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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".