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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".