Subsampling multi-standard receiver design for cognitive radio systems
Bibliographic record
Abstract
Cognitive radio (CR), as coined and defined by its first proponent, is the integration of model-based reasoning with software radio techniques [1]. An important aspect of cognitive radio is the efficient use of resources, especially the frequency spectrum, in a typical communication environment. Spectrum management requires spectrum sensing; the subsampling technique has been demonstrated to be an efficient approach for spectrum sensing for CR applications [2]. Cognitive radio technology, in the context of white space, has been discussed in Chapter 1 of this book. Situation awareness and learning capability are some of the features in a CR through which it becomes aware of the location, radio frequency (RF) environment, and updates its knowledge. Environmental information in a CR is typically provided by an in-built or network-enabled radio environment map (REM) through some learning process. A typical CR node consists of the RF front-end and configurable hardware and software platform. The current software-defined radio (SDR) platforms will facilitate the evolution of CR by adding cognitive and intelligent features to it with the help of cognitive engines (CE). Cognitive engines are essentially the software packages that facilitate the cognitive feature to an agile radio platform.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".