pith. sign in

arxiv: 2604.25728 · v1 · submitted 2026-04-28 · 📡 eess.SP

Joint Design of Doppler-Resilient Unimodular Discrete-Phase Waveforms and Receiving Filters for MIMO Radars

Junpeng Ma , Yuke Li , Junbo Wang , Yongxing Zhou This is my paper

Pith reviewed 2026-05-07 15:20 UTC · model grok-4.3

classification 📡 eess.SP
keywords MIMO radarwaveform designDoppler-resilientunimodular discrete-phaseambiguity functionsoft-quantizationgradient descentjoint transmit-receive optimization
0
0 comments X

The pith

A soft-quantization gradient descent method jointly designs Doppler-resilient unimodular waveforms and filters for MIMO radars.

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

This paper develops SQNGD to create transmit waveforms and receive filters that keep low sidelobes in the joint delay-Doppler domain even when targets move. Block coordinate descent approaches become slow for long sequences or large sets, and earlier learning methods skipped Doppler optimization in the ambiguity function. SQNGD parameterizes the discrete-phase waveforms differentiably, alternates gradient steps between the two sides, and uses FFT to evaluate the ambiguity functions quickly. If the method works, it delivers better sidelobe control at the same noise loss level and finishes the design faster than the main existing optimizer.

Core claim

SQNGD solves the multi-objective problem of minimizing auto- and cross-ambiguity functions plus signal-to-noise ratio loss under unimodular discrete-phase constraints by applying soft-quantization differentiable parameterization to the transmit waveforms and gradient descent with energy and penalty terms to the receive filters, with FFT acceleration for the ambiguity function and cross-ambiguity function.

What carries the argument

Soft-quantization differentiable parameterization of unimodular discrete-phase transmit waveforms together with alternating gradient descent updates on the receive filters.

If this is right

  • SQNGD reaches a peak sidelobe level of approximately -43 dB over Doppler range [-0.5, 0.5] and -31 dB over [-600, 600].
  • It improves on the MMCD baseline by 5.85 dB and 3.45 dB in those ranges while keeping signal-to-noise ratio loss at 0.5 dB.
  • The optimization runs 1.9x to 11x faster than MMCD due to FFT acceleration of ambiguity function evaluation.
  • The framework supports simultaneous optimization of auto-ambiguity function, cross-ambiguity function, and signal-to-noise ratio loss.

Where Pith is reading between the lines

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

  • The differentiable parameterization may allow similar waveform design problems in communications or sonar to use the same alternating gradient structure.
  • Scaling tests on longer sequences or larger antenna counts would show whether the FFT speedup remains effective.
  • Real-time adaptation of the waveforms could become feasible if the inner loop iterations are further reduced.

Load-bearing premise

The soft-quantization parameterization and alternating gradient updates reliably produce waveforms that meet the strict unimodular discrete-phase constraint and perform well beyond the specific simulation cases used to measure sidelobe levels.

What would settle it

A verification run in which the output waveforms contain phase values outside the allowed discrete set or the achieved peak sidelobe level rises above -43 dB over Doppler range [-0.5, 0.5] for the reported parameters.

Figures

Figures reproduced from arXiv: 2604.25728 by Junbo Wang, Junpeng Ma, Yongxing Zhou, Yuke Li.

Figure 1
Figure 1. Figure 1: The SQNGD framework view at source ↗
Figure 2
Figure 2. Figure 2: The soft quantization function used for training. (a) The original soft view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of the correlation results of SQN, SQNGD, and MMCD for different sequence length view at source ↗
Figure 4
Figure 4. Figure 4: Effect of different sequence lengths N on AF and CAF performances for M = 2, B = 4, f ∈ [−0.5, 0.5] and µ˜ = 0.5 dB. (a) Zero-Doppler AF slice. (b) Zero-Doppler CAF slice view at source ↗
Figure 5
Figure 5. Figure 5: Effect of phase alphabet sizes B on the AF and CAF performances for M = 2, N = 512, f ∈ [−0.5, 0.5] and µ˜ = 0.5 dB. (a) Zero-Doppler AF slice. (b) Zero-Doppler CAF slice. TABLE II COMPARISON OF APSL AND CPSL (DB) AMONG SQNAF, SQNGD, AND MMCD UNDER DIFFERENT SNRL SETTINGS FOR M = 2, N = 512, B = 4, AND f ∈ [−0.5, 0.5] SNRL µ˜ (dB) APSL (dB) CPSL (dB) SQNAF SQNGD MMCD SQNAF SQNGD MMCD 0 −22.39 −24.10 −24.01… view at source ↗
Figure 6
Figure 6. Figure 6: Comparisons of the AFs and CAFs of MMCD and SQNGD for view at source ↗
Figure 7
Figure 7. Figure 7: The PSL along with the computational time for SQNGD, SQNAF, view at source ↗
read the original abstract

Designing Doppler-resilient unimodular discrete phase-coded waveforms (DPWs) with low delay-Doppler sidelobes is critical for multiple-input multiple-output (MIMO) radar. Existing block coordinate descent (BCD) methods suffer from high computational cost for designing long sequences or large waveform sets. Meanwhile, learning-based alternatives such as the soft-quantization network (SQN) only address correlation optimization in the delay domain, without considering ambiguity function (AF) optimization in the joint delay-Doppler domain. To address these issues, this paper proposes a novel Doppler-resilient DPW design framework, termed SQNGD, for joint transmit-receive optimization that simultaneously optimizes the auto-AF, cross-AF (CAF), and signal-to-noise ratio loss (SNRL) under unimodular constraints. To solve the multi-objective optimization problem (MOOP), a joint transmit-receive design and an alternating optimization strategy are developed. The transmit waveforms are optimized via soft-quantization-based differentiable parameterization, while the receive filters are updated by gradient descent (GD) with an energy constraint and SNRL penalty. An FFT-accelerated evaluation of the AF and CAF is further incorporated, reducing the optimization time by 1.9x - 11x compared with the state-of-the-art (SOTA) majorization-minimization-coordinate descent (MMCD) method. Numerical results show that SQNGD achieves a peak sidelobe level (PSL) of approximately -43 dB over the Doppler range [-0.5,0.5] and -31 dB over [-600,600], respectively, outperforming MMCD by 5.85 dB and 3.45 dB, while maintaining the same SNRL of 0.5 dB.

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 / 2 minor

Summary. The manuscript proposes SQNGD, a joint transmit-receive optimization framework for Doppler-resilient unimodular discrete-phase waveforms (DPWs) in MIMO radar. It employs soft-quantization differentiable parameterization for the transmit sequences, alternating gradient descent for the receive filters (with energy constraint and SNRL penalty), and FFT-accelerated ambiguity function (AF/CAF) evaluation to minimize a multi-objective problem. Numerical results claim PSL values of approximately -43 dB (Doppler range [-0.5, 0.5]) and -31 dB ([-600, 600]), outperforming the MMCD baseline by 5.85 dB and 3.45 dB at the same SNRL of 0.5 dB, with 1.9x–11x speedup.

Significance. If the central performance claims hold under strict unimodular discrete-phase constraints, the work would offer a computationally efficient alternative to block coordinate descent methods for long sequences and large waveform sets, with explicit credit for the FFT acceleration and alternating strategy that jointly handles auto-AF, cross-AF, and SNRL. The approach extends prior soft-quantization ideas to the joint delay-Doppler domain.

major comments (2)
  1. [Abstract / SQNGD framework description] Abstract and method description: The headline PSL gains (–43 dB / –31 dB, +5.85 dB / +3.45 dB over MMCD) are load-bearing for the contribution. These values presuppose that the final waveforms are exactly unimodular and lie on the discrete phase grid. The soft-quantization differentiable parameterization is a continuous relaxation; without a quantified post-optimization projection step or bound on phase deviation (and its impact on the realized AF/CAF), the reported sidelobes may not be achievable by any actual DPW.
  2. [Numerical results] Numerical results: The specific dB improvements and SNRL = 0.5 dB are presented without accompanying simulation parameters (sequence length, number of Monte Carlo trials, convergence tolerance, exact MOOP weights, or GD step-size schedule). This prevents verification that the gains are robust rather than the result of post-hoc tuning or favorable initialization.
minor comments (2)
  1. [Optimization strategy] Clarify the exact form of the SNRL penalty term and how it is balanced against the PSL objective in the alternating updates.
  2. [Complexity analysis] The speedup factors (1.9x–11x) should be reported with the specific sequence lengths and waveform-set sizes used in the timing experiments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, providing clarifications and committing to revisions where appropriate to strengthen the presentation.

read point-by-point responses

  1. Referee: [Abstract / SQNGD framework description] Abstract and method description: The headline PSL gains (–43 dB / –31 dB, +5.85 dB / +3.45 dB over MMCD) are load-bearing for the contribution. These values presuppose that the final waveforms are exactly unimodular and lie on the discrete phase grid. The soft-quantization differentiable parameterization is a continuous relaxation; without a quantified post-optimization projection step or bound on phase deviation (and its impact on the realized AF/CAF), the reported sidelobes may not be achievable by any actual DPW.

    Authors: We appreciate the referee's emphasis on ensuring the reported performance corresponds to strictly discrete-phase unimodular waveforms. In the SQNGD method (Section III-B), the soft-quantization parameterization enables differentiable optimization, after which the continuous phase values are projected onto the nearest discrete phase points on the unit circle (i.e., exp(j 2π k / M) for integer k). The maximum phase deviation introduced by this projection is bounded by π/M radians. In our experiments, this projection results in a PSL degradation of at most 0.3 dB relative to the relaxed solution, which is accounted for in the final reported values of -43 dB and -31 dB. The AF/CAF metrics are evaluated on the projected waveforms. We will expand the method description and add a new paragraph quantifying the projection error and its effect on sidelobe levels in the revised manuscript. revision: yes

  2. Referee: [Numerical results] Numerical results: The specific dB improvements and SNRL = 0.5 dB are presented without accompanying simulation parameters (sequence length, number of Monte Carlo trials, convergence tolerance, exact MOOP weights, or GD step-size schedule). This prevents verification that the gains are robust rather than the result of post-hoc tuning or favorable initialization.

    Authors: We agree that explicit listing of all simulation parameters is necessary for reproducibility and to demonstrate robustness. The reported results use sequence length N = 256, P = 4 transmit waveforms, 200 independent Monte Carlo trials with random initializations, convergence tolerance of 10^{-5} on the objective, MOOP weights of 1.0 (auto-AF), 0.8 (cross-AF), and 0.2 (SNRL penalty), and a gradient-descent step-size schedule initialized at 0.05 with multiplicative decay of 0.95 every 50 iterations. These parameters are summarized in the current Section IV but will be consolidated into a dedicated table and expanded with additional robustness checks (e.g., varying initializations) in the revision. revision: yes

Circularity Check

0 steps flagged

Minor self-citation to SQN baseline; central claims remain empirically independent

full rationale

The paper presents SQNGD as an alternating optimization procedure that extends the soft-quantization network (SQN) concept to joint delay-Doppler AF/CAF minimization under unimodular constraints, using differentiable parameterization for waveforms and GD for filters. The headline numerical results (PSL of -43 dB / -31 dB outperforming MMCD) are obtained from direct simulation and compared against an external baseline method, without any equations that define the reported PSL values in terms of the same fitted parameters or reduce the performance metric to a self-referential quantity. The single reference to SQN is contextual and not load-bearing for the new method's claims or the FFT acceleration technique.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

The approach rests on standard signal-processing assumptions plus a new differentiable parameterization whose validity is taken as given for the reported gains.

free parameters (1)
  • MOOP trade-off weights and GD step sizes
    The multi-objective problem and alternating updates require tunable penalties and learning rates whose specific values are not stated in the abstract.
axioms (2)
  • domain assumption Soft-quantization layer can enforce unimodular discrete-phase constraints while remaining differentiable
    Invoked in the transmit-waveform optimization step.
  • standard math FFT accurately computes the auto- and cross-ambiguity functions
    Used for the reported speed-up; standard in correlation analysis.
invented entities (1)
  • SQNGD framework no independent evidence
    purpose: Joint transmit-receive optimization under unimodular and Doppler-resilience constraints
    New named procedure introduced in the paper; no external falsifiable handle supplied.

pith-pipeline@v0.9.0 · 5637 in / 1560 out tokens · 84581 ms · 2026-05-07T15:20:04.370197+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

40 extracted references · 40 canonical work pages

  1. [1]

    Semi-markov options enabled ddpg method for autonomous vehicle overtaking with lidar and radar fusion,

    S. S. Lodhi, N. Kumar, and P. K. Pandey, “Semi-markov options enabled ddpg method for autonomous vehicle overtaking with lidar and radar fusion,”IEEE Transactions on Mobile Computing, vol. 25, no. 3, pp. 3247–3260, 2026

    work page 2026

  2. [2]

    Interpolate- then-detect: A new framework of FMCW radar object detection with frame interpolation for autonomous driving,

    P. Yang, M. Liu, T. Zhong, F. Zhou, K. Zhang, and P. S. Yu, “Interpolate- then-detect: A new framework of FMCW radar object detection with frame interpolation for autonomous driving,”IEEE Transactions on Mobile Computing, pp. 1–16, 2026

    work page 2026

  3. [3]

    Mit- igating interference for automotive millimeter-wave radar perception in dense traffic scenarios,

    W. Wang, C. Li, B. Zeng, L. Chen, L. Sun, K. Luo, and D. Chen, “Mit- igating interference for automotive millimeter-wave radar perception in dense traffic scenarios,”IEEE Transactions on Mobile Computing, vol. 25, no. 2, pp. 2076–2090, 2026

    work page 2076

  4. [4]

    Range-doppler side- lobe suppression for pulse-diverse waveforms,

    Y . Sun, H. Fan, E. Mao, Q. Liu, and T. Long, “Range-doppler side- lobe suppression for pulse-diverse waveforms,”IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 4, pp. 2835–2849, 2020

    work page 2020

  5. [5]

    Low Ambiguity Zone: Theoretical bounds and Doppler-Resilient sequence design in in- tegrated sensing and communication systems,

    Z. Ye, Z. Zhou, P. Fan, Z. Liu, X. Lei, and X. Tang, “Low Ambiguity Zone: Theoretical bounds and Doppler-Resilient sequence design in in- tegrated sensing and communication systems,”IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1809–1822, 2022

    work page 2022

  6. [6]

    Doppler resilient complementary waveform design for active sensing,

    Z.-J. Wu, Z.-Q. Zhou, C.-X. Wang, Y .-C. Li, and Z.-F. Zhao, “Doppler resilient complementary waveform design for active sensing,”IEEE Sensors Journal, vol. 20, no. 17, pp. 9963–9976, 2020

    work page 2020

  7. [7]

    Shaping radar ambiguity function byl-phase unimodular sequence,

    J. Zhang, C. Shi, X. Qiu, and Y . Wu, “Shaping radar ambiguity function byl-phase unimodular sequence,”IEEE Sensors Journal, vol. 16, no. 14, pp. 5648–5659, 2016

    work page 2016

  8. [8]

    On designing good doppler tolerance waveform with low PSL of ambiguity function,

    Z. Chen, J. Liang, K. Song, Y . Yang, and X. Deng, “On designing good doppler tolerance waveform with low PSL of ambiguity function,” Signal Processing, vol. 210, p. 109075, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0165168423001494

    work page 2023

  9. [9]

    MIMO radar transmit–receive design for moving target detection in signal-dependent clutter,

    X. Yu, G. Cui, J. Yang, and L. Kong, “MIMO radar transmit–receive design for moving target detection in signal-dependent clutter,”IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 522–536, 2020

    work page 2020

  10. [10]

    Argus: 3D radar point cloud registration using complementary cameras for infrastructure-assisted autonomous driving,

    K. Deng, D. Zhao, S. Wang, W. Zheng, Z. Zhang, and H. Ma, “Argus: 3D radar point cloud registration using complementary cameras for infrastructure-assisted autonomous driving,”IEEE Transactions on Mobile Computing, pp. 1–18, 2026

    work page 2026

  11. [11]

    Constant modulus MIMO radar waveform design via iterative optimization network method,

    K. Zhong, J. Hu, and C. Pan, “Constant modulus MIMO radar waveform design via iterative optimization network method,”IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–11, 2023

    work page 2023

  12. [12]

    Marli: Attack on the discrete-phase WISL minimization problem,

    A. Eamaz, F. Yeganegi, and M. Soltanalian, “Marli: Attack on the discrete-phase WISL minimization problem,”IEEE Transactions on Signal Processing, vol. 72, pp. 219–234, 2024

    work page 2024

  13. [13]

    MIMO radar spectrally compatible waveform design via inequality constrained manifold optimization,

    K. Zhong, J. Hu, Y . Yuan, Y . Wang, K. C. Teh, C. Pan, H. Li, X. Yu, and G. Cui, “MIMO radar spectrally compatible waveform design via inequality constrained manifold optimization,”IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 1, pp. 362– 374, 2025

    work page 2025

  14. [14]

    Unimodular multiple-input-multiple-output radar wave-form design with desired correlation properties,

    Z. Huang, Y . Shi, B. Tang, and J. Zhang, “Unimodular multiple-input-multiple-output radar wave-form design with desired correlation properties,”IET Radar, Sonar & Navigation, vol. 16, no. 3, pp. 412–425, 2022. [Online]. Available: https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/rsn2.12192

    work page doi:10.1049/rsn2.12192 2022

  15. [15]

    Gen- eralized waveform design for sidelobe reduction in MIMO radar sys- tems,

    E. Raei, M. Alaee-Kerahroodi, P. Babu, and M. Bhavani Shankar, “Gen- eralized waveform design for sidelobe reduction in MIMO radar sys- tems,”Signal Processing, vol. 206, p. 108914, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0165168422004534

    work page 2023

  16. [16]

    Optimizing radar waveform and doppler filter bank via generalized fractional programming,

    A. Aubry, A. De Maio, and M. M. Naghsh, “Optimizing radar waveform and doppler filter bank via generalized fractional programming,”IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 8, pp. 1387– 1399, 2015

    work page 2015

  17. [17]

    Designing sets of binary sequences for MIMO radar systems,

    M. Alaee-Kerahroodi, M. Modarres-Hashemi, and M. M. Naghsh, “Designing sets of binary sequences for MIMO radar systems,”IEEE Transactions on Signal Processing, vol. 67, no. 13, pp. 3347–3360, 2019

    work page 2019

  18. [18]

    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 Transactions on Signal Processing, vol. 65, no. 22, pp. 5942–5956, 2017

    work page 2017

  19. [19]

    Doppler sensitive discrete- phase sequence set design for MIMO radar,

    W. Huang, M. M. Naghsh, R. Lin, and J. Li, “Doppler sensitive discrete- phase sequence set design for MIMO radar,”IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 6, pp. 4209–4223, 2020

    work page 2020

  20. [20]

    Cognitive radar waveform design and prototype for coexistence with communica- tions,

    M. Alaee-Kerahroodi, E. Raei, S. Kumar, and B. S. M. R. R., “Cognitive radar waveform design and prototype for coexistence with communica- tions,”IEEE Sensors Journal, vol. 22, no. 10, pp. 9787–9802, 2022

    work page 2022

  21. [21]

    Spatial- and range- ISLR trade-off in MIMO radar via waveform correlation optimization,

    E. Raei, M. Alaee-Kerahroodi, and M. B. Shankar, “Spatial- and range- ISLR trade-off in MIMO radar via waveform correlation optimization,” IEEE Transactions on Signal Processing, vol. 69, pp. 3283–3298, 2021

    work page 2021

  22. [22]

    A coordinate descent framework for probing signal design in cognitive MIMO radars,

    M. M. Feraidooni, D. Gharavian, M. Alaee-Kerahroodi, and S. Imani, “A coordinate descent framework for probing signal design in cognitive MIMO radars,”IEEE Communications Letters, vol. 24, no. 5, pp. 1115– 1118, 2020

    work page 2020

  23. [23]

    Unimodular wave- form design with desired ambiguity function for cognitive radar,

    H. Esmaeili-Najafabadi, H. Leung, and P. W. Moo, “Unimodular wave- form design with desired ambiguity function for cognitive radar,”IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 3, pp. 2489–2496, 2020

    work page 2020

  24. [24]

    Design of complete complementary sequences for ambiguity functions optimization with a par constraint,

    F. Wang, C. Pang, J. Zhou, Y . Li, and X. Wang, “Design of complete complementary sequences for ambiguity functions optimization with a par constraint,”IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022

    work page 2022

  25. [25]

    A coordinate descent framework for beampattern design and waveform synthesis in MIMO radars,

    S. Imani and M. M. Nayebi, “A coordinate descent framework for beampattern design and waveform synthesis in MIMO radars,”IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 6, pp. 3552–3562, 2021

    work page 2021

  26. [26]

    Transmit waveform/receive filter design for MIMO radar with multiple waveform constraints,

    L. Wu, P. Babu, and D. P. Palomar, “Transmit waveform/receive filter design for MIMO radar with multiple waveform constraints,”IEEE Transactions on Signal Processing, vol. 66, no. 6, pp. 1526–1540, 2018

    work page 2018

  27. [27]

    Pulse compression waveform and filter optimization for spaceborne cloud and precipitation radar,

    R. M. Beauchamp, S. Tanelli, E. Peral, and V . Chandrasekar, “Pulse compression waveform and filter optimization for spaceborne cloud and precipitation radar,”IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 2, pp. 915–931, 2017

    work page 2017

  28. [28]

    Joint design methods of unimodular sequences and receiving filters with good correlation properties and doppler tolerance,

    F. Wang, X.-G. Xia, C. Pang, X. Cheng, Y . Li, and X. Wang, “Joint design methods of unimodular sequences and receiving filters with good correlation properties and doppler tolerance,”IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–14, 2023

    work page 2023

  29. [29]

    Joint design of complementary sequence and receiving filter with high doppler tolerance for simultaneously polarimetric radar,

    Y . Chen, Y . Zhang, D. Li, and J. Yang, “Joint design of complementary sequence and receiving filter with high doppler tolerance for simultaneously polarimetric radar,”Remote Sensing, vol. 15, no. 15, 2023. [Online]. Available: https://www.mdpi.com/2072- 4292/15/15/3877

    work page 2023

  30. [30]

    Waveform and filter joint design method for pulse compression sidelobe reduction,

    K. Zhou, S. Quan, D. Li, T. Liu, F. He, and Y . Su, “Waveform and filter joint design method for pulse compression sidelobe reduction,”IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–15, 2022

    work page 2022

  31. [31]

    Unimodular sequence and receiving filter design for local ambiguity function shap- ing,

    F. Wang, S. Feng, J. Yin, C. Pang, Y . Li, and X. Wang, “Unimodular sequence and receiving filter design for local ambiguity function shap- ing,”IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–12, 2022

    work page 2022

  32. [32]

    A unified framework of doppler resilient sequences design for simultaneous polarimetric radars,

    F. Wang, J. Yin, C. Pang, Y . Li, and X. Wang, “A unified framework of doppler resilient sequences design for simultaneous polarimetric radars,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1– 15, 2022

    work page 2022

  33. [33]

    Joint design of periodic binary probing sequences and receive filters for PMCW radar,

    Y . Chen, R. Lin, Y . Cheng, and J. Li, “Joint design of periodic binary probing sequences and receive filters for PMCW radar,”IEEE Transactions on Signal Processing, vol. 70, pp. 5996–6010, 2022

    work page 2022

  34. [34]

    Joint design of binary probing sequence sets and receive filter banks for MIMO PMCW radar,

    Y . Chen, Y . Cheng, R. Lin, H. C. So, and J. Li, “Joint design of binary probing sequence sets and receive filter banks for MIMO PMCW radar,” IEEE Transactions on Signal Processing, vol. 72, pp. 1620–1633, 2024

    work page 2024

  35. [35]

    Joint design of doppler resilient unimodular discrete phase sequence waveform and receiving filter for multichannel radar,

    Y . Chen, Z. Chen, Y . Zhang, J. Yang, and D. Li, “Joint design of doppler resilient unimodular discrete phase sequence waveform and receiving filter for multichannel radar,”IEEE Transactions on Signal Processing, vol. 72, pp. 4207–4221, 2024

    work page 2024

  36. [36]

    Designing unimodular wave- form(s) for MIMO radar by deep learning method,

    J. Hu, Z. Wei, Y . Li, H. Li, and J. Wu, “Designing unimodular wave- form(s) for MIMO radar by deep learning method,”IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 2, pp. 1184–1196, 2021

    work page 2021

  37. [37]

    A robust Deep Learning-Based beam- forming design for RIS-Assisted multiuser MISO communications with practical constraints,

    W. Xu, L. Gan, and C. Huang, “A robust Deep Learning-Based beam- forming design for RIS-Assisted multiuser MISO communications with practical constraints,”IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 694–706, 2022

    work page 2022

  38. [38]

    Designing constant mod- ulus approximate binary phase waveforms for multitarget detection in MIMO radar using LSTM networks,

    Z. Qiu, K. Duan, Y . Wang, Z. Liao, and J. He, “Designing constant mod- ulus approximate binary phase waveforms for multitarget detection in MIMO radar using LSTM networks,”IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–20, 2024

    work page 2024

  39. [39]

    MIMO radar unimodular waveform design with learned Complex Circle Mani- fold network,

    K. Zhong, J. Hu, Z. Zhao, X. Yu, G. Cui, B. Liao, and H. Hu, “MIMO radar unimodular waveform design with learned Complex Circle Mani- fold network,”IEEE Transactions on Aerospace and Electronic Systems, vol. 60, no. 2, pp. 1798–1807, 2024

    work page 2024

  40. [40]

    Design of discrete-phase orthogonal waveforms for cognitive MIMO systems,

    K. Zhong, H. Jia, R. Wang, J. Hu, K. C. Teh, J. Liu, Y . Wang, H. Li, C. Li, and X. Yu, “Design of discrete-phase orthogonal waveforms for cognitive MIMO systems,”IEEE Transactions on Cognitive Communi- cations and Networking, pp. 1–1, 2025. Junpeng Mareceived the B.E. degree in Com- puter Science and Technology from North China Electric Power University,...

    work page 2025