Shared-Resource Generative Adversarial Network (GAN) Training for 5G URLLC Deep Reinforcement Learning Augmentation
Bibliographic record
Abstract
Deep Reinforcement Learning (DRL) solutions to 5G problems often face with communication unreliability issues due to imbalanced state-space distributions and the scarcity of rare samples. Generative Adversarial Network (GAN) is promising to improve DRL reliability. However, employing GANs in resource-constrained edge environments is very challenging due to their heavy resource consumption. Previous general resource allocation models for training neural networks do not consider GAN quality requirements such as the minimum number of training samples. We propose an architecture for sharing edge and cloud resources among multiple GANs, then formulate an optimization model, named OGAN, to maximize DRL reliability with respect to resource constraints for training GANs and fine-tuning DRLs. OGAN allocates resources for training several GANs and DRLs concurrently based on an upper bound error. Difference convex programming is then used to solve this mixed-integer non-linear model. Our experimental results show that OGAN improves the overall system reliability and performance by 23 % and 22 %, respectively, compared to baselines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".