Classification of User Trajectories in LTE HetNets Using Unsupervised Shapelets and Multiresolution Wavelet Decomposition
Bibliographic record
Abstract
The classification of user trajectories in Long-Term Evolution (LTE) heterogeneous networks (HetNets) is investigated in this paper. We propose a methodology to classify user trajectories based on the measurement reports submitted to the serving base station as part of the handover process; we propose to consider each measurement report as a time series. This methodology allows base stations to automatically and autonomously discover the radio-frequency (RF) conditions of their cell edge (e.g., signal strength degradation and interference levels). We propose the application of machine learning and data mining techniques to identify patterns in the reference signal received power measurement reports submitted by users as they approach the edge of the service area. Our time-series clustering algorithm based on unsupervised shapelets and multiresolution wavelet decomposition provided superior performance compared to a discrete Fourier transform (DFT)-based clustering algorithm. Our algorithm was able to provide clustering results with an average accuracy of 95%. Furthermore, the quality measure of the resulting clusters was up to 75% better, compared to the clustering results provided by the DFT-based algorithm. We also proposed a novel methodology to calculate a suitable number of clusters with no prior knowledge regarding the data; an average accuracy close to 90% was achieved.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".