Knowledge-Collaboration-Based Resource Allocation in 6G IoT: A Graph Attention RL Approach
Bibliographic record
Abstract
In future 6G-enabled Internet of Things (IoT), users and devices will be divided into numerous distributed domains with smaller base station coverage due to the utilization of terahertz high-frequency band communication. Deep reinforcement learning (DRL) agents will be increasingly deployed in the domain to achieve intelligent service provisioning and resource allocation. However, the existing DRL-based method faces the problem of repeated model training and poor generalization ability when service demand fluctuates and environmental changes occur. In addition, limited training samples in each domain also lead to insufficient model training. Inspired by the collaborative learning of human knowledge, we propose a knowledge collaboration-based resource allocation mechanism for future 6G-enabled IoT and address two basic issues: 1) which agent should collaborate with and 2) how to collaborate. Specifically, we first model the distributed network as a graph and use graph attention (GAT) to capture the fluctuant service demands and time-varying resource capacities in temporal and spatial domains, and then calculate the similarity between the agents. We further propose a collective reinforcement learning (CRL) algorithm that facilitates knowledge collaboration between the agents through the policy distribution. Simulation results verify that the proposed GAT-CRL achieves fast convergence as deep deterministic policy gradient (DDPG) in 4K steps, computing the similarity score more accurately with the increasing attention heads, and achieves higher successful flow than the soft actor-critic (about 3.6%–5.4%) and DDPG (about 14.6%–21%) when adapting to unseen traffic patterns/loads and increasing topology scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".