Cognitive Radios Equipped With Modulation and STBC Recognition Over Coded Transmissions
Why this work is in the frame
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Bibliographic record
Abstract
Signal recognition has recently emerged as a key ingredient for cognitive radios with defense and industrial uses. This letter area encompasses the recognition of a broad range of transmission aspects such as modulation format, space-time mapping, channel coding form, central frequency, and data rate. Each of these aspects has been extensively researched in the literature. Only a few works addressed co-recognition. To the best of the authors’ knowledge, there is a single study that is dedicated to the recognition of combined modulation and space-time block coding (STBC), with the constraints of having more receive antennas than transmit antennas and operating over frequency-flat channels. In this letter, we propose a novel algorithm that recognizes modulation and STBC simultaneously over unknown frequency-selective channels while relaxing the requirement of having more antennas at the receiver than at the transmitter. The mathematical treatments demonstrate how an iterative expectation-maximization strategy is simply used to create a maximum-likelihood solution for this problem. Additionally, we make use of soft information coupled with channel decoders to enhance the proposed algorithm’s recognition performance. The proposed design also incorporates the supplementary task of channel estimation as a part of its overall structure. According to the findings of the computational complexity analysis and simulation results, the proposed algorithm easily defeats the one documented in the literature.
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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 | 0.000 | 0.001 |
| 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