Fast and robust identification of GSM and LTE signals
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
Signal identification algorithms have found many applications in both military and commercial communications, such as spectrum surveillance, and software-defined and cognitive radios. Such algorithms are essential for building instruments used in radio spectrum monitoring. In this paper, we present an algorithm to identify signals from global system for mobile communications (GSM) and long-term evolution (LTE) networks. The presented algorithm relies on the signal cumulative distribution function as an identification feature, and on the Kolmogorov-Smirnov test as the decision criteria. The performance of the identification algorithm is evaluated using standard cellular signals generated and acquired using test and measurement equipment. Experimental results verify the applicability of the algorithm with short observation intervals leading to improved response time of the instrument. Moreover, the presented algorithm does not require timing and frequency offset estimation and correction; therefore, it has low implementation complexity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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