Massive MIMO Detection Method Based on Quasi-Newton Methods and Deep Learning
Bibliographic record
Abstract
Due to the increase in the number of antennas in massive Multiple-Input Multiple-Output (MIMO) systems, traditional MIMO detection algorithms need to be improved. In this letter, we introduce trainable variables in Broyden Quasi-Newton method to obtain Broyden-Net that avoids the high-dimensional matrix inversion of the linear Minimum Mean Square Error (MMSE) detector and also breaks through the performance limitations of the linear detector. To further simplify the complexity of the algorithm, we use the search direction consistency of the special form Broyden-Fletcher-Goldfarb-Shanno (BFGS) in Broyden to further combine the BFGS Quasi-Newton method with Deep Learning (DL), to propose BFGS-Net that is more adapted to massive MIMO detection. Numerical results show that Broyden-Net and BFGS-Net effectively reduce the computational complexity of the MMSE detector and can achieve good detection performance for massive MIMO systems in spatially correlated scenarios.
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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".