Cognitive networking of large scale wireless 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
We propose the concept of cognitive networking for large-scale wireless systems, which opportunistically utilises network resources including both spectrum bandwidth and radio availability. Both types of resources cannot be predetermined in large-scale wireless systems, due to various reasons such as interferences and dynamic traffic load. The proposed technology not only establishes dynamic wireless networks, but also provides for reliable network quality of services (QoS). The supporting network architecture, embedded wireless interconnect (EWI), is proposed to implement the cognitive networking concept and supply an effective application-programming interface for large-scale data management systems. Two example applications are presented, including wireless mesh networks for broadband wireless internet access and wireless sensor networks for target tracking. Major advantages of the technology are further discussed. We suggest that the performance of the proposed system would improve with larger network scale and the implementation complexity could be independent of the network scale.
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.
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 | 0.001 | 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 | 0.001 | 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