Scalable Spatial and Geometric Learning Approach for Joint Power Control and Channel Allocation
Bibliographic record
Abstract
This research paper introduces an unsupervised scalable probabilistic approach for radio resource management in device-to-device (D2D) communication networks, essential for enhancing wireless data service capacity. We propose a joint optimization framework for spectrum allocation and power control, aiming to optimize the network’s mean rate while meeting minimum data rate requirements. Although deep learning (DL) models have been explored for this purpose, their scalability is constrained by the fixed sizes of their input/output features, and their effectiveness is often limited by an insufficient understanding of the network’s geometric structure. Consequently, Graph Neural Networks (GNNs) were introduced to integrate the wireless network’s topology into the learning process. However, GNNs typically lose spatial correlation data when converting the tensorized channel state information (CSI) into a graph structure. To overcome this limitation, our solution combines GNNs, convolutional neural networks (CNNs), and variational autoencoders to extract meaningful embeddings from the CSI, preserving spatial and geometric features. We also introduce an innovative graph attention mechanism that enhances the model’s focus on crucial node and edge features. Our holistic approach exploits the wireless network’s topological and spatial relationships, offering a scalable, unsupervised, and generalizable solution without the need for retraining or architectural adjustments across various wireless setups. Our findings confirm our method’s superior performance and adaptability to different wireless environments.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".