Service Offloading in Terrestrial-Satellite Systems: User Preference and Network Utility
Bibliographic record
Abstract
In this paper, we investigate service offloading in an integrated terrestrial-satellite (T-S) system. We consider the terrestrial base station (TBS) and satellites to be service providers, all user equipment (UE) to be service requesters, and the service can be content delivery, computation, etc. While offloading services to the satellites can prevent the TBS from being overloaded, the quality of service (QoS), e.g., content delivery latency, may degrade, necessitating a balance between the user preference and the utilities of the TBS and satellites. From the perspective of network management, we propose an abstract model that incorporates the utilities of the TBS, the satellites, and the UE, as well as the service capacity, service load, and service cost at the TBS and the satellites. While finding the optimal offloading decision, a problem of integer programming, is NP-hard, we develop two algorithms with low complexity for finding sub-optimal solutions of the offloading decision problem in the scenarios of one satellite and multiple satellites, respectively. Moreover, we prove that the solution found by the first algorithm is guaranteed to be optimal under the condition that the tasks for service from all UE have an identical size. Numerical results demonstrate the performance of the proposed algorithms compared to that of the optimal offloading by exhaustive search and the offloading by the greedy algorithm.
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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 | 0.000 |
| 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".