Learning Resilient Distributed Channel Access Policies in V2I Networks Under Intelligent Jamming
Bibliographic record
Abstract
While the Internet of Vehicles (IoV) can revolutionize transportation systems through intelligent connectivity, a critical challenge in realizing this potential lies in ensuring efficient channel allocation in the IoV ecosystem, particularly considering dynamic channel conditions and adversarial jamming exacerbated by the emergence of artificial intelligence (AI)-based jamming. To address these challenges, in this study, we use distributed edge intelligence (DEI) to propose a distributed channel access mechanism for the vehicle-to-infrastructure (V2I) mode of IoV networks. Specifically, using an actor-critic-based multiagent reinforcement learning (MARL) framework with a common critic, we model the distributed channel access problem in V2I communications under varying channel conditions and an intelligent jamming device$(\tt {iJD})$interference as a decentralized partially observable stochastic game (Dec-POSG). Furthermore, by addressing challenges, such as partial observations, nonstationarity, and credit assignment, our proposed approach fosters collaboration among intelligent vehicles ($\tt {iV}$s) without direct communication. In addition, our unique counterfactual reasoning-aided action evaluation mechanism and a novel utility function design enable the$\tt {iV}$s to learn mixed collaborative-competitive channel access policies, thereby enhancing channel utilization, mitigating the impact of the$\tt {iJD}$, and improving the network’s sum cross-layer achievable rate (SCLAR).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".