Joint Impact of Phase Error, Transceiver Hardware Impairments, and Mobile Interferers on RIS-Aided Wireless System Over <i>κ</i>-<i>μ</i> Fading Channels
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology that can potentially benefit the existing wireless communication technologies in addition to being able to fulfill the more stringent requirements of beyond-$5^{th}$generation/$6^{th}$generation wireless networks. Motivated by the numerous benefits of RIS technology for improving the performance of wireless communication systems, in this letter, a RIS-aided wireless system is considered in which the destination node is surrounded by the mobile co-channel interferers (CCIs). Each mobile CCI follows the random waypoint (RWP) mobility pattern within a circular region centered around the destination node. Source-destination, source-RIS, RIS-destination, and each interferer-destination links follow the$\kappa - \mu $distribution. Additionally, a more realistic system model is considered that also incorporates the impact of transceiver (transmitter as well as receiver) hardware distortions. The system’s performance is evaluated by deriving novel closed-form expressions for the coverage probability (CP) and ergodic capacity (EC) based on the cumulative distribution function and probability density function (PDF) of the received signal-to-interference-plus-distortion-plus-noise ratio (SIDNR).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".