RIS-IoE for Data-Driven Networks: New Mentalities, Trends and Preliminary Solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) enables an intelligent and programmable communication environment for future sixth-generation (6G) wireless networks, owing to its native passive reflecting and smart phase shifts adjustment. To support the ultra data process for the Internet of Everything (IoE), in this article, new mentalities are investigated in details, such as artificial intelligence (AI) driven RIS, their corresponding designs, deployments, and optimizations. Considering applications and implementations with RIS, the integrating of emerging technologies is also studied to provide a significant performance enhancement in terms of the achievable capacity, power consumption and transmitting security, including physical layer security (PLS), simultaneous wireless information and power transfer (SWIPT), non-orthogonal multiple access (NOMA) and unmanned artificial vehicle (UAV). Then, to address the challenge of channel estimations, RIS-NOMA networks are comprehensively investigated with a simple case study, where the tough issue can be tackled by means of proposed decoding principles. Furthermore, future research trends and open issues of RIS-IoE networks are summarized associated with rate splitting multiple access (RSMA), massive multiple-input multiple-output (mMIMO), and millimeter wave (mmWave), providing constructive directions for the subsequent study.
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.
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 | 0.001 | 0.001 |
| 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 it