Mutual Information-Based Integrated Sensing and Communications: A WMMSE Framework
Why this work is in the frame
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Bibliographic record
Abstract
In this letter, a weighted minimum mean square error (WMMSE) empowered integrated sensing and communication (ISAC) method is investigated. One transmitting base station and one receiving wireless access point are considered to serve multiple users and a sensing target. Inspired by mutual information (MI), a unified framework to link sensing and communication is constructed, and communication MI and sensing MI rates are utilized as the performance metrics under the presence of clutters. In particular, we propose a novel MI-based WMMSE-ISAC method to maximize the weighted sensing and communication sum rate of this system. Such a maximization process is achieved by utilizing the classical method—WMMSE, aiming to better manage the effect of sensing clutters and the interference among users. Numerical results show the effectiveness of our proposed method, and the performance trade-off between sensing and communication is also validated.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it