About the practicality of using partially overlapping channels in IEEE 802.11 b/g networks
Bibliographic record
Abstract
IEEE 802.11 WLANs are currently one of the most popular wireless technologies, but their immediate success results in dense deployments and high demand of user traffic. This in turn leads to decrease in throughput and poor spectrum utilization. Especially in the 2.4 GHz ISM band, where the spectrum is a very scarce resource, all available WLAN channels should be exploited in the best possible way to achieve higher utilization. One way to reach this goal is the usage of partially overlapping channels (POC). Most of the previous work related to POC is based on two major studies addressing 802.11 b, but none of them evaluates the POC behavior in the 802.11 g networks. Moreover, most of the previous results are based on simulations. The main contribution of this work is an experimental evaluation of POC in 802.11g networks. In this paper we confirm quantitatively that 802.11b reacts as expected from the previous studies, while 802.11 g reacts entirely different to the presence of adjacent channel interference. That leads to the conclusion that the usage of POC for 802.11g is not recommended.
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 | 0.018 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".