Hybrid Beamforming for RIS-Assisted Multiuser Fluid Antenna Systems
Bibliographic record
Abstract
Recent advances in reconfigurable antennas have led to the new concept of the fluid antenna system (FAS) for shape and position flexibility, as another degree of freedom for wireless communication enhancement. This paper explores the integration of a transmit FAS array for hybrid beamforming (HBF) into a reconfigurable intelligent surface (RIS)-assisted communication architecture for multiuser communications in the downlink, corresponding to the downlink RIS-assisted multiuser multiple-input single-output (MISO) FAS model (Tx RIS-assisted-MISO-FAS). By considering Rician channel fading, we formulate a sum-rate maximization optimization problem to alternately optimize the HBF matrix, the RIS phase-shift matrix, and the FAS position. Due to the strong coupling of multiple optimization variables, the multi-fractional summation in the sum-rate expression, the modulus-1 limitation of analog phase shifters and RIS, and the antenna position variables appearing in the exponent, this problem is highly non-convex, which is addressed through the block coordinate descent (BCD) framework in conjunction with semidefinite relaxation (SDR) and majorization-minimization (MM) methods. To reduce the computational complexity, we then propose a low-complexity grating-lobe (GL)-based telescopic-FA (TFA) system with multiple delicately deployed RISs under the sub-connected HBF architecture and the line-of-sight (LoS)-dominant channel condition, to allow closed-form solutions for the HBF and TFA position. Our simulation results illustrate that the former optimization scheme significantly enhances the achievable rate of the proposed system, while the GL-based TFA scheme also provides a considerable gain over conventional fixed-position antenna (FPA) systems, requiring statistical channel state information (CSI) only and with low computational complexity.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".