Sparse Bayesian Least Squares Regression Model for Direction of Arrival Estimation in Massive MIMO Networks
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Bibliographic record
Abstract
Massive multiple-input multiple-output (MIMO) antenna arrays pose significant challenges in terms of accuracy, efficiency and computational complexity in directionof-arrival (DOA) estimation, particularly in fifth-generation (5G) and beyond fifthgeneration (B5G) networks.Current DOA estimation methods, including cluster-based, spatial-temporal, and machine-learning approaches, struggle in dynamic and noisy environments, incurring inaccuracy in DOA estimation and substantial computational demands.To overcome these issues, this research work introduces a Sparse Bayesian Least Squares Regression (SBLSR) model designed explicitly for massive MIMO systems, which employs an advanced least squares regression technique.Unlike existing sparse Bayesian models or regression-based estimators, SBLSR introduces an adaptive probabilistic framework that iteratively adjusts regression weights for dynamic noise conditions, achieving near-Cramer-Rao lower bounds (CRLB) accuracy with significantly lower complexity.By combining Bayesian probabilistic modelling with least squares regression, the SBLSR significantly enhances estimation accuracy, particularly in environments with high levels of noise.Finally, the simulation results indicate that the proposed SBLSR method improves DOA estimation accuracy and reduces root mean square error (RMSE) values for various signal-to-noise ratio (SNR) limits when compared to existing approaches.
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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 it