Frequency-Tunable Ris for Beam Split Mitigation in Wideband Thz Massive MIMO Systems
Bibliographic record
Abstract
The beam split effect, caused by frequencyindependent phase shifts in conventional hybrid beamforming, poses a significant challenge for wideband THz communication, undermining array gain and system performance. Despite the promise of THz frequencies for ultra-high data rates and abundant spectrum, their wideband nature exacerbates beamforming difficulties, necessitating innovative solutions to address these limitations. This paper introduces a novel frequency-tunable reconfigurable intelligent surface (FRIS) architecture that addresses beam split in angular-based hybrid beamforming THz systems. Drawing inspiration from graphene-based metasurfaces, the proposed FRIS is capable of shaping the propagation paths of different subcarriers independently, thereby ensuring consistent beam alignment over an ultra-wide bandwidth. Our design further leverages a reduced-complexity hybrid beamforming strategy at the transmitter, mitigating the hardware burden typically associated with fully digital solutions. Numerical evaluations reveal that the proposed architecture effectively mitigates beam split and achieves high spectral efficiency, providing a scalable and energy-efficient solution for future wideband THz communication systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".