Computation Offloading for Rechargeable Users in Space-Air-Ground Networks
Bibliographic record
Abstract
Relying on space-air-ground (SAG)-integrated artificial intelligence of everything (AIoE) networks, massive computation-intensive and latency-sensitive tasks can be efficiently either executed locally by ground AIoE users, or offloaded to SAG servers, such as remote base stations, aerial high altitude platform (HAP) and low earth orbit satellites. However, joint optimization of communication and computation resources becomes a great challenge considering dynamic network environment, large-scale coverage and battery energy backup constraint. Hence, in this paper, we propose a SAG-integrated heterogenous computation offloading architecture for the deep integration of communication and computation resources in order to maximize the sum-rate of all AIoE users. Moreover, we propose a multi-agent proximal policy optimization algorithm with the aid of Lyapunov-based profile to solve the task scheduling and HAP selection. And a convex optimization based communication and computation resource allocation scheme processes the CPU-cycle frequency and transmission power. The battery energy backup is tackled via the linear programming policy. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art baselines in terms of convergence speed, average sum-rate and battery backup level of AIoE users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".