The paper also contains a normalization inconsistency in the statement of Theorem 1 that must be corrected.

major comments (3)
  1. [Appendix B, Eq. (68)] The claim that equality in ||\tilde{V}b_R||^2 <= ||b_R||^2 and ||\tilde{V}b_I||^2 <= ||b_I||^2 holds if and only if \tilde{V} is a permutation matrix is false when the vector being transformed has repeated entries. For example, with N=8, L=2 and lag k=4, \tilde{g}_{n,4}=1 for every Nyquist pulse and \tilde{f}^*_{k+1} has entries {1,-j,-1,j} each repeated twice; both b_R and b_I then contain repeated values. A block-Hadamard unitary that averages within each repeated-value pair produces a unistochastic matrix \tilde{V} that is not a permutation matrix but preserves both b_R and b_I, so equality holds in (68). Thus the per-lag uniqueness assertion used to prove Theorem 2 is invalid. The authors need to repair the proof, for example by establishing that a non-permutation \tilde{V} that attains the bound for some k must fail to attain it for some other k, or by qualifying the theorem's uniqueness claim.
  2. [Theorem 1, Eqs. (26)-(27)] The definition of \tilde{f}_{k+1} in (27) as 'the first N entries of f_{k+1}' is inconsistent with the scaling in (26). Since f_{k+1} is a column of F_{LN}, its entries are normalized by 1/\sqrt{LN}; with this definition one obtains N|\tilde{f}_{k+1}^H \tilde{g}_k|^2 = (1/L)|\Sigma_n \tilde{g}_{n,k} e^{j2\pi k(n-1)/(LN)}|^2, whereas the derivation in (59)-(60) and Corollary 1 require this term to equal |\Sigma_n \tilde{g}_{n,k} e^{j2\pi k(n-1)/(LN)}|^2. The definition should be corrected, e.g., \tilde{f}_{k+1,n} = e^{-j2\pi k(n-1)/(LN)}/\sqrt{N}, or the coefficient in (26) should be changed to LN. This is a notational error in the central formula that should be fixed.
  3. [Abstract and Theorem 2] The statement that the result applies to 'QAM/PSK constellations' overstates the scope actually proven. Assumption 2 (E(s)=0 and E(s^2)=0) excludes BPSK and 8-QAM, as the authors themselves note after (2). Since Lemma 2 and Theorem 1 rely on this assumption, the abstract, Theorem 2, and the conclusion should explicitly restrict to constellations satisfying Assumption 2 (e.g., proper QAM/PSK, excluding BPSK and 8-QAM).
minor comments (5)
  1. [Throughout] There are several typos, including 'staitionary' (Sec. III-C), 'ainticipated' and 'perofrmance' (Introduction), 'Sqaured' and 'Integartion' in Fig. 1, and 'Integartion' in Fig. 3.
  2. [Sec. IV-C3, Eq. (47)] The optimization problem (47) cites constraints '(71)-(74)', but those equation numbers belong to Appendix C; the intended constraints are (43)-(46).
  3. [Sec. V-C] References to subfigures appear as 'Fig. ??' twice; the authors should insert the correct figure references.
  4. [Appendix A, Lemma 2] Lemma 2 is stated without proof, citing the authors' unreviewed arXiv preprint [26]. Given the paper's reliance on this lemma, including its proof or a peer-reviewed reference would improve self-containedness.
  5. [Sec. IV-A] The sentence following Theorem 2 claims that uniqueness is 'guaranteed, as being detailed in the proof'; this is not supported once the Appendix B equality condition is corrected, so the sentence should be revised to match the actual proof.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the closed-form expression and the OFDM/SC optimality claims are derived from the stated model, and the one self-citation (Lemma 2) is independent, parameter-free support.

full rationale

Walking the derivation chain: Assumptions 1 and 2 fix the symbol statistics; Lemma 1 is the standard DFT diagonalization of circulant matrices; equations (20)-(25) manipulate the oversampled circulant model into the frequency-domain ACF representation; Lemma 2, cited from the authors' prior preprint [26], supplies the fourth-moment matrix S = E(vec(ss^H) vec(ss^H)^H) for i.i.d. zero-mean, zero-pseudo-variance symbols; substituting S into (50) produces Theorem 1 exactly as stated. The iceberg/sea decomposition in (26) is the definitional identity E|R_k|^2 = |E(R_k)|^2 + var(R_k), but the paper's claimed new content is the explicit evaluation of both terms in terms of the modulation basis U, the constellation kurtosis mu_4, and the pulse spectrum g_k; that evaluation is not assumed from the conclusion. Theorem 2 and Theorem 3 are proved in Appendices B and C from the sea-level expression obtained in Theorem 1, not imported as conclusions from [26]; the use of [26] is for Lemma 2 and for context. Lemma 2 is parameter-free in the sense that it does not involve U, k, or p, and its assumptions do not include the target result, so under the stated rules it counts as independent evidence rather than circular reliance. No parameter is fitted to the numerical demonstrations; the curves evaluate the closed forms with standard constellation kurtosis values. The only substantive concern is a correctness risk in Appendix B's equality condition when g_k \odot f^*_{k+1} has repeated entries; that is a validity question, not a circularity of the derivation chain. Therefore no circular step is exhibited.

Assumptions & free parameters 0 free parameters · 6 assumptions · 0 invented entities

There are no fitted constants; N, L, alpha, and the constellation kurtosis mu_4 are all inputs. The paper's central claim rests on the moment lemma imported from the authors' own preprint [26], the Nyquist folded-spectrum assumption, and the discrete oversampling model. The proof of OFDM optimality uses a majorization argument whose equality condition is not fully established.

assumptions (6)
  • domain assumption Symbols are i.i.d. with unit power and zero pseudo-variance, Assumptions 1 and 2, Eqs. (1) and (2).
    This is the statistical model behind the closed-form ACF expression; it excludes BPSK and 8-QAM, so the abstract overclaims coverage for all QAM/PSK.
  • domain assumption The Nyquist pulse has roll-off factor alpha <= 1 and satisfies the folded-spectrum criterion g_{(L-1)N+n} = 1 - g_n, Eq. (24).
    Needed to obtain the compact ACF representation in Eq. (25) and to guarantee ISI-free communication.
  • domain assumption The transmitted continuous waveform is well approximated by its oversampled discrete sequence with integer oversampling ratio L, and all targets lie on the sampling grid.
    Used in Eqs. (7) to (9); fractional-delay ranging performance is only approximated, not exactly analyzed.
  • standard math Lemma 2 from [26] gives the fourth-order moment matrix S = E(vec(ss^H)vec(ss^H)^H).
    Reproduced from an overlapping-author preprint and not re-proven here; the central theorem depends on it.
  • ad hoc to paper The convex relaxation from unistochastic to bistochastic matrices is tight, and equality in the Schur-convex norm bound forces a permutation matrix.
    Used in Appendix B to prove uniqueness of OFDM; the equality condition is asserted without handling degenerate b_R or b_I vectors.
  • domain assumption Standard ISAC modeling assumptions: white Gaussian noise, CP longer than maximum delay, and matched filtering via periodic time shifts.
    These define the received signal model in Eq. (11) and the ACF metric in Eq. (14).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals." pith.science (2026). https://pith.science/paper/US77FY4D

@misc{pith2026250101721,
  author       = {Pith},
  title        = {Pith review of: Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/US77FY4D}},
  note         = {Machine review of arXiv:2501.01721}
}
read the original abstract

Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance.

Figures

Figures reproduced from arXiv: 2501.01721 by the authors.

Figure 1
Figure 1. The average squared ACF and its coherent integration version of an [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. The average squared ACF of SC, CDMA, and OFDM signals, with [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. The average squared ACF of SC and OFDM signals under [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: The average squared ACFs with 250,000 coherent integrations under [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: The range estimation performance and profiles of two targets under [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: The range estimation performance and profiles of two targets under [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ISAC Imaging by Channel State Information using Ray Tracing for Next Generation 6G

    eess.SP 2025-09 conditional novelty 4.0 of 10

    A two-segment optimization maps per-path delays and angles to equivalent reflection points, yielding 3D object point clouds in 6.75 GHz multi-bounce ray-tracing simulations.

Reference graph

Works this paper leans on

32 extracted references · 21 canonical work pages · cited by 1 Pith paper

  1. [26]

    OFDM achieves the lowest ranging sidelobe under random ISAC signaling

    F. Liu, Y . Zhang, Y . Xiong et al. (2024) “OFDM achieves the lowest ranging sidelobe under random ISAC signaling”. [Online]. Available: https://arxiv.org/abs/2407.06691

    arXiv 2024

  2. [1]

    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. Surveys Tuts. , vol. 25, no. 2, pp. 868–904, Secondquarter 2023

    work page 2023

  3. [2]

    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 Network, vol. 34, no. 3, pp. 134–142, 2019

    2019

  4. [3]

    Integrated sensing and communi- cations: Toward dual-functional wireless networks for 6G and beyond,

    F. Liu, Y . Cui, C. Masouros et al. , “Integrated sensing and communi- cations: Toward dual-functional wireless networks for 6G and beyond,” IEEE J. Sel. Areas Commun. , vol. 40, no. 6, pp. 1728–1767, 2022

    work page 2022

  5. [4]

    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

    2023

  6. [5]

    Enabling joint communica- tion and radar sensing in mobile networks—A survey,

    J. A. Zhang, M. L. Rahman, K. Wu et al., “Enabling joint communica- tion and radar sensing in mobile networks—A survey,” IEEE Commun. Surveys Tuts., vol. 24, no. 1, pp. 306–345, 2022

    work page 2022

  7. [6]

    A shared cluster-based stochastic channel model for integrated sensing and communication systems,

    Y . Liu, J. Zhang, Y . Zhang, Z. Yuan, and G. Liu, “A shared cluster-based stochastic channel model for integrated sensing and communication systems,” IEEE Trans. Veh. Technol. , vol. 73, no. 5, pp. 6032–6044, 2024

    work page 2024

  8. [7]

    Integrated sensing and commu- nication channel: Measurements, characteristics, and modeling,

    J. Zhang, J. Wang, Y . Zhang et al. , “Integrated sensing and commu- nication channel: Measurements, characteristics, and modeling,” IEEE Commun. Mag., vol. 62, no. 6, pp. 98–104, 2024

    work page 2024

Show all 32 references
  1. [8]

    On outage-based beamforming design for dual-functional radar-communication 6G systems,

    A. Bazzi and M. Chafii, “On outage-based beamforming design for dual-functional radar-communication 6G systems,” IEEE Trans. Wireless Commun., vol. 22, no. 8, pp. 5598–5612, 2023

    2023

  2. [9]

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

    Z. Wei, H. Qu, Y . Wang et al., “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

    2023

  3. [10]

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

    Z. Wei, Y . Wang, L. Ma et al. , “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

    2023

  4. [11]

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

    Y . Xiong, F. Liu, Y . Cuiet al., “On the fundamental tradeoff of integrated sensing and communications under Gaussian channels,” IEEE Trans. Inf. Theory, vol. 69, no. 9, pp. 5723–5751, 2023

    2023

  5. [12]

    Input distribution optimization in OFDM dual-function radar-communication systems

    Y . Zhang, S. Aditya, and B. Clerckx. (2023) “Input distribution optimization in OFDM dual-function radar-communication systems”. [Online]. Available: https://arxiv.org/abs/2305.06635

    2023 arXiv

  6. [13]

    Fundamental trade-offs in monostatic ISAC: A holistic investigation towards 6G

    M. F. Keskin, M. M. Mojahedian, J. O. Lacruz et al. (2024) “Fundamental trade-offs in monostatic ISAC: A holistic investigation towards 6G”. [Online]. Available: https://arxiv.org/abs/2401.18011

    2024 arXiv

  7. [14]

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

    Z. Du, F. Liu, Y . Xiong et al. , “Reshaping the ISAC tradeoff under OFDM signaling: A probabilistic constellation shaping approach,” IEEE Trans. Signal Process., 2024, early access

    2024

  8. [15]

    Waveform design and signal processing aspects for fusion of wireless communications and radar sensing,

    C. Sturm and W. Wiesbeck, “Waveform design and signal processing aspects for fusion of wireless communications and radar sensing,” Proc. IEEE, vol. 99, no. 7, pp. 1236–1259, Jul. 2011

    2011

  9. [16]

    MIMO-OFDM joint radar-communications: Is ICI friend or foe?

    M. F. Keskin, H. Wymeersch, and V . Koivunen, “MIMO-OFDM joint radar-communications: Is ICI friend or foe?” IEEE J. Sel. Topics Signal Process., vol. 15, no. 6, pp. 1393–1408, 2021

    2021

  10. [17]

    Joint radar-communication with cyclic prefixed single carrier waveforms,

    Y . Zeng, Y . Ma, and S. Sun, “Joint radar-communication with cyclic prefixed single carrier waveforms,” IEEE Trans. Veh. Technol., vol. 69, no. 4, pp. 4069–4079, 2020

    2020

  11. [18]

    Low ambiguity zone: Theoretical bounds and Doppler-resilient sequence design in integrated sensing and communication systems,

    Z. Ye, Z. Zhou, P. Fan et al. , “Low ambiguity zone: Theoretical bounds and Doppler-resilient sequence design in integrated sensing and communication systems,” IEEE J. Sel. Areas Commun. , vol. 40, no. 6, pp. 1809–1822, 2022

    2022

  12. [19]

    On the effective- ness of OTFS for joint radar parameter estimation and communication,

    L. Gaudio, M. Kobayashi, G. Caire, and G. Colavolpe, “On the effective- ness of OTFS for joint radar parameter estimation and communication,” IEEE Trans. Wireless Commun. , vol. 19, no. 9, pp. 5951–5965, 2020

    2020

  13. [20]

    Integrated sensing and communications with MIMO-OTFS: ISI/ICI exploitation and delay- Doppler multiplexing,

    M. F. Keskin, C. Marcus, O. Eriksson et al. , “Integrated sensing and communications with MIMO-OTFS: ISI/ICI exploitation and delay- Doppler multiplexing,” IEEE Trans. Wireless Commun. , vol. 23, no. 8, pp. 10 229–10 246, 2024

    2024

  14. [21]

    Code-division OFDM joint communication and sensing system for 6G machine-type communication,

    X. Chen, Z. Feng, Z. Wei, P. Zhang, and X. Yuan, “Code-division OFDM joint communication and sensing system for 6G machine-type communication,” IEEE Internet Things J. , vol. 8, no. 15, pp. 12 093– 12 105, 2021

    2021

  15. [22]

    Integrated sensing and communication system via dual-domain waveform superposition,

    D. Tagliaferri, M. Mizmizi, S. Mura et al. , “Integrated sensing and communication system via dual-domain waveform superposition,” IEEE Tran. Wireless Commun., vol. 23, no. 5, pp. 4284–4299, 2024

    2024

  16. [23]

    From OTFS to DD-ISAC: Integrating sensing and communications in the delay Doppler domain,

    W. Yuan, L. Zhou, S. K. Dehkordi et al. , “From OTFS to DD-ISAC: Integrating sensing and communications in the delay Doppler domain,” IEEE Wireless Commun., pp. 1–9, 2024

    2024

  17. [24]

    Affine frequency division multiplexing for next generation wireless communications,

    A. Bemani, N. Ksairi, and M. Kountouris, “Affine frequency division multiplexing for next generation wireless communications,” IEEE Trans. Wireless Commun., vol. 22, no. 11, pp. 8214–8229, 2023

    2023

  18. [25]

    Integrated sensing and communications with affine frequency division multiplexing,

    ——, “Integrated sensing and communications with affine frequency division multiplexing,” IEEE Wireless Commun. Lett. , vol. 13, no. 5, pp. 1255–1259, 2024

    2024

  19. [27]

    Fractional delay and doppler estimation for OTFS based ISAC systems,

    O. Zacharia and M. Vani Devi, “Fractional delay and doppler estimation for OTFS based ISAC systems,” in IEEE Wireless Commun. Netw. Conf. (WCNC), 2023, pp. 1–6

    2023

  20. [28]

    Pulse shaping for random ISAC signals: The ambiguity function between symbols matters

    Z. Liao, F. Liu, S. Li et al. (2024) “Pulse shaping for random ISAC signals: The ambiguity function between symbols matters”. [Online]. Available: https://arxiv.org/abs/2407.15530

    2024 arXiv

  21. [29]

    The capacity of discrete-time memoryless rayleigh-fading channels,

    I. Abou-Faycal, M. Trott, and S. Shamai, “The capacity of discrete-time memoryless rayleigh-fading channels,” IEEE Trans. Inf. Theory, vol. 47, no. 4, pp. 1290–1301, 2001

    2001

  22. [30]

    The noncoherent Rician fading channel-part I: Structure of the capacity-achieving input,

    M. Gursoy, H. Poor, and S. Verdu, “The noncoherent Rician fading channel-part I: Structure of the capacity-achieving input,” IEEE Trans. Wireless Commun., vol. 4, no. 5, pp. 2193–2206, 2005

    2005

  23. [31]

    Noncoherent Rician fading channel-part II: Spectral efficiency in the low-power regime,

    ——, “Noncoherent Rician fading channel-part II: Spectral efficiency in the low-power regime,” IEEE Trans. Wireless Commun. , vol. 4, no. 5, pp. 2207–2221, 2005

    2005

  24. [32]

    J. G. Proakis and M. Salehi, Digital Communications . McGraw-hill, 2008

    2008

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.