Analysis and rate optimization of GFDM‐based cognitive radios
Bibliographic record
Abstract
Abstract Generalized frequency division multiplexing (GFDM) is suitable for cognitive radio networks due to its low out‐of‐band emission and high spectral efficiency. In this paper, we thus consider the use of GFDM to allow an unlicensed secondary user (SU) to access a spectrum hole. However, in extremely congested spectrum scenarios, both active incumbent primary users on the left and right channels of the spectrum hole will experience out‐of‐band interference. While constraining this interference, we thus investigate the problem of power allocation to the SU transmitter to maximize the overall data rate where the SU receiver is employing a matched filter or zero‐forcing receiver. The power allocation problem is thus solved as a classic convex optimization problem. Finally, total transmission rate of GFDM is compared with that of orthogonal frequency division multiplexing. For instance, when right and left interference should be below 10 dBm, the capacity gain of GFDM over OFDM is 400%.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".