Generative AI for Integrated Sensing and Communication: Insights From the Physical Layer Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As generative artificial intelligence (GAl) models continue to evolve, their generative capabilities are increasingly enhanced, and being used exten-sively in content generation. Furthermore, GAl also excels in data modeling and analysis, benefiting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems. Specifically, we first provide an overview of GAI and ISAC, touching on GAl's potential support across multi-ple layers of ISAC. We then thoroughly investigate GAl's applications in the physical layer, such as channel estimation, which demonstrates the benefits that GAl-enhanced physical layer technologies bring to ISAC systems. Finally, in the case study, we present a diffusion model-based method for estimating signal direction of arrival in near-field scenarios using uniform linear arrays with antenna spacing over half the wavelength. With a mean square error of 1.03 degrees, the method confirms GAl's support for the physical layer in near-field sensing and communications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it