DRL-Based Optimisation for Task Offloading in Space-Air-Ground Integrated Networks: A Reliability-Driven Approach
Bibliographic record
Abstract
This paper addresses the problem of reliable task offloading in space-air-ground integrated network (SAGIN) based edge computing systems. Specifically, we aim to maximise the successful task offloading ratio for ground users communicating with a satellite's edge server. In our network topology, end-to-end communications are facilitated by relay unmanned aerial vehicles (UAVs). The formulated problem jointly optimises task offloading portions and bandwidth allocations for both ground-to-air and air-to-space links, subject to quality-of-service (QoS) requirements, transmission rates, system bandwidth, and the computing capacity of the satellite's edge server. To solve the formulated complex non-linear, non-convex, and mixed-integer problem, we propose an efficient solution underpinned by a deep reinforcement learning (DRL). Simulation results demonstrate the effectiveness of the proposed method, which achieves stable training performance and an optimised reliable offloading ratio compared to benchmark schemes.
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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 | 0.001 | 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 | 0.000 | 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".