Integration Gain Maximization in ISAC Systems Through Adaptive Unified Resource Allocation
Bibliographic record
Abstract
The exponential increase in concurrent demands, driven by diverse intelligent applications, poses substantial challenges for emerging beyond-communication networks, such as integrated sensing and communication (ISAC). Specifically, this growing demand necessitates the development of efficient resource allocation strategies and unified performance metrics to effectively manage heterogeneous service requirements in dynamic environments. To realize 'sensing with communication' and ultimately enhance service quality, this paper proposes an adaptive resource allocation scheme aimed at maximizing integration gain. To achieve this, we introduce an integration gain evaluation metric that leverages the inherent similarities between sensing and communication (S&C) channels to quantify information sharing, thereby continuously strengthening the correlation and integration of both functions within the ISAC system. To enable unified system operation across heterogeneous services, an integration gain-guided adaptive grained search algorithm is developed for bandwidth resource allocation. Simulation results demonstrate improved performance of the proposed integration framework and resource allocation algorithm compared with other benchmarks.
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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 | 0.000 | 0.001 |
| 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".