Cross-Band Interference Reduction Trade-Offs in SISO and MISO OFDM-Based Cognitive Radios
Bibliographic record
Abstract
Cognitive radio is a promising approach for efficient utilization of radio spectrum. Due to its high spectral efficiency and flexibility, OFDM is considered as a good signaling scheme for cognitive radios. In this paper, we investigate the problem of cross-band interference minimization in OFDM-based cognitive systems. Cross-band interference is mainly caused by high OFDM sidelobes. In the first part of our work, we propose a framework to study the trade-off between two recently proposed techniques, adaptive symbol transition which is performed in the time domain, and active interference cancellation which is performed in the frequency domain. We use the trade-off study results to maximize the useful data rate for a desired level of interference. Simulation results show that the best trade-off depends on the configuration of spectral opportunities. In the second part, a new method for interference reduction in multiple-antenna cognitive systems is developed. We show that with knowledge of the channel, the secondary transmitted sequences can be jointly optimized over multiple antennas such that the interference at the primary receiver location is better minimized. Computer simulations demonstrate an improvement of almost 10 dB compared to separate-antenna optimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".