On the Performance of Training-Based IRS-Assisted Communications Under Correlated Rayleigh Fading
Bibliographic record
Abstract
The channel state information (CSI) is crucial in communication systems assisted by intelligent reflecting surfaces (IRSs). This paper is on the phase estimation of individual channels in IRS-assisted communication systems with single-antenna transceivers under the correlated Rayleigh fading. We consider both the fully-active-IRS where all IRS elements are active and the hybrid-IRS where partially IRS elements are active, where an active IRS element is equipped with a sensing device for pilot signal reception. We derive the maximum likelihood (ML) estimator for channel phases of all IRS elements based on the observations on active IRS elements. This estimator is also proved to be the minimum mean square error (MMSE), maximum a posterior (MAP) and minimum mean absolute error (MMAE) estimators. Then we conduct performance analysis in terms of the gain and the capacity of the cascaded transmitter-IRS-receiver channel with perfectly known and estimated individual channel phases. Numerical results are provided to show that the performance of IRS-assisted communication systems with our proposed phase estimator is close to that with perfect CSI and validate our theoretical analysis.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".