A Hypernetwork Framework for Learning Adaptive Beamforming Schemes in RIS Systems
Bibliographic record
Abstract
This work develops a learning-based framework that directly exploits noisy pilots to optimize reconfigurable intelligent surface (RIS) systems while accommodating different service priorities and fairness via user weights. First, an adaptive beamforming configuration problem is formulated to generate the base station active beamforming vectors and RIS passive beamforming reflection coefficients that optimize the weighted sum-rate. Under mild regularity conditions, this problem is shown to attain a maximum. To learn approximate solutions, a novel hypernetwork-based beamforming (HNB) framework is proposed. Particularly, a beamforming network (BFN) exploits available information, including noisy pilots, to generate optimized beamforming configurations. Rather than learning one BFN, a hypernetwork is trained to dynamically generate BFN learning parameters from an input conditioning vector. When the conditioning vector is chosen as the user weights, the trained HNB can tune the BFN to the user weights without the need for retraining. Numerical experiments demonstrate that tuning allows the proposed HNB to perform close to an optimistic block-coordinate descent with perfect CSI benchmark and significantly outperform static learning where a BFN is directly trained to optimize beamforming configurations. Additionally, employing the HNB to also tune the BFN to location information considerably reduces the pilots needed to generate optimized beamforming configurations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".