Optimized phase shifts in intelligent reflective surfaces for robust radar-based indoor coverage enhancement
Bibliographic record
Abstract
Intelligent reflective surfaces (IRS) have garnered significant attention due to their potential to intelligently shape the wireless environment for beyond-fifth and sixth-generation wireless communication and sensing. IRSs modify radio channels by dynamically adjusting the phase shifts of reflected signals, thereby enhancing signal reception and suppressing interference. For robust beam steering and signal-to-noise ratio optimization, the IRS’s phase profile must be accurately estimated and optimized. This work presents a unique approach for estimating the position and orientation of an indoor IRS using active tags and optimizing the phase shifts with a tag-FMCW radar system for robust radar-based IRS-assisted indoor coverage enhancement. The proposed method offers a practical scheme for localizing the IRS elements and calculating the phase delays effectively, ultimately improving the overall performance of IRS-assisted wireless networks. For implementation, we proposed non-volatile phase-change radio-frequency switches that can operate over a broad range and can provide flexibility in implementation from DC to 67 GHz for 5G-Advanced applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".