QoS-Aware multi-agent DDPG for adaptive edge service distribution in intelligent wireless communication networks
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Bibliographic record
Abstract
Effective service distribution management is essential in Intelligent Wireless Communication Networks to meet the increasing Quality of Service (QoS) demands across various applications. Traditional transmission strategies often prioritize high-QoS data, which can lead to access starvation for lower-priority data in resource-constrained environments. To address this, we propose a QoS-aware adaptive service distribution strategy that balances the needs of high- and low-priority data without compromising the performance of either. Leveraging enhanced Multi-Agent Deep Deterministic Policy Gradient (e-MADDPG), our solution dynamically optimizes service distribution in mobile edge networks. By employing a gated recurrent unit-enhanced reinforcement learning framework, we enable intelligent agents to collaboratively decide channel access based on real-time traffic conditions. The proposed multi-criteria Decision-based Multi-channel Access algorithm allows high-priority data to defer access if necessary, improving the completion rates of lower-priority data. Furthermore, our method integrates network slicing and computation offloading to enhance service adaptability, ensuring efficient use of edge resources. Simulation results confirm that our framework significantly outperforms existing approaches in terms of channel utilization, QoS adherence, and overall network efficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it