Multi-RIS Assisted Hybrid Beamforming Design for Terahertz Massive MIMO Systems
Bibliographic record
Abstract
This paper presents an innovative approach for terahertz (THz) band communications, utilizing reconfigurable intelligent surfaces (RISs) to implement an angular-based hybrid beamforming (AB-HBF). This study examines two significant THz channel scenarios with cost-efficient solutions utilizing RISs to enhance performance. The first scenario ensures reliable communication in the presence of obstacles blocking the direct path, where RISs offer an alternative transmission path. The second scenario leverages multiple-RIS in line-of-sight dominant or sparse environments to increase the rank of the channel matrix, consequently improving the achievable rate. By introducing an innovative HBF design for multiple-RIS-assisted THz massive MIMO systems, a three-stage design is proposed based on a geometry-based THz channel model. This design encompasses the transmit and receive radio frequency (RF) beamformers, transmit/receive baseband (BB) precoder/combiner, and RIS phase shift matrix. A particle swarm optimization-based solution is employed to design the RIS phase reflections. This design methodology is extended to the multiple-RIS-aided scenario, optimizing the phase shift matrices of each RIS. We also provide sub-connected AB-HBF configuration by offering a remarkable reduction in the total number of phase shifters compared to the fully-connected AB-HBF. Additionally, a deep learning-based phase shift design is incorporated to effectively optimize the RIS configurations, significantly reducing the computational time required for system calibration in dynamic THz environments. Extensive numerical experiments demonstrate the significant enhancement in the achievable rate of THz AB-HBF systems by incorporating RISs, mitigating the wireless propagation disruptions as well as reducing hardware cost/complexity and power consumption for massive MIMO systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".