A deep learning approach based on Richardson and Gauss–Seidel for massive <scp>MIMO</scp> detection
Bibliographic record
Abstract
Abstract Massive multiple‐input multiple‐output (MIMO) systems can improve the spectrum utilization and the system capacity, but this also increases the computational complexity of the signal detection. The existing iterative algorithms can greatly reduce the computational complexity; however, the detection performance is limited. In order to achieve a better balance between the computational complexity and the detection performance, this article combines the model‐driven deep learning approached with Massive MIMO signal detection to construct RGNet (RIGS‐based deep learning Network). First, RIGS is proposed as a hybrid method of RI (Richardson) and GS (Gauss–Seidel). The RIGS algorithm combines these methods to achieve faster convergence. However, the performance of RIGS joint algorithm is limited to the spatially correlated channel scenarios. To improve robustness, we further extend RIGS, by adding learnable parameters in each iteration and introducing staircase activation functions to significantly improve detection performance. Simulation results show that the proposed RGNet has low computational complexity and a simple and fast training process. It can also achieve excellent detection performance in Rayleigh fading channel and spatially correlated channel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".