Resource Allocation for Heterogeneous Statistical QoS Provisioning Over Internet-of-Vehicles via Hierarchical Deep Reinforcement Learning
Bibliographic record
Abstract
The high-dynamic characteristics of vehicular networks make it difficult to satisfy strict deterministic quality-of-service (QoS) conditions. Statistical QoS provisioning instead of deterministic QoS provisioning can provide an efficient way to satisfy delay-bounded QoS requirements of vehicular services. Different from the existing work, this paper considers the heterogeneity of statistical QoS provisioning for delay-sensitive services in Internet-of-Vehicles (IoV), where the overall delay violation probability is limited in each time slot for mission-critical applications, and the queue length bound along with its higher-order statistics is limited from a long-term perspective for infotainment services. A long-term resource optimization problem is formulated to minimize the average energy consumption with heterogeneous statistical latency guarantees. To provide a stable and fast solution, Lyapunov optimization method is first leveraged to transform the formulated stochastic problem to a series of short-term deterministic optimization sub-problems. Afterwards, a novel two-level hierarchical deep reinforcement learning (H-DRL) framework is presented, where the knowledge sharing is further introduced to realize the fast model training and resource decision-making with QoS performance guarantees. Simulation results verify the convergence speed and the training accuracy of the presented H-DRL framework, and it also shows the superiority of the proposal in the perspective of the end-to-end delay and the queue stability for heterogeneous delay-bounded services under high-dynamic environments of IoV.
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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".