A Fingerprint Localization Method in Collocated Massive MIMO-OFDM Systems Using Clustering and Gaussian Process Regression
Bibliographic record
Abstract
Localization has been a notable feature in wireless communications due to the increasing demand for location information. Fingerprinting-based (FP) localization methods are promising for rich scattering environments due to their high reliability and accuracy. The Gaussian process regression (GPR) method could potentially be used as an FP-based localization method to facilitate localization and 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 in collocated massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems using the affinity propagation clustering (APC) algorithm and Gaussian process regression (GPR) to estimate the user’s location. Fingerprints are extracted based on instantaneous channel state information (CSI) by taking full advantage of the high resolution in the angle and delay domains. Then, the training fingerprints are clustered using the (APC) algorithm to reduce matching complexity and computational complexity. Finally, the data distribution within each cluster is accurately modeled using GPR to provide excellent support for further localization. Simulation studies reveal that the proposed method improves localization performance significantly by reducing the location estimation error. Additionally, it reduces the matching complexity and computational complexity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".