From Rigid Isolation to Elastic Integration: Progressively Unified Resource Allocation in ISAC for Value of Service Maximization
Bibliographic record
Abstract
Concurrently supporting heterogeneous services, e.g., sensing and communication (S&C), presents a significant challenge for future wireless networks due to the increasing number of connected devices, limited resources, and the complexity of integrated service provisioning. Furthermore, dynamic network conditions, along with varying heterogeneous needs from coexisting devices, further exacerbate the challenges of traditional rigid system operation, where heterogeneous network services are treated as either entirely independent or fully integrated. This rigid operation neglects the fluctuating gains and costs of the integrated heterogeneous service provisioning. To transform isolated operations into a highly integrated paradigm, this paper proposes a progressive scheme for integrated sensing and communication (ISAC). The scheme elastically adjusts the integration level based on continuously accumulated system state observations, including user demand, resource conditions, and environmental changes, to regulate resource utilization dynamically. Specifically, we present a unified Value of Service (VoS) metric, which adaptively incorporates user service experiences, resource costs, and gains from S&C coupling to guide efficient resource allocation. In addition, building on this progressive integration scheme, we develop a dynamic stage-dependent resource optimization algorithm for bandwidth allocation. Simulation results demonstrate the effectiveness of the proposed integrated framework and algorithm in optimizing resource allocation and maintaining system performance under stringent resource constraints.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".