A successive refinement approach to wireless infrastructure network deployment
Bibliographic record
Abstract
There has been a recent proliferation in wireless infrastructure network deployments. In a typical deployment, an installer uses either a one-time site survey or rules of thumb to place wireless access points and allocate them with channels and power levels. Because the access point location problem is inherently complex and one that requires tradeoffs among competing requirements, these approaches can result in either dead spots or significant unintended interference among wireless access points. This degrades network performance for end clients, with throughput reduction factors of 4x found in field measurements. In this paper, we take a first step towards improving client performance by coordinating choices of channels and power levels at wireless access points using a successive refinement approach. Our contributions are two-fold: first, we develop a mathematical model that crisply defines the solution space and identifies the characteristics of an optimal channel and power-level configuration. Second, we present heuristics that, under some simplifying assumptions, yield near-optimal configurations. We use Monte Carlo simulations to evaluate the performance of our heuristics. We find that the choice of heuristics for transmit power control impacts performance more than the channel allocation strategy, especially at high densities. Also, surprisingly, randomly assigning channels to access points appears to be an effective strategy at higher deployment densities. Taken together, we believe that this study paves the way to designing rapidly deployable real-world infrastructure networks that also have good performance
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".