Identification of PCP-OFDM Signals at Very Low SNR for Spectrum Efficient Communications
Bibliographic record
Abstract
An adaptive Orthogonal Frequency Division Multiplexing (OFDM) system, with a preceded cyclic prefix (PCP), was proposed earlier to address the recent need of robust and flexible transmission technique in cognitive radio (CR) communications [1]. Identification of PCP-OFDM signals is therefore of great importance for the design of fair spectrum sharing mechanism, particularly at very low signal-to-noise ratio (SNR)when synchronization is not achievable. The preceded cyclic prefix, multiplexed with the data-carrying OFDM signals, provides one unique and recognizable feature of PCP-OFDM signals. In this paper, a robust PCP-OFDM signal identification technique is proposed under very low SNR in the presence of unknown timing and carrier frequency offset (CFO). Robust performance with very low false alarm probability and short sensing time was achieved under various channel conditions including Rician, Rayleigh and the additive white Gaussian noise (AWGN) channels.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".