Multi-Agent DRL for Resource Allocation in AoI-Aware Energy-Efficient C-V2X Networks
Bibliographic record
Abstract
This paper aims to tackle the complex problem of channel assignment and joint power-energy al-location within a cellular-vehicle-to-everything (C-V2X) network in an urban traffic intersection. Here, the C-V2X network is strategically deployed to facilitate the coordination of multiple vehicle platoons. This includes updating platoon states to the roadside unit (RSU) and managing the exchange of cooperative awareness messages (CAMs) among vehicles. Our objective is to minimise the average age of information (AoI), maximise the CAM delivery probability, and promote sustainable, green communication practices through optimal power-energy management. Given the intricate nature of this challenge, we adopt a multi-agent deep reinforcement learning (MADRL) approach based on Markov decision process (MDP). Next, we introduce two innovative algorithms based on multi-agent deep deterministic policy gradient (MADDPG) and twin delayed deep deterministic policy gradient (TD3) to effectively address the optimisation problem. Finally, the simulation results demonstrate remarkable performance in terms of energy efficiency, while maintaining algorithm convergence speed and AoI level.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".