Unit Cell Phase-Frequency Profile Optimization in RIS-Assisted Wide-Band OFDM Systems
Bibliographic record
Abstract
The reflection characteristics of a reconfigurable intelligent surface (RIS) depend on the reflection response of the constituent unit cells, which are necessarily frequency dependent. This paper investigates the role of an RIS comprised of unit cells with different phase-frequency profiles in improving the achievable rate of a wide-band orthogonal frequency division multiplexing (OFDM) system. Specifically, we propose phase-frequency profiles with both variable phase and variable slope that enable improvements in the spectral efficiency of a channel. We first propose a mathematical model for the frequency response of the reflection coefficient of a realizable RIS unit cell and parameterize the phase-frequency profile by its slope and by its resonance center frequency. Then, modelling each RIS element with b control bits, we propose a method for selecting the parameter pairs to obtain a set of$2^{b}$reflection profiles. We then use a low-complexity optimization algorithm to maximize the data rate through the joint optimization of (a) the reflection profile for each RIS unit cell from the available sets and (b) the power allocations across the sub-carriers. We show that the resulting RIS outperforms existing designs over a wide range of user locations in single-input single-output and multi-user multiple-input single-output OFDM systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".