Embedding General Antenna Patterns in Machine Learning Based Propagation Models
Bibliographic record
Abstract
Designing an efficient wireless communication system requires careful planning for access point placement and antenna pattern selection. Extensive measurement campaigns can be performed to assess the signal levels for a specific antenna configuration. However, that process is time-consuming and expensive. Simulation methods such as ray-tracing can be used instead. Even though these methods are more cost-effective than measurement campaigns, changing the position of a transmitter or its pattern still necessitates new runs of the solver. This can quickly present a considerable computational load, especially in large venues such as stadiums, where large numbers of transmitting antennas are used. In this paper, we present a solution to this problem based on machine learning. Off-line synthetic (simulation) data are used to train an artificial neural network model. The model learns the relation between a set of features, including the antenna location and its pattern, with the received signal strength across a communication channel within a frequency bandwidth of interest. We show that the trained model is very accurate at predicting the signal levels for arbitrary transmitter locations and antenna patterns. That enables rapid and efficient selection of the optimal locations, number or patterns of the transmitting antennas, to meet any design objectives.
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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 | 0.000 |
| 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".