Introduction to Intelligent Surfaces
Bibliographic record
Abstract
The advancing radio environment reconfiguration technique has recently emerged as a promising new paradigm to achieve smart and highly controllable ratio propagation channels for next-generation wireless communication systems. Generally, an intelligent surface is a planar surface comprising a large number of passive reflecting/refracting elements, each of which can induce a controllable amplitude and/or phase change to the incident signal independently. With a dense deployment of smart surfaces in a wireless network and smartly coordinating their reflection/refraction, the signal propagation/radio between transmitters and receivers can be flexibly reconfigured to achieve the desired realizations and/or distributions. Motivated by the above advantages, it is foreseen that intelligent surfaces will bring fundamental paradigm shifts in wireless network design in the future. Several new trends in intelligent surface applications are being investigated, including the transition from single surfaces to network-level surfaces, from passive surfaces to active and hybrid surfaces, from reflective/refractive surfaces to omni-surfaces, from ground surfaces to integrated air-ground surfaces, from fixed surfaces to mobile surfaces, and from simple connection surfaces to beyond diagonal surfaces.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".