A Novel Simplified RIS-Assisted Hybrid Transceiver Scheme for mmWave MIMO Systems
Bibliographic record
Abstract
Integrating reconfigurable intelligent surface (RIS) with hybrid precoding has recently emerged as an effective approach to enhance spectral efficiency and link reliability in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Motivated by this potential, we propose a novel simplified RIS-assisted hybrid transceiver (SRIS-HT) scheme that combines a simple RIS matrix with an efficient hybrid transceiver design. Initially, the optimal unconstrained RIS matrix is derived for scenarios where the direct base station–user link is blocked. For practical implementation, this matrix is approximated by extracting and normalizing its diagonal elements, exploiting its unitary properties. The resulting RIS matrix is then employed to design the hybrid transceiver using momentum and Newton's methods. The SRIS-HT scheme is further extended to cases where a direct base station–user link exists. Simulation results demonstrate that the proposed SRIS-HT scheme achieves spectral efficiency close to that of fully digital designs, significantly outperforming existing hybrid schemes. Additionally, the results highlight the robustness of SRIS-HT against imperfect channel estimation and weak direct links, while maintaining lower computational complexity compared to conventional optimization methods, especially when the number of transmit antennas is comparable to or smaller than the number of RIS elements.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".