AMADRL: Privacy-Aware Attention-Based Multiagent Deep Reinforcement Learning for Optimizing Spectral Allocation in 6G Vehicular Networks
Bibliographic record
Abstract
The emergence of 6G-enabled Vehicle-to-Everything (V2X) networks has created unprecedented demand for ultra-reliable, low-latency spectrum allocation across heterogeneous entities including vehicles, IoT devices, and industrial systems. Current spectrum allocation methods suffer from exponential computational complexity, extensive information sharing requirements, and poor scalability in dense networks. This paper proposes AMADRL (Attention-based Multi-Agent Deep Reinforcement Learning), a novel framework employing dual critic networks with multi-head self-attention mechanisms for intelligent spectrum allocation. The dual critic architecture resolves individual-collective optimization conflicts through local critics for independent entity optimization and a global critic with attention-based coordination. Our approach significantly reduces information sharing requirements while handling heterogeneous QoS demands across diverse entity types. Comprehensive experimental evaluation comparing AMADRL against state-of-the-art baselines including MADDPG, MAAC, QMIX, attention-based methods (A-DDPG, MHA-DQN), and game-theoretic approaches reveals that AMADRL achieves superior performance across multiple metrics including spectrum utilization efficiency, interference mitigation, and network scalability, while preserving user privacy and satisfying strict latency constraints required by safety-critical and industrial use cases.
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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 | 0.000 |
| 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 | 0.001 |
| 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".