On the Planning Problem of Wireless Local Area Networks with Directional Antennas
Bibliographic record
Abstract
Wireless local area networks (WLANs) are more deployed today than ever before. However, this rapid growth led to uncoordinated deployment and planning tools are rarely used. In this paper, we first propose a mathematical model to solve the WLAN planning problem considering directional antennas. The problem consists of selecting the location of the access points in the network and selecting the transmit power, the antenna type and its orientation as well as the communication channel of each one. The use of interfering channels is allowed to solve the frequency allocation problem. Since the problem is NP-hard, we propose a two-step tabu-based metaheuristic algorithm to find solutions satisfying a radio coverage constraint, a throughput constraint and an interference level constraint. Our objective is to minimize the cost of the network and to distribute evenly the resources amongst the users. The aim of the first step of the algorithm is to obtain a solution that satisfies all the constraints while the second step focuses on optimizing the previously obtained solution. A new neighborhood, based on radio coverage of an access point, is proposed. The results show that solutions can be obtained with the proposed approach for real-size instances of the problem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".