Indoor Localization Using Channel State Information With Regression Artificial Neural Networks
Bibliographic record
Abstract
In this paper, the Channel State Information (CSI) is used to locate mobile stations in an indoor environment. The novelty of our technique is to use multiple packets of CSI for each location without feature extraction to provide a reach fingerprint. Two different mapping algorithms are investigated and compared with each other in terms of location accuracy and precision. In the first approach, the collected CSIs are fed to a multilayer perceptron (MLP) as input features and the learned artificial neural network (ANN) is used as a pattern-matching algorithm in order to predict a user's location. The second approach uses General Regression Neural Networks (GRNNs) from which, exploration is performed to find the best hidden-layer configuration and spread factors for Multilayer Perceptrons (MLPs) and General Regression Neural Networks (GRNNs), respectively. The novelty of this work partly stems from data expanding multiple CSI packets. The paper finally compares, the accuracy of our proposed method with previously reported state-of-the-art methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".