Unsupervised Learning-Based Joint Beamforming and Phase-Shift Optimization for RIS-Assisted DeepMIMO With Large-Scale Arrays
Bibliographic record
Abstract
This paper considers a reconfigurable intelligent surface (RIS)-aided network, where discrete phase-shift RIS is investigated in large-scale arrays with hundreds of antennas at the source and thousands of passive elements at the RIS. We propose unsupervised learning (UnSL) approaches that eliminate the need for labeled data to address the joint beamforming and phase-shift (JBPS) optimization problem with high degrees of freedom (DoF), i.e., thousands of optimization variables, for efficient transmission design in RIS-DeepMIMO networks. Performance is analyzed by comparing the signal-to-noise ratio (SNR) with that of end-to-end SNR-based exhaustive search (ESES) and particle swarm optimization (PSO) algorithms under maximum ratio transmission (MRT) beamforming. We show that MRT-PSO, MRT-UnSL, and JBPS-UnSL with multitask neural network suffer performance degradation due to the high-dimensional input. On the other hand, numerical results reveal that the GPU-MRT-CCES method outperforms the other solutions and exhibits high scalability, owing to its novel combination of a predefined MRT solution, theoretical cascaded channel-based RIS configuration, and GPU-parallelized computation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".