Hybrid Beamforming for RIS-Aided ISAC: Maximizing Weighted Sum of SCNR and SINR
Bibliographic record
Abstract
This paper investigates the beamforming for the reconfigurable intelligent surface (RIS)-aided millimeter wave integrated sensing and communication system. We propose a fractional programming (FP) and alternating optimization-based hybrid beamforming (HBF) scheme. The weighted sum of the signal-to-clutter-and-noise-ratio at the radar receiver and the smallest signal-to-interference-plus-noise ratio among all communication users is maximized under the hardware constraints. Since it is difficult to directly obtain a solution for this non-convex FP problem, it is divided into three sub-problems that are alternately solved. Two sub-problems optimizing the digital transceiving beamforming at the base station (BS) are transformed into typical convex quadratic constraint quadratic programming ones using quadratic transformation. The other sub-problem optimizing the RIS passive beamforming is transformed into a manifold optimization one using Dinkelbach transformation. In addition, we consider the HBF structure at the BS through substituting the fully digital beamformer by the digital and analog ones. To reduce the computational complexity, a low-complexity HBF scheme based on Rayleigh quotient, zero-forcing and discrete Fourier transform codewords is proposed with closed-form expressions. Simulation results verify the effectiveness of two proposed schemes.
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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 | 0.001 |
| 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) | 0.002 | 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".