Principles, Applications, and Challenges of Reconfigurable Intelligent Surface-Enabled Backscatter Communication: A Comprehensive Survey and Outlook
Bibliographic record
Abstract
Backscatter communication (BackCom) networks are expected to provide ultra-low-power transmission and massive connectivity in future wireless communication systems. Nevertheless, the double-fading effect significantly reduces signal intensity and is a primary problem of modern BackCom systems. Reconfigurable Intelligent Surface (RIS) can artificially modify the wireless environment through numerous controllable reflecting elements. With the help of RIS, the desired signals in the BackCom procedure can be captured and reflected in the specified direction, which is able to alleviate the double-fading problem. Specifically, RIS can be a backscatter device to send information instead of a collaborator to enhance transmission, yielding the RIS-BackCom mechanism. However, a comprehensive review of this technique still needs to be made available, dramatically limiting its development. This paper presents the fundamental principles and functions of legacy BackCom systems, the RIS technique, and RIS-BackCom networks. Then, we discuss the system-level performance with different modulation and channel estimation approaches. Further, we introduce symbiotic radio (SR) networks developed from the RIS-BackCom technique. After that, we provide a survey on diverse optimization issues existing in the applications of RIS-BackCom networks. Finally, we envision emerging use scenarios, potential challenges, and possible solutions in future wireless communication networks.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".