Bibliographic record
Abstract
Interest in a new generation of engineering systems enabled with cognition, started with cognitive radio , a term that was coined by Mitola and McGuire (1999). In that article, the idea of cognitive radio was introduced within the software-defined radio (SDR) community. Subsequently, Mitola (2000) elaborated on a so-called “radio knowledge representation language” in his own doctoral dissertation. Furthermore, in a short section entitled “Research issues” at the end of his doctoral dissertation, Mitola went on to say the following: ‘How do cognitive radios learn best? merits attention’. The exploration of learning in cognitive radio includes the internal tuning of parameters and the external structuring of the environment to enhance machine learning. Since many aspects of wireless networks are artificial, they may be adjusted to enhance machine learning. This thesis did not attempt to answer these questions, but it frames them for future research. Then, in Haykin (2005a), the first journal paper on cognitive radio, detailed expositions of signal processing, control, learning and adaptive processes, and game-theoretic ideas that lie at the heart of cognitive radio were presented for the first time. Three fundamental cognitive tasks, embodying the perception–action cycle of cognitive radio, were identified in that 2005 paper: radio-scene analysis of the radio environment performed in the receiver; transmit-power control and dynamic spectrum management, both performed in the transmitter; and global feedback, enabling the transmitter to act and, therefore, control data transmission across the forward wireless (data) channel in light of information about the radio environment fed back to it by the receiver.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".