Performance analysis of channel assignment schemes for coordinated cognitive WLAN networks
Bibliographic record
Abstract
Nowadays local wireless networks are very used by an elevated number of people and its use continues rising. This provokes an increase of the interference and as consequence, congestion in the non-licensed band of 2.4GHz, caused by the increase of users in a given zone and thus, the limitation of channels offered by this band. As a solution to this problem, in this project it is studied the possibility of using additional channels from licensed bands used for other radio-communications services. The use of these channels in the local network (named in this context as secondary user of this spectrum) is done in an opportunistic manner, when ever it does not provoke any interference to the users of the service which have the rights to use this spectrum (named primary users in this context), since these ones have priority to access these bands of the spectrum. As a first approximation to the solution of the assignment problem, it is studied the behaviour of a random channel assignment in a scenario with a certain density of local networks that can generate interference between them and where the availability of the primary channel for opportunistic use is not homogeneous. Later, stated the possibility of improving the level of interference even with a random channel assignment, there are proposed two channel assignment methods designed to have in concern the availability of primary channels. The first method is set out through the formulation of an Integer Linear Programming (ILP) problem, which allows obtaining an optimal solution but with an elevated execution time. The other method is an heuristic one based on obtaining a Minimum Spanning Tree (MST) on interference terms, which allows to obtain near-optimal solutions in less time of execution. In the project it is done a detailed comparison of these two methods to contrast the advantages of each one. Finally, there are identified some aspects of the implementation of these methods in a real scenario.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".