Performance analysis for IRS‐aided communication systems with composite fading/shadowing direct link and discrete phase shifts
Bibliographic record
Abstract
Abstract Intelligent reflecting surface (IRS) is an emerging technology and serves as a key component of any smart radio environment. In this article, we consider an IRS that assists communication of a direct link between a single‐antenna transmitter and a receiver. It is assumed that a direct link experiences composite fading/shadowing and is modeled by Generalized‐Kdistribution. Moreover, IRS has line‐of‐sight (LoS) paths, therefore, Rician distribution is used to characterize the fading for these paths. We also consider phase errors that exist due to discrete number of phase shifts. We derive an approximation for the end‐to‐end signal‐to‐noise ratio (SNR) which shows that an amplitude of the direct link is increased by a positive offset because of the presence of IRS. Based on this SNR approximation, a new statistical framework that includes cumulative distribution function (CDF), probability density function (PDF), and moment generating function (MGF) is developed. Leveraging this statistical framework, we derive new and accurate closed‐form approximations for the outage probability, average error probability, ergodic capacity and generalized moments. It is shown that the considered IRS‐aided system (IAS) achieves much better performance even with a few numbers of phase shifts as compared to other baseline systems. Simulations are also provided which verify the tightness of our derived approximations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".