Low-Overhead Kronecker-Based Intelligent Reflective Surfaces for 5G and Beyond
Bibliographic record
Abstract
Intelligent Reflective Surfaces (IRSs) typically require element-wise control, which necessitates complex wiring systems and leads to higher power consumption. Additionally, the number of control signals needed for each configuration equals the number of IRS elements, resulting in substantial control overhead and challenges in achieving rapid configuration changes. In order to not only simplify the control mechanism but also enhance the overall efficiency of IRS-assisted wireless networks by mitigating these drawbacks, we present a novel approach for controlling the phase shifts introduced by IRS elements. This approach incorporates a Kronecker structure into the phase-shifting matrix, enabling more efficient implementation and control. Consequently, our method significantly reduces the number of control signals required compared to traditional element-wise control methods. We jointly optimize active and passive beamforming for downlink communication in a controllable IRS-assisted wireless network to evaluate the performance of our proposed method.
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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".