Dynamic Control of Reconfigurable Intelligent Surfaces: An IC-Based MOS Varactor Approach
Bibliographic record
Abstract
Reconfigurable intelligent surfaces (RISs) are recognized as a fundamental enabler for improving energy efficiency in 6G and future networks. However, the power consumption and the reconfiguration delay still need improvement for what is required at GHz frequencies, thus delaying their commercial adaptation. On that regard, this study proposes the incorporation of Integrated Circuits (ICs) with MOS varactor loadings as part of the RIS framework, to improve power consumption and speed, while having precise tuning of the reflection phase for individual unit-cells. The presented circuit design features an asynchronous digital circuit responsible for transmitting binary streams to digital-to-analogue converters, which in turn, bias MOS varactors that are directly connected to each unit-cell within the RIS. The use of asynchronous digital control circuits facilitates the development of ultra-low power, high-speed ICs, thereby enhancing the dynamic scalability of the RIS system. Simulated results of the asynchronous circuit are presented on a mature, cost-effective, CMOS 0.18 μm process technology, showing static power consumption of 40,63 μW, dynamic energy consumption of 474.43 pJ and reconfiguration delay of 23.38 ns. The simulations are accompanied by a scalability analysis and a discussion of potential capabilities, offering valuable insights for the future of ICs on RIS systems. The proposed approach and circuit provide flexibility and performance to RIS systems not achievable with conventional control systems due to their benefits of using clockless networking communication.
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 | 0.000 |
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 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".