Efficient Wireless Extension Point Placement Algorithm in Urban Rectilineal WLANs
Bibliographic record
Abstract
With a small amount of software modification, wireless extension points (EPs), which are now commercially available, can be used to improve the throughput capacity of a wireless local area network (WLAN). An EP is an immobile device that has access to the power supply or is equipped with a high-capacity battery but does not have direct access to the Internet. They wirelessly relay data between the access point and the mobile hosts. In this paper, we investigate the optimal placement of the EPs such that the throughput capacity of an urban rectilineal WLAN can be maximized. Two channel models that are based on the Shannon capacity bound and IEEE 802.11 specifications are studied. We first formulate a solution for the optimal EP placement problem in a general network scenario as a nonlinear programming problem; then, we propose an efficient algorithm that determines the optimal locations of a fixed number of EPs in a rectilineal network. Our results show that, for a wide range of system parameters, the optimally placed EPs can significantly increase the network throughput capacity. Moreover, we study how the number of EPs, transmission power, path loss exponent, channel models, and traffic characteristics affect the optimal EP placement and expected throughput capacity of the network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".