Integrated Sensing and Communication Channel Modeling and Measurements: <i>Requirements and Methodologies Toward 6G Standardization</i>
Bibliographic record
Abstract
Integrated sensing and communication (ISAC) has been defined as one of the major usage scenarios for 6G. When sensing and communication channels coexist in ISAC scenarios, neither conventional communication nor sensing channel models are applicable. As the foundation of ISAC studies, a new channel modeling methodology is required to characterize both sensing and communication channels and their correlations. This article introduces the framework of a general ISAC channel model, which integrates a deterministic multiscattering-center (MSC) model of sensing targets to the stochastic propagation channel model. Parameterizations of the proposed channel model rely on channel measurements. This article introduces two ISAC channel measurement methodologies based on the vector network analyzer (VNA) and a novel dual-sensor measurement system. The proposed methods can be applied in future 6G ISAC channel model standardization and system evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".