Carrier Frequency Offset Mitigation in OFDM Systems Using Efficient Tone Reservation
Bibliographic record
Abstract
One of the major drawbacks of orthogonal frequency division multiplexing (OFDM) is sensitivity to carrier frequency offset (CFO) caused by a mismatch between the transmitter and receiver oscillators. CFO destroys the orthogonality of the subcarriers and introduces intercarrier interference (ICI), reducing the system performance. Previously, tone reservation (TR) was considered for reducing the peak interference-to-carrier ratio (PICR) and shown to provide significant PICR reduction. In this paper, we use submatrices of the inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) to reduce the number of variables in the optimization to efficiently compute the optimal tones. Performance results are presented which demonstrate that the proposed algorithm yields PICR performance similar to that with the previous TR technique.
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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 | 0.000 | 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 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".