Distributed Reconfigurable Intelligent Surfaces for Energy-Efficient Indoor Terahertz Wireless Communications
Why this work is in the frame
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Bibliographic record
Abstract
With the fifth-generation (5G) networks widely commercialized and fast deployed, the sixth-generation (6G) wireless communication is envisioned to provide competitive Quality of Service (QoS) in multiple aspects to global users. The critical and underlying research of 6G is, first, highly dependent on the precise modeling and characterization of the wireless propagation when the spectrum is believed to expand to the terahertz (THz) domain. Moreover, future networks’ power consumption and energy efficiency are critical factors to consider. In this research, based on a review of the fundamental mechanisms of reconfigurable intelligent surface (RIS)-assisted wireless communications, we utilize the 3-D ray-tracing method to analyze a realistic indoor THz propagation environment with the existence of human blockers. Furthermore, we propose a distributed RISs framework (DRF) to assist the indoor THz wireless communication to achieve overall energy efficiency. The numerical analysis of simulation results based on more than 2900 indoor THz wireless communication subscenarios has demonstrated the significant efficacy of applying distributed RISs to overcome the mobile human blockage issue, improve the THz signal coverage, and increase signal-to-noise ratios (SNRs) and QoS. With practical hardware design constraints investigated, we eventually envision how to utilize the existing integrated sensing and communication techniques to deploy and operate such a system in reality. Such a DRF can also lay the foundation of efficient THz communications for Internet of Things (IoT) networks.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it