Maximizing the Value of Service Provisioning in Multi-User ISAC Systems Through Fairness Guaranteed Collaborative Resource Allocation
Bibliographic record
Abstract
The proliferation of wireless-enabled industrial applications highlights the growing importance of Integrated Sensing and Communication (ISAC) for concurrent provisioning of environment sensing and data transmission capabilities. However, the resource-hungry nature of sensing processes, coupled with competing demands from coexisting users, poses the fundamental challenge of effective and fair resource allocation in multi-user ISAC systems. To address this challenge, we propose a value of service (VoS)-oriented resource allocation scheme for concurrent heterogeneous service provisioning in a multi-user collaborative ISAC system. Specifically, a performance indicator VoS is utilized to guide system-wide effective resource allocation while guaranteeing fairness among all ISAC users. Specifically, we formulate the multi-user resource allocation problem as a bargaining game-based model and tackle it with an iterative algorithm to attain the Nash equilibrium. In each iteration, the allocation of power and bandwidth resources is optimized by solving the Lagrangian dual problem. Numerical simulations are performed under varying resource conditions, service demands, and channel states. The results demonstrate the superiority of the proposed scheme over non-collaborative alternatives and the other two benchmark schemes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".