Anti-Jamming Task Scheduling in MEC-O-RAN With Hierarchical DRL and Transformer-Based Control
Bibliographic record
Abstract
This paper presents a Deep Hierarchical Reinforcement Learning (DHRL) framework for reliable task scheduling in MEC-enabled 5G Open RAN systems under on-off jamming attacks. The scheduling problem is modeled as a combinatorial integer nonlinear program (ComINP), which is hard to solve directly. To handle this, we use Deep Reinforcement Learning (DRL). Since the jamming environment is only partially observable, the problem becomes a Partially Observable Markov Decision Process (POMDP). To deal with this, we split the problem into two DRL agents. The first agent is a jamming estimator that uses an Alternating Discrete Phase-Type Renewal Process (ADPHRP) to predict jammed time slots based on dense distribution patterns. It is trained using a Proximal Policy Optimization (PPO) algorithm. The second agent is a task scheduler called Weighted MAC-based Task Scheduler (WMAC-TS), which schedules tasks during non-jammed slots while maintaining quality of service (QoS). It uses a transformer-based Actor-Critic model with linear complexity relative to the number of tasks, considering both short-term and long-term rewards. Simulation results show that the PPO-based jamming estimator achieves a cumulative prediction error of 13 time slots in 100 time slots, compared to 25 time slots for DDQN with historical data and 48 time slots for standard DDQN. For 50 active users, WMAC-TS achieves a task drop ratio of 0.917 lower than the 0.942 of the baseline genetic algorithm, and cuts execution time from 1260 seconds to 316 seconds.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".