RIS-Assisted Joint Sensing and Communications via Fractionally Constrained Fractional Programming
Bibliographic record
Abstract
This paper studies an uplink dual-functional sensing and communication system assisted by an active or passive reconfigurable intelligent surface (RIS), whose reflection pattern is optimally configured to trade off sensing and communication functionalities. Specifically, the Bayesian Cramér-Rao lower bound (BCRLB) for sensing is minimized under the quality-of-service (QoS) communication constraints. We show that this problem can be formulated as a fractionally constrained fractional programming (FCFP) problem for which a quadratic transform, originally proposed for the sum-of-ratio fractional programs, can be used to decouple the numerators and denominators in both the objective function and the constraints. In this way, the FCFP is turned into a sequence of sub-problems that are convex except for the constant-modulus amplitude constraints which can be dealt with using a penalty-based method. Numerical results unveil nontrivial beamforming reflection patterns that the RIS can be configured to generate in order to facilitate both sensing and communications. The results demonstrate the effectiveness of the proposed algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".