ASCENT: A Context-Aware Spectrum Coexistence Design and Implementation Toolset for Policymakers in Satellite Bands
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces ASCENT (context-Aware Spectrum Coexistence DEsigN and ImplemenTation) toolset, an advanced context-aware terrestrial-satellite spectrum sharing toolset designed for researchers, policy-makers, and regulators. It serves two essential purposes: (a) evaluating the potential for harmful interference to primary users in satellite bands and (b) facilitating the analysis, design, and implementation of diverse regulatory policies on spectrum usage and sharing. Notably, ASCENT implements a closed-loop feedback system that allows dynamic adaptation of policies according to a wide range of contextual factors (e.g., weather, buildings, summer/winter foliage, etc.) and feedback on the impact of these policies through realistic simulation. Specifically, ASCENT comprises the following components- (i) interference evaluation tool for evaluating interference at the incumbents in a spectrum sharing environment while taking the underlying contexts; (ii) dynamic spectrum access (DSA) framework for providing context-aware instructions to adapt networking parameters and control secondary terrestrial network’s access to the shared spectrum band according to context-aware prioritization; (iii)Context broker to acquire essential and relevant contexts from external context-information providers; and (iv) DSA Database to store dynamic and static contexts and the regulator’s policy information. The closed-loop feedback system of ASCENT is implemented by integrating these components in a modular software architecture. A case study of sharing the lower $12 \mathbf{G H z ~ K u}$-band (12.2-12.7 $\mathbf{G H z}$) with the 5G terrestrial cellular network is considered, and the usability of ASCENT is demonstrated by dynamically changing exclusion-zone’s radius in different weather conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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