Pith. sign in

REVIEW 4 major objections 5 minor 40 references

The paper introduces an open-source simulation framework and a 25-million-frame, ~200 TB synthetic radio dataset with 100 modulation classes, designed to supply the scale that large AI models need for spectrum sensing.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

A new open-source simulator and a characterized 200TB, 25M-frame synthetic radio dataset with 100 modulation classes, intended to train AI models for spectrum sensing.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection A genuinely large and well-specified synthetic RF dataset plus an open-source generator, but the paper's central Sim2Real bridging claim is asserted rather than tested—worth refereeing, not yet worth believing. the 4 major comments →

arxiv 2508.19552 v1 pith:TW2AGQII submitted 2025-08-27 eess.SP

CSRD2025: A Large-Scale Synthetic Radio Dataset for Spectrum Sensing in Wireless Communications

Shuo Chang , Rui Sun , Jiashuo He , Sai Huang , Kan Yu , Zhiyong Feng This is my paper
classification eess.SP
keywords synthetic radio datasetspectrum sensinglarge AI modelsmodulation classificationray tracing channel modelRF impairmentsCOCO object detectionsignal metadata
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 remove the data bottleneck for training large AI models in wireless spectrum sensing by offering a way to generate synthetic radio data at unprecedented scale. It introduces an open-source, modular simulator that models the full transmit-receive chain: 100 modulation types, statistical and ray-traced channels from real map data, transmitter and receiver impairments, and multi-antenna links. Using it, the authors characterize CSRD2025, roughly 25 million frames and 200 TB of passband IQ data, about 10,000 times larger than the 2018 benchmark, with per-signal ground-truth metadata and COCO-format spectrogram annotations for object detection. If the simulation realism holds, this would give spectrum-sensing researchers a training resource comparable in scale to the image and text corpora behind large models.

Core claim

The central discovery is a dataset, not a theorem: synthetic RF data can be manufactured at the scale and diversity required by scaling laws. CSRD2025 contains 100 modulation classes—analog, single-carrier, multi-carrier OFDM/SCFDMA, and OTFS—with frame lengths varying from 2,000 to 4,000,000 samples and one to four transmitters and receivers; 10% of scenarios are ray-traced through map-derived 3D environments. Every frame carries exhaustive JSON ground truth (modulation, timing, bandwidth, channel, impairments, per-signal SNR), and a conversion pipeline turns IQ data into spectrograms with COCO bounding boxes. The claimed payoff is that this is the first benchmark large enough and diverse e

What carries the argument

The load-bearing mechanism is the CSRD simulator's modular frame-by-frame pipeline: an engine samples scenario parameters from JSON configuration files, instantiates modulators, event scheduling, transmitter and receiver impairment models, and either statistical or ray-traced channels, then archives IQ plus metadata. The two features that do the real work for scale are the stochastic event controller that places one to three signal segments per transmitter with controlled spectral overlap, and the perfect ground-truth generator that derives COCO annotations from simulation parameters, making supervised time-frequency detection possible without hand labeling.

Load-bearing premise

The whole Sim2Real value rests on the assumption that the simulated channels, RF impairments, and SNR calculations faithfully reproduce real over-the-air signal behavior; the paper provides no measurement collected from real hardware or over the air to test that transfer.

What would settle it

Take a fixed test set of real over-the-air recordings with known modulation types and SNRs, then train a detector and classifier on a CSRD2025 subset. If accuracy does not improve with dataset size, or is no better than the same model trained on a far smaller existing synthetic benchmark, the scale-driven Sim2Real claim fails. A minimal check would compare per-class precision on real captures against performance on held-out simulated frames.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Training and evaluation of spectrum-sensing models can move from tens of gigabytes to hundreds of terabytes, approaching the dataset sizes that large-model scaling laws call for.
  • Object-detection models can be applied directly to spectrograms for joint time-frequency localization and modulation classification using the provided COCO annotations.
  • The standardized 8:1:1 frame-index splits make results across different studies directly comparable.
  • Users can generate custom datasets of arbitrary scale and parameter distributions by editing the configuration files, so the framework extends beyond the specific CSRD2025 instance.
  • The 25 ray-traced environment types provide site-specific propagation diversity that statistical fading alone cannot capture.

Where Pith is reading between the lines

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

  • Editorial inference: If the simulation fidelity holds, CSRD2025 could support pre-training a spectrum foundation model whose representations transfer to narrow real-world sensing tasks with only a small amount of labeled over-the-air data—the endgame of the authors' three-stage strategy, which they leave implicit.
  • Editorial inference: The tiered class distribution, with abundant common modulations and sparse high-order and OTFS variants, may create a long-tail learning problem; models may need class-balanced sampling or augmentation to avoid bias, a property worth testing directly.
  • Editorial inference: The COCO annotations could also be used to benchmark time-frequency segmentation or few-shot novel-class detection, tasks the paper does not explore but that the same labels would support.
  • Editorial inference: A direct testable extension is to measure accuracy on a small real-world radio capture benchmark after training on CSRD2025 subsets; the Sim2Real claim would be supported if performance degrades gracefully as channel and impairment fidelity are reduced in ablation.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper introduces ChangShuoRadioData (CSRD), a modular MATLAB-based simulation framework for generating synthetic RF data, and presents CSRD2025, a benchmark instance claimed to contain over 25,000,000 recorded frames (~200 TB) of passband IQ data from about 10,000,000 simulation scenarios. The dataset is claimed to cover 100 modulation classes, statistical and OSM-based ray-tracing channel models, diverse transmitter/receiver RF impairments, COCO-format spectrogram annotations, and standardized 8:1:1 train/validation/test splits. The authors position CSRD2025 as a Stage-1 synthetic resource engineered to 'bridge the Sim2Real gap' for spectrum-sensing large AI models. The paper contains no model training or transfer experiments; its central evidence is the described generation pipeline and internal distributional statistics.

Significance. If the claims are substantiated, this could be a valuable community resource: the stated scale (≈200 TB, 25M frames) is orders of magnitude larger than RadioML 2018.01A, and the combination of 100 modulation classes, passband signal representation, impairment modeling, OSM ray tracing, and COCO annotations could support a range of spectrum-sensing tasks. The framework's config-driven design, use of SigMF-like metadata, and fixed frame-index splits are strengths for reproducibility. However, the significance currently rests on two unvalidated pillars: (1) the 'high fidelity' and 'Sim2Real gap' claims are asserted on the basis of simulation components rather than demonstrated against over-the-air or hardware-in-the-loop data, and (2) the dataset itself is not hosted, so the central artifact cannot be independently inspected. The paper therefore reads as a promising resource description rather than a demonstrated benchmark.

major comments (4)
  1. [§III.B and §IV.A (Abstract)] The abstract and Section III.B claim that CSRD2025 is 'specifically engineered to bridge the Sim2Real gap' and that the pipeline provides 'high-fidelity' data. No experiment in the paper tests transfer to real radio environments: there are no OTA recordings, no hardware-in-the-loop measurements, no calibration of the impairment ranges in Table II to specific hardware, and no evaluation of models trained on CSRD2025 against real-world datasets such as RadioML 2018.01A or SPREAD. The internal arithmetic is consistent, but the load-bearing premise that synthetic features match physical signal structure is unvalidated. This is the central claim of the paper, so it should be supported, or the claims should be scaled back to 'intended to bridge the Sim2Real gap'.
  2. [Footnote 1, §IV.F] The paper describes CSRD2025 as a large-scale dataset benchmark, but footnote 1 states that the 200 TB dataset is not hosted for direct download and can only be fully reproduced using the framework, configurations, and fixed random seeds. No commit hash, version tag, checksum manifest, or exact generator environment is given, and no representative subset is provided. This makes the central artifact inaccessible for independent verification or immediate use. A dataset paper should either host a usable subset (e.g., 100 GB–1 TB) or provide a precise, versioned, executable recipe plus a manifest of emitted files, so readers can reproduce the corpus and confirm the claimed 25M-frame/200TB statistics.
  3. [Table II and Fig. 10 (§IV.E)] There is an unresolved inconsistency between the generation parameters and the reported signal-duration distribution. Table II states a symbol rate of 30–50 kHz and 500–2000 symbols per segment, which gives a maximum per-segment duration of about 0.067 s. Figure 10, however, reports signal durations extending to roughly 0.8 s and a long tail beyond that. If 'signal duration' includes multiple segments with idle intervals or is measured differently from 'segment duration', that needs to be stated precisely; otherwise the reader cannot tell which parameter bounds actually generated the data. This matters because the variable frame length and burst structure are advertised as key features.
  4. [§IV.E] Several dataset statistics, especially in Fig. 10 and the accompanying text, are said to be derived from a 'representative sample' or 'sample data' without specifying the sample size, the sampling procedure, or the fraction of the 25M frames covered. Since the full dataset is not publicly accessible, these summary statistics are the only evidence of the corpus's internal distribution. For a benchmark paper, the authors should either report full-corpus statistics or provide a rigorous sampling plan with confidence intervals. I also note the absence of any proof-of-concept evaluation (e.g., AMC accuracy, object-detection mAP on the COCO annotations) that would demonstrate label correctness and practical usability of the dataset.
minor comments (5)
  1. [§III.B.4 (Ray tracing)] The text says OSM map files for '25 distinct geographic locations' are used, then mentions 'downloading 10 representative 2km x 2km examples for each category' with nine named categories. This is inconsistent: 9 categories × 10 examples = 90 files, not 25. Clarify the number of environments and examples actually included.
  2. [§IV.C, Listing 1] The example metadata shows a MasterClockRate of 1.11e6 Hz while the dataset includes signal bandwidths up to ~800 kHz. Without down-conversion or band-pass filtering details, this seems to violate Nyquist. The authors should comment on the relationship between master clock, carrier frequency, and reported bandwidth in the metadata example.
  3. [Data and Code Access] The phrase 'open-source framework' should be qualified: the implementation uses MATLAB and the Communications Toolbox, so reproducibility depends on commercially licensed software. State the exact dependencies, toolbox versions, and a license for the framework code. The author footnote describing an equal contribution as 'mainly for supplying the MATLAB license' is not appropriate for a scientific paper and should be removed.
  4. [§IV.D] The COCO annotation pipeline is not fully specified: the STFT window, FFT size, hop length, and any scaling/gain normalization need to be stated so that bounding-box coordinates and spectrogram image sizes can be reproduced exactly by third parties.
  5. [Throughout] There are minor presentation issues: 'Barcelon `es' in the Fig. 5 caption, the Fig. 7 axis labels are difficult to read, and there is no complete machine-readable list of the 100 modulation classes apart from the figure. Please include a table of all classes in an appendix and fix the typographical issues.

Circularity Check

0 steps flagged

No circularity: dataset construction is direct simulation; fidelity claims are unvalidated but not circular.

full rationale

CSRD2025 is a dataset/resource paper, not a derivation of predictions from fitted parameters. Its only quantitative identity is Eq. (1), Nframes = sum_i NRx,i, which is an accounting definition rather than a derived result. The headline scale claims (25M frames, ~200TB, 100 modulation classes) are direct arithmetic consequences of the stated simulation configuration and are not circular. The 'Sim2Real gap' language is an asserted design goal, not a conclusion derived from any equation; it rests on an unvalidated fidelity assumption, which is a missing-evidence or correctness concern rather than a circularity. The paper's self-citations ([8], [9]) appear in general motivation and are not load-bearing for the dataset's construction, statistics, or benchmarking claims. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported, and no ansatz is smuggled in via self-citation. Thus no specific circular step can be exhibited from the paper's own text.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

The paper introduces no new physical entities, forces, or mediators. Its central claims depend on simulation fidelity assumptions and hand-chosen configuration ranges rather than on fitted parameters. The free parameters are dataset design choices, not hidden fitted constants, but they still determine the character of the claimed dataset.

free parameters (3)
  • Reference configuration parameter ranges
    Ranges in Table II (symbol rate 30-50 kHz, noise figure 10-20 dB, IQ imbalance 0-5 dB/deg, PA/LNA IIP3 20-40 dBm, overlap probability 0.15, etc.) are hand-selected in the default config, not calibrated to real hardware or real spectrum occupancy, yet they determine the dataset's diversity and SNR distribution.
  • Number of simulation scenarios = 10,000,000
    Chosen to reach approximately 25M frames through the formula in Section IV.A; not derived from any external requirement.
  • Modulation class set and class distribution = 100 classes; common classes overrepresented
    The curated tiered class distribution in the default configuration is a design choice, not a sampling from a measured spectrum occupancy model.
axioms (4)
  • domain assumption MATLAB Communications Toolbox statistical channel and impairment models faithfully represent real-world RF propagation and hardware
    The 'high fidelity' and 'bridge the Sim2Real gap' claims rely entirely on this fidelity assumption, and no OTA or HIL validation is presented (Section III.B).
  • domain assumption Ray tracing from OSM building footprints produces physically valid site-specific channel impulse responses
    The OSM-based propagation is presented as more realistic, but the paper provides no comparison against measurements or a reference ray tracer (Section III.B.2).
  • domain assumption Ground-truth SNR computed relative to thermal noise before receiver impairments is a correct and useful label
    The SNR metadata is a key supervised label, but the definition and its relevance to real detectors are assumed rather than validated (Section III.B).
  • domain assumption Fixed random seeds in the released repository reproduce the same dataset across runs and MATLAB versions
    The paper relies on this for reproducibility, but gives no reproducibility report or version pinning for the simulation environment.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of CSRD2025: A Large-Scale Synthetic Radio Dataset for Spectrum Sensing in Wireless Communications." pith.science (2026). https://pith.science/paper/TW2AGQII

@misc{pith2026250819552,
  author       = {Pith},
  title        = {Pith review of: CSRD2025: A Large-Scale Synthetic Radio Dataset for Spectrum Sensing in Wireless Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TW2AGQII}},
  note         = {Machine review of arXiv:2508.19552}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

The development of Large AI Models (LAMs) for wireless communications, particularly for complex tasks like spectrum sensing, is critically dependent on the availability of vast, diverse, and realistic datasets. Addressing this need, this paper introduces the ChangShuoRadioData (CSRD) framework, an open-source, modular simulation platform designed for generating large-scale synthetic radio frequency (RF) data. CSRD simulates the end-to-end transmission and reception process, incorporating an extensive range of modulation schemes (100 types, including analog, digital, OFDM, and OTFS), configurable channel models featuring both statistical fading and site-specific ray tracing using OpenStreetMap data, and detailed modeling of realistic RF front-end impairments for various antenna configurations (SISO/MISO/MIMO). Using this framework, we characterize CSRD2025, a substantial dataset benchmark comprising over 25,000,000 frames (approx. 200TB), which is approximately 10,000 times larger than the widely used RML2018 dataset. CSRD2025 offers unprecedented signal diversity and complexity, specifically engineered to bridge the Sim2Real gap. Furthermore, we provide processing pipelines to convert IQ data into spectrograms annotated in COCO format, facilitating object detection approaches for time-frequency signal analysis. The dataset specification includes standardized 8:1:1 training, validation, and test splits (via frame indices) to ensure reproducible research. The CSRD framework is released at https://github.com/Singingkettle/ChangShuoRadioData to accelerate the advancement of AI-driven spectrum sensing and management.

Figures

Figures reproduced from arXiv: 2508.19552 by Jiashuo He, Kan Yu, Rui Sun, Sai Huang, Shuo Chang, Zhiyong Feng.

Figure 1
Figure 1. Figure 1: Proposed pragmatic three-stage strategy for constructing large-scale spectrum datasets. This approach balances feasibility [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Annotation complexity comparison: (a) Object detection [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the CSRD framework architecture, illustrating the modular components and simulation workflow for [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of diverse geographical environments sourced from OpenStreetMap data. The eight scenarios depicted are [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Examples of ray tracing simulation outputs in [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example spectrogram generated from CSRD2025 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of the number of unique modulation [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of signal instances per modulation class in [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of per-signal ground-truth SNR across [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distribution of signal duration and bandwidth in the [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗

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 · 34 canonical work pages · 3 internal anchors

  1. [1]

    Gpt-4 technical report,

    OpenAI, J. Achiam, S. Adler et al. , “Gpt-4 technical report,” 2024. [Online]. Available: https://arxiv.org/abs/2303.08774

    Pith/arXiv arXiv 2024

  2. [2]

    Deepseek-v3 technical report,

    DeepSeek-AI, A. Liu, B. Feng et al., “Deepseek-v3 technical report,”

    work page

  3. [3]

    Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,

    G. Team, P. Georgiev, V . I. Lei et al. , “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,” 2024. [Online]. Available: https://arxiv.org/abs/2403.05530

    Pith/arXiv arXiv 2024

  4. [4]

    Scaling laws for neural language models,

    J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” 2020. [Online]. Available: https://arxiv.org/abs/2001.08361

    Pith/arXiv arXiv 2020

  5. [5]

    BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation,

    J. Li, D. Li, C. Xiong, and S. C. H. Hoi, “BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA , ser. Proceedings of Machine Learning Research, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesv ´ari, G. N...

    work page 2022

  6. [6]

    Coca: Contrastive captioners are image-text foundation models,

    J. Yu, Z. Wang, V . Vasudevan, L. Yeung, M. Seyedhosseini, and Y . Wu, “Coca: Contrastive captioners are image-text foundation models,” 2022. [Online]. Available: https://arxiv.org/abs/2205.01917

    Pith/arXiv arXiv 2022

  7. [7]

    Large generative ai models for telecom: The next big thing?

    L. Bariah, Q. Zhao, H. Zou, Y . Tian, F. Bader, and M. Debbah, “Large generative ai models for telecom: The next big thing?” IEEE Communications Magazine, vol. 62, no. 11, pp. 84–90, 2024

    work page 2024

  8. [8]

    A fast multi-loss learning deep neural network for automatic modulation classification,

    S. Chang, Z. Yang, J. He, R. Li, S. Huang, and Z. Feng, “A fast multi-loss learning deep neural network for automatic modulation classification,” IEEE Transactions on Cognitive Communications and Networking , vol. 9, no. 6, pp. 1503–1518, 2023

    work page 2023

  9. [9]

    Joint signal detection and automatic modulation classification via deep learning,

    H. Xing, X. Zhang, S. Chang, J. Ren, Z. Zhang, J. Xu, and S. Cui, “Joint signal detection and automatic modulation classification via deep learning,” IEEE Transactions on Wireless Communications , vol. 23, no. 11, pp. 17 129–17 142, 2024

    work page 2024

  10. [10]

    Energy detection spectrum sensing under rf imperfections,

    A.-A. A. Boulogeorgos, N. D. Chatzidiamantis, and G. K. Karagiannidis, “Energy detection spectrum sensing under rf imperfections,” IEEE Transactions on Communications , vol. 64, no. 7, pp. 2754–2766, 2016

    work page 2016

  11. [11]

    A cmos spectrum sensor based on quasi- cyclostationary feature detection for cognitive radios,

    P. Sepidband and K. Entesari, “A cmos spectrum sensor based on quasi- cyclostationary feature detection for cognitive radios,” IEEE Transactions on Microwave Theory and Techniques , vol. 63, no. 12, pp. 4098–4109, 2015

    work page 2015

  12. [12]

    Machine learning techniques for cooperative spectrum sensing in cognitive radio networks,

    M. Karaputugala, K. W. Choi, N. Saquib, and E. Hossain, “Machine learning techniques for cooperative spectrum sensing in cognitive radio networks,” IEEE Journal on Selected Areas in Communications , vol. 31, no. 11, pp. 2209–2221, 2013

    work page 2013

  13. [13]

    Machine learning for spectrum sharing: A survey,

    F. R. V . Guimar˜aes, J. M. B. da Silva Jr., C. C. Cavalcante, G. Fodor, M. Bengtsson, and C. Fischione, “Machine learning for spectrum sharing: A survey,” Foundations and Trends® in Networking, vol. 14, no. 1-2, pp. 1–159, 2024. [Online]. Available: http://dx.doi.org/10.1561/1300000073

    work page doi:10.1561/1300000073 2024

  14. [14]

    Toolqa: A dataset for llm question answering with external tools,

    Y . Zhuang, Y . Yu, K. Wang, H. Sun, and C. Zhang, “Toolqa: A dataset for llm question answering with external tools,” in Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 36. Curran Associates, Inc., 2023, pp. 50 117–50 143. [Online]. Available: https://proceedings.neurips....

    work page 2023

  15. [15]

    A survey on llm-generated text detection: Necessity, methods, and future directions,

    J. Wu, S. Yang, R. Zhan, Y . Yuan, L. S. Chao, and D. F. Wong, “A survey on llm-generated text detection: Necessity, methods, and future directions,” Computational Linguistics , pp. 1–64, 02 2025. [Online]. Available: https://doi.org/10.1162/coli a 00549

    doi:10.1162/coli 2025

  16. [16]

    Wrist: Wideband, real-time, spectro-temporal rf identification system using deep learning,

    H. N. Nguyen, M. V omvas, T. D. V o-Huu, and G. Noubir, “Wrist: Wideband, real-time, spectro-temporal rf identification system using deep learning,” IEEE Transactions on Mobile Computing , vol. 23, no. 2, pp. 1550–1567, 2024

    work page 2024

  17. [17]

    Deep Learning Object Detection Approaches to Signal Identification

    L. Wood, K. Anderson, P. Gerstoft, R. Bell, R. Subbaraman, and D. Bharadia, “Deep learning object detection approaches to signal identification,” 2022. [Online]. Available: https://arxiv.org/abs/2210.16173

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  18. [18]

    Sigmf: The signal metadata format,

    B. Hilburn, N. West, T. O’Shea, and T. Roy, “Sigmf: The signal metadata format,” Proceedings of the GNU Radio Conference , vol. 3, no. 1, 2018. [Online]. Available: https://pubs.gnuradio.org/index.php/ grcon/article/view/52

    work page 2018

  19. [19]

    Microsoft coco: Common objects in context,

    T.-Y . Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision – ECCV 2014 , D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, pp. 740–755

    work page 2014

  20. [20]

    Top Choice: Get Your Certified Used N9040B UXA Signal Analyzer with Warranty - Keysight Technologies

    “Top Choice: Get Your Certified Used N9040B UXA Signal Analyzer with Warranty - Keysight Technologies.” [Online]. Avail- able: https://www.keysight.com/used/us/en/lp/spectrum-signal-analyzers/ n9040b-uxa-signal-analyzer-2hz-50ghz

    work page

  21. [21]

    Ettus Research - High performance Software Defined Radio (SDR)

    E. R. Brand, a National Instruments, “Ettus Research - High performance Software Defined Radio (SDR).” [Online]. Available: https://www.ettus.com/products/usrp-x-series/

    work page

  22. [22]

    Learning robust general radio signal detection using computer vision methods,

    T. O’Shea, T. Roy, and T. C. Clancy, “Learning robust general radio signal detection using computer vision methods,” in 2017 51st Asilomar Conference on Signals, Systems, and Computers , 2017, pp. 829–832

    work page 2017

  23. [23]

    A wideband signal recognition dataset,

    N. West, T. O’Shea, and T. Roy, “A wideband signal recognition dataset,” in 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021, pp. 6–10

    work page 2021

  24. [24]

    Boost spectrum prediction with temporal-frequency fusion network via transfer learning,

    K. Li, C. Li, J. Chen, Q. Zhang, Z. Liu, and S. He, “Boost spectrum prediction with temporal-frequency fusion network via transfer learning,” IEEE Transactions on Mobile Computing , vol. 22, no. 6, pp. 3209–3223, 2023

    work page 2023

  25. [25]

    Joint detection and classification of rf signals using deep learning,

    A. Vagollari, V . Schram, W. Wicke, M. Hirschbeck, and W. Gerstacker, “Joint detection and classification of rf signals using deep learning,” in 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) , 2021, pp. 1–7

    work page 2021

  26. [26]

    Vslm: Virtual signal large model for few-shot wideband signal detection and recognition,

    X. Hao, S. Yang, R. Liu, Z. Feng, T. Peng, and B. Huang, “Vslm: Virtual signal large model for few-shot wideband signal detection and recognition,” IEEE Transactions on Wireless Communications , vol. 24, no. 2, pp. 909–925, 2025

    work page 2025

  27. [27]

    RadDet: A Wideband Dataset for Real-Time Radar Spectrum Detection

    Z. Huang, S. Denman, A. Pemasiri, T. Martin, and C. Fookes, “Raddet: A wideband dataset for real-time radar spectrum detection,” 2025. [Online]. Available: https://arxiv.org/abs/2501.10407

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  28. [28]

    Intelligent detection algorithm of broadband communication signal based on spectral decomposition,

    Y . Dong, M. Ruipeng, H. Tao, C. Kaixin, W. Di, T. Zhifu, and W. Yanyun, “Intelligent detection algorithm of broadband communication signal based on spectral decomposition,” High Power Laser and Particle Beams, vol. 35, no. 6, pp. 069 001–9, 2023. [Online]. Available: https: //www.hplpb.com.cn/en/article/doi/10.11884/HPLPB202335.230024

    work page arXiv 2023

  29. [29]

    Colosseum: Large-scale wireless exper- imentation through hardware-in-the-loop network emulation,

    L. Bonati, P. Johari, M. Polese, S. D’Oro, S. Mohanti, M. Tehrani- Moayyed, D. Villa, S. Shrivastava, C. Tassie, K. Yoder, A. Bagga, P. Patel, V . Petkov, M. Seltser, F. Restuccia, A. Gosain, K. R. Chowdhury, S. Basagni, and T. Melodia, “Colosseum: Large-scale wireless exper- imentation through hardware-in-the-loop network emulation,” in 2021 IEEE Interna...

    work page 2021

  30. [30]

    Over-the-air deep learning based radio signal classification,

    T. J. O’Shea, T. Roy, and T. C. Clancy, “Over-the-air deep learning based radio signal classification,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 168–179, 2018

    work page 2018

  31. [31]

    An optimized faster region- based cnn for 1d spectrum sensing and signal identification in cluttered rf environments,

    T. Morehouse, C. Montes, and R. Zhou, “An optimized faster region- based cnn for 1d spectrum sensing and signal identification in cluttered rf environments,” in 2023 IEEE Future Networks World Forum (FNWF) , 2023, pp. 1–6

    work page 2023

  32. [32]

    Spectrogram data set for deep-learning-based rf frame detection,

    J. Wicht, U. Wetzker, and V . Jain, “Spectrogram data set for deep-learning-based rf frame detection,” Data, vol. 7, no. 12, 2022. [Online]. Available: https://www.mdpi.com/2306-5729/7/12/168

    work page 2022

  33. [33]

    Combined rf-based drone detection and classification,

    S. Basak, S. Rajendran, S. Pollin, and B. Scheers, “Combined rf-based drone detection and classification,” IEEE Transactions on Cognitive Communications and Networking , vol. 8, no. 1, pp. 111–120, 2022

    work page 2022

  34. [34]

    A framework for wireless technology classification using crowdsensing platforms,

    A. Scalingi, D. Giustiniano, R. Calvo-Palomino, N. Apostolakis, and G. Bovet, “A framework for wireless technology classification using crowdsensing platforms,” in IEEE INFOCOM 2023 - IEEE Conference on Computer Communications , 2023, pp. 1–10

    work page 2023

  35. [35]

    Finding waldo in the cbrs band: Signal detection and localization in the 3.5 ghz spectrum,

    N. Soltani, V . Chaudhary, D. Roy, and K. Chowdhury, “Finding waldo in the cbrs band: Signal detection and localization in the 3.5 ghz spectrum,” in GLOBECOM 2022 - 2022 IEEE Global Communications Conference , 2022, pp. 4570–4575

    work page 2022

  36. [36]

    Large Scale Radio Frequency Wideband Signal Detection & Recognition

    L. Boegner, G. Vanhoy, P. Vallance, M. Gulati, D. Feitzinger, B. Comar, and R. D. Miller, “Large scale radio frequency wideband signal detection & recognition,” 2022. [Online]. Available: https://arxiv.org/abs/2211.10335

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  37. [37]

    Hisarmod: A new challenging modulated signals dataset,

    K. Tekbıyık, C. Ke c ¸eci, A. R. Ekti, A. G ¨orc ¸in, and G. K. Kurt, “Hisarmod: A new challenging modulated signals dataset,” 2019. [Online]. Available: https://dx.doi.org/10.21227/8k12-2g70

    work page doi:10.21227/8k12-2g70 2019

  38. [38]

    Rml22: Realistic dataset generation for wireless modulation classification,

    V . Sathyanarayanan, P. Gerstoft, and A. E. Gamal, “Rml22: Realistic dataset generation for wireless modulation classification,” IEEE Trans- actions on Wireless Communications , vol. 22, no. 11, pp. 7663–7675, 2023

    work page 2023

  39. [162]

    12 888–12 900

    PMLR, 2022, pp. 12 888–12 900. [Online]. Available: https: //proceedings.mlr.press/v162/li22n.html

    work page 2022

  40. [2025]

    Available: https://arxiv.org/abs/2412.19437

    [Online]. Available: https://arxiv.org/abs/2412.19437

    Pith/arXiv arXiv

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.