Design of a Dual Functionality Self-Powered Reconfigurable Intelligent Surface with Dual-Polarization Control
Bibliographic record
Abstract
The practical deployment of Reconfigurable Intel-ligent Surfaces (RIS) is hindered by the significant challenge of powering their numerous active elements. To address this limitation, we propose and demonstrate a self-sustained RIS architecture based on a multifunctional unit cell that integrates electromagnetic wave control and RF energy harvesting. Our approach enables fully autonomous operation by harvesting energy directly from ambient incident waves, thus eliminating the need for external power supplies or batteries. The core of our contribution is a novel unit-cell design that exploits polarization orthogonality to achieve two simultaneous, non-interfering functions. Under x-polarized excitation, the cell provides a controllable 1-bit phase reflection using a PIN diode, while it concurrently harvests energy from y-polarized waves through a dedicated absorption port. Optimized for the X-band (9.4–10.6 GHz), the proposed unit cell demonstrates excellent performance, achieving a reflection amplitude better than −2 dB, a stable phase difference of 180° ± 20°, strong energy harvesting absorption below −15 dB, and high polarization isolation exceeding 20 dB. This work validates a practical pathway toward energy-autonomous RIS platforms, paving the way for their large-scale and sustainable deployment in future wireless networks.
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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".