Integrated Sensing and Communication: A Network Level Perspective
Bibliographic record
Abstract
Given the wide coverage of communication networks and tremendous number of mobile devices, it has been proposed to integrate wireless sensing capabilities into mobile communication networks so that the growing demands for ubiquitous sensing can be satisfied without extensively deploying dedicated sensing devices. In this article, we study integrated sensing and communication (ISAC) functionalities from a network level perspective. Specifically, we thoroughly investigate how to efficiently manage the available communication, sensing, computing, and storage resources in the network so that sensing requirements can be satisfied without compromising communication performance. First, we discuss the benefits of embedding ISAC into wireless networks as well as the interactions between communication, sensing, computing, and storage on the network level. Then, we present a feasible solution to efficiently allocate sensing tasks among base stations such that the impact of introducing extra sensing workloads on communication services is minimized. Finally, we identify potential research directions and discuss the associated challenges. This article offers a new viewing angle on ISAC-related research and can motivate more research interests to explore ISAC operations from the networking perspective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".