Improved active interference cancellation for sidelobe suppression in cognitive OFDM systems
Bibliographic record
Abstract
Active interference cancellation (AIC) is known to be a very effective technique for reducing the interference of OFDM sidelobes to primary licensed users in cognitive OFDM systems. However, AIC has some shortcomings such as high computational complexity and spectrum overshoot on the cancellation subcarriers. Spectrum overshoot is mainly caused due to unconstrained or unbalanced power allocation to the cancellation tones used in AIC. In this paper, we propose an improved AIC technique in which the problem of spectrum overshoot is tackled. We show that by a modification to the solution of the optimization problem involved in AIC, we can obtain a trade-off between the amount of spectrum overshoot and sidelobe suppression without increasing the system complexity. In particular, spectrum overshoot can be completely eliminated. Furthermore, simulations prove that with this modification, the peak spectral interference at the primary band is less than that of the AIC technique with a single power constraint.
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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 | 0.001 |
| 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 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".