Subsampling multi-standard receiver design for cognitive radio systems
Bibliographic record
Abstract
Introduction 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. A software-defined radio (SDR) is a radio that can accommodate a significant range of RF bands and air interface modes through software [1]. An ideal SDR receiver will sample and digitize the RF signals as close as possible to the receiver antenna.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".