Machine-to-Machine Communications in Cognitive Cellular Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine-to-machine (M2M) communication systems consist of massive autonomous devices which hold the ability to sense, communicate, and process information ubiquitously. The major challenges posed by M2M devices are spectrum scarcity, spectrum inefficiency, and network overload. Endowing M2M devices with cognitive radio technology can be a promising solution to these challenges. This paper presents an overview of cognitive M2M communication. We present cognitive cellular system architecture for M2M communication and discuss the possible transmission scenarios. In addition, the paper offers a spectrum sensing and resource allocation framework for cognitive M2M communication. Focus is given to lightweight selective compressive sensing and energy efficient resource allocation. Finally, the paper is supported by simulation results showing the benefits offered by cognitive cellular systems for M2M communications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it