Reactive cognitive radio algorithms for co-existence between IEEE 802.11b and 802.16a networks
Bibliographic record
Abstract
This paper investigates the use of reactive cognitive radio algorithms to enable co-existence between IEEE 802.11b and 802.16a networks in the same unlicensed band. In particular, we develop a system model in which the two wireless systems share radio resources in frequency, space and time, and reactive coordination methods are used to reduce the mutual interference and improve link throughput. Reactive cognitive radio schemes utilize the available degrees of freedom in frequency, power and time, and react to observations in these dimensions to avoid interference. Dynamic frequency selection (DFS) enables radios to choose the band with the least interference. power control (PC) allows communications at the least possible transmit power. Time agility (TA) enables radios to adapt to each other's traffic patterns and avoid increasing interference in poor channel conditions. Simulation results are given for the following scenarios: (i) single 802.16a cell with single 802.11b hotspot; (ii) multiple 802.16a cells with multiple 802.11b hotspots. The results demonstrate that reactive cognitive radio schemes can provide significant improvements in 802.11b and/or 802.16a throughputs in the typical operating scenarios considered.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".