Intelligence Resource Management in 5G/6G D2D-SIoET Network Through Centralized Training with Decentralized Execution Multi-Agent Reinforcement Learning
Bibliographic record
Abstract
Wireless communication is developing rapidly towards 5 G and 6 G networks, while necessitating advanced resource management techniques to handle massive device connectivity, high data rates and low latency requirements. Existing methods faced difficulty with spectrum efficiency, latency and high energy consumption and dynamic environments particularly Device-to-Device (D2D) communication and Social Internet of Education Things (SIoET). Static resource management frameworks struggle to adapt to the non-stationary nature of user behavior and device mobility leading to degraded Quality of Service (QoS). To overcome this issue, Centralized Training Decentralized Execution for Social Internet of Education Things (CTDE-SIoET) is applied to 5 G/6 G D2D and edge resource allocation. This method integrates Multi-Agent Reinforcement Learning (MARL) framework that employs centralized critic network during training, while also allowing the agents from global system information to address non-stationary and credit assignment problems. During execution phase, centralized critic is discarded and each agents uses its own decentralized actor network based on local observation to select actions independently, thereby ensuring scalability and efficient operation, where continuous global communication is not feasible. The results demonstrate that the proposed method archives superior performance metrics compared to existing RL techniques showing lower latency (20-35%), reduced energy consumption (15-30%) and higher network throughput (1025%).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".