Channel Modeling for RIS-Assisted 6G Communications
Bibliographic record
Abstract
Terahertz communication has been proposed as one of the basic key technologies of the sixth-generation wireless network (6G) due to its significant advantages, such as ultra-large bandwidth, ultra-high transmission rates, high-precision positioning, and high-resolution perception. In terahertz-enabled 6G communication systems, the intelligent reconfiguration of wireless propagation environments by deploying reconfigurable intelligent surfaces (RIS) will be an important research direction. This paper analyzes the far field and near field of RIS-assisted wireless communication and a detailed system description is presented. Subsequently, this paper presents a specific study of the channel model for an RIS-assisted 6G communication system in the far-field and near-field cases, respectively. Finally, an integrated simulation of the channel models for the far-field and near-field cases is carried out, and the performance of the RIS auxiliary link measured in terms of signal-to-noise ratio (SNR) is compared and analyzed. The results show that increasing the size of the RIS surface to improve the SNR is an effective method to enhance the coverage performance of the 6G THz communication system under the strong guarantee of the ultra-large bandwidth of THz.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".