RIS-IoE for Data-Driven Networks: New Mentalities, Trends and Preliminary Solutions
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".