Learned Preconditioned Conjugate Gradient Descent for Massive MIMO Detection
Bibliographic record
Abstract
In this paper, we investigate the use of model-based neural networks for Massive Multiple-Input Multiple-Output (MMIMO) detection. Recently, a new M-MIMO detection architecture called LcgNet [1] was obtained by unfolding an iterative conjugate gradient descent algorithm into a layer-wise network and introducing additional trainable parameters. Herein, we extend this approach by introducing a preconditioner aimed at improving the spectrum of the filter matrix used in the uplink MIMO detector. Specifically, the preconditioning scheme reduces the eigenvalue spread of the filter matrix, thus resulting in better convergence of the conjugate gradient algorithm. The proposed extension of LcgNet with preconditioning, referred to as PrLcgNet, is evaluated by means of simulations over M-MIMO uncorrelated Rayleigh fading channels and correlated fading channels. Compared to the original LcgNet, Pr-LcgNet exhibits faster convergence and lower residual error in the training phase, while achieving comparable bit error rate (BER) performance using fewer layers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".