Enhancing WLAN Capacity by Strategic Placement of Tetherless Relay Points
Bibliographic record
Abstract
With the proliferation of wireless local area network (WLAN) technologies, wireless Internet access via public hotspots will become a necessity in the near future. In outdoor areas where the installation of a large number of wired access points is practically or economically infeasible, mobile users located at the edge of the network communicate with the access point at a very low rate and, in turn, waste network resources. In this work, we promote the use of tetherless relay points (TRPs) to improve the throughput ot a WLAN in such environments. We first provide a high level description on how to integrate TRPs in a multirate WLAN architecture. We then propose an integer-programming optimization formulation and an iterative approach to compute the best placement of a fixed number of TRPs. Finally, we show in numerical analysis, through a case study based on relay-enabled rate adaptation and IEEE 802.11-like multirate physical model with Rayleigh fading, that, for a wide range of system parameters, significant performance gain can be achieved when TRPs are strategically installed in 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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".