A Comprehensive Analysis of Packet Delay in Reconfigurable Intelligent Surface-Assisted Communication Networks
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) technology has been widely used to enhance the performance of wireless communication networks. Specifically, to overcome the blockage issue, the RIS is equipped with passive elements that can steer the wireless signals around the obstacle and construct a virtual line-of-site (LoS) link between the transmitter and receiver. This paper presents a comprehensive analysis of packet delay in RIS-assisted communications, an area that has received limited attention in the literature. Our analysis carefully examines the cascaded wireless channel via RIS to calculate the signal-to-noise ratio (SNR) distribution within Nakagami-m fading channels. Furthermore, we utilize the SNR distribution to develop a novel expression for average packet delay. Our method was evaluated thoroughly through numerical and simulation-based performance testing. We explored the impact of various parameters on average delay performance, including the number of reflecting elements, average SNR, and distance between the user and RIS. Our research indicates that a larger number of passive reflecting elements in an RIS can significantly improve signal reception at the user, leading to better average packet delays. The findings of this paper can be used in multi-user scenarios, where the packet delay is one of the metrics used to address the user-RIS association problem based on the user's delay requirements.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".