Flexible Resource Allocation in IRS-assisted Systems using Hypernetworks
Bibliographic record
Abstract
Flexible resource allocation in intelligent reflecting surface (IRS)-assisted systems is necessary to account for fairness as well as time-varying channel behavior and user service priorities. An IRS-assisted system can achieve this flexibility by assigning different weights to each user when optimizing resources and the IRS configuration. In this paper, for the first time, we propose a hypernetwork-based beamforming (HNB) framework to dynamically leverage pilot information and user weights to generate the beamforming vectors and IRS configuration that maximize the weighted sum-rate (WSR) in a multi-user IRS-assisted system. As opposed to a traditional learning approach where a beamforming network (BFN) is trained once to optimize the WSR for every possible set of user weights, in a hypernetwork approach, a hypernetwork is trained to generate the learning parameters of the BFN conditioned on the input user weights, i.e., the BFN parameters are now adapted to the user weights without the need for any retraining. Numerical experiments corroborate the effectiveness of the proposed HNB framework to provide performance close to (within approximately 15−17% of) the optimistic benchmark produced by a numerical block-coordinate descent (BCD) algorithm that assumes perfect channel state information (CSI) knowledge. Moreover, the HNB trained with only a few epochs outperforms traditional fully-trained deep learning methods such as fully connected neural networks (FCNN) and graph neural networks (GNN). For example, in one considered scenario, the HNB nearly halves the gap to the BCD-with-CSI performance to 16% compared to gaps of 31% and 28% associated with FCNN and GNN schemes, respectively.
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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) | 0.000 | 0.000 |
| 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) | 0.002 | 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".