Low-Complexity Beamforming Design for RIS-Assisted Fluid Antenna Systems
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) has recently drawn substantial attention toward performance enhancement in wireless communication systems. One of the key challenges in RIS-assisted systems is to design efficient joint beamforming algorithms. However, most existing algorithms rely on instantaneous channel state information (CSI) and iterative optimization methods, which suffer from high computational complexity. Therefore, by exploiting the property of the recent popular fluid antennas, this paper proposes a low-complexity joint beamforming scheme for an RIS-assisted fluid antenna system (RIS-FAS) requiring only statistical CSI. Specifically, by carefully adjusting the FA array at the transmitter side, it is possible to respectively steer its main-lobe (ML) and grating-lobe (GL) towards the user and RIS, where the line-of-sight (LoS) channels from the transmitter to the user and RIS are guaranteed to be identical, allowing independent beamforming for the three cascade channels. Theoretical analysis and simulation results validate that the proposed scheme achieves both high-performance and low-complexity characteristics compared with its conventional counterparts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".