A Survey on RIS Advances in Terahertz Communications: Emerging Paradigms and Research Frontiers
Bibliographic record
Abstract
Communication at terahertz (THz) frequencies has emerged as a potential answer to the challenge of providing massive capacity and ultra-high data speeds for 6G wireless networks. However, THz waves are vulnerable to obstruction because of their attenuation during propagation and poor diffraction. The development of reconfigurable intelligent surfaces (RIS) technology has shown promise in addressing these issues. The RIS enables the development of smart radio settings with enhanced spectrum coverage and efficiency by regulating the phase shifts of passively reflecting elements. Amidst the vast potential of RIS-based THz communication, a noticeable void exists in comprehensive research, calling for a systematic survey to shed light on its principles, use cases, performance evaluation, challenges, and future trajectories. This review investigates the exciting field of RIS-based THz communication in fifth-generation (B5G), sixth-generation (6G), and beyond. Our study provides a comprehensive overview of this emerging technology, covering a variety of use cases, including channel estimation (CE), coverage, security, sum rate, and energy efficiency. We also investigate resource allocation strategies for THz communication using RIS. Additionally, the performance of RIS-based THz systems with multiple-input and multiple-output (MIMO) and massive MIMO and unmanned aerial vehicle (UAV) technologies is analyzed. By analyzing the existing literature, we illuminate the various approaches, methodologies, and future developments in this field. We also emphasize open issues and research opportunities in THz communication based on RIS. This survey is a valuable resource, contributing to understanding the emerging paradigms and research frontiers of RIS-based THz communication.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".