Fast Selection of Indoor Wireless Transmitter Locations With Generalizable Neural Network Propagation Models
Bibliographic record
Abstract
The continuous emergence of new wireless communication systems increases the need for intelligent planning prior to their deployment. This planning includes determining the position of wireless access points (WAPs) to meet quality of service (QoS) objectives. How to effectively place a set of transmitters in an environment is a long-standing problem that has been widely studied over the years. This process has mainly relied on expensive measurement campaigns, low-fidelity empirical models, or high-fidelity but time-consuming simulations. Recent advances in scientific machine learning (ML) create new possibilities for overcoming this dichotomy between speed and accuracy. In this article, we train a deep neural network (U-Net) to predict the received signal strength (RSS) levels generated by multiple transmitters within a variety of different geometries. Then, we leverage the computational efficiency of the trained model to determine the position of transmitters in new geometries, both generated and real. This approach dramatically accelerates the process of selecting the position of access points (APs), meeting multiple optimization objectives in an efficient manner. Also, for the first time, we experimentally demonstrate the viability of a U-Net-based transmitter placement for an indoor Wi-Fi system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".