Model Drift-Adaptive Resource Reservation in ISAC Networks: A Digital Twin-Based Approach
Bibliographic record
Abstract
Reserving resources in integrated sensing and communication (ISAC) networks requires the predictive modelling of the demand and performance of a sensing service, which can be achieved by the synergy of mathematical and Artificial Intelligence (AI) models. However, under non-stationary network conditions, the accuracy of predictive modelling can degrade over time, which is referred to as model drift. In this paper, we propose a digital twin (DT)-based model drift-adaptive resource reservation scheme. The objective is to provide a statistical quality of service guarantee with minimal resource consumption for a sensing service. To achieve this objective, a DT is constructed at a network controller that collects the locations of sensing nodes and targets and captures their spatial distribution. To handle model drift, two candidate predictive spatial models are constructed in the DT. Then, a network emulation-based scheme is proposed to evaluate the resource reservation decisions made with the two candidate spatial models and select the optimal decision. Simulation results show that the proposed resource reservation scheme can reduce the resource under-/over-provision under non-stationary network conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".