Fingerprinting Localization Method Based on Clustering and Gaussian Process Regression in Distributed Massive MIMO Systems
Bibliographic record
Abstract
Fingerprinting (FP) localization methods are used in massive multiple-input multiple-output (MIMO) systems due to their high reliability and accuracy. The Gaussian process regression (GPR) method could potentially be used, as an FP-based localization method, in a massive MIMO system to provide high accuracy. However, it is limited by high complexity, especially in a large-scale environment. In this paper, we propose an FP-based localization method, using affinity propagation (AP) clustering and Gaussian process regression (GPR) to estimate user's location in a distributed massive MIMO system based on the uplink received signal strength (RSS) vectors. First, the training RSS vectors are clustered using the AP algorithm to reduce the computational complexity. Then, the data distribution within each cluster is accurately modeled using GPR to provide excellent support for further positioning. Simulation studies reveal that the proposed method improves root-mean-squared estimation error (RMSE) performance significantly by reducing the location estimation error compared to using only GPR for all training RSS data. Also, it reduces the computational complexity of using GPR.
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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".