Influence of Channel Fading and Capture for Performance Evaluation in Vehicular Communications
Bibliographic record
Abstract
Autonomy and intelligent transportation systems (ITSs) have recently received increased interest for vehicular ad hoc networks (VANETs). In addition, the impending 5G and 6G technologies will result in substantial advancements for VANETs. The IEEE 802.11p summarizes specifications of physical (PHY) and medium access control (MAC) layers for VANETs. Although IEEE 802.11p MAC performance has been investigated, analytical methods need improvement. Bit error and channel capture influence the performance of vehicular communications in real-world transmission. These effects are investigated separately in previous works. In this article, an extensive study is provided that integrates these two major factors. In VANETs, the influence of channel fading and capture on IEEE 802.11p is investigated analytically using a Markov chain model. For Nakagami-m, Rayleigh, and Rician fading channels, performance-impacting factors are considered, and the relationships between parameters as well as performance metrics are derived. The probability of unsuccessful and successful transmission, outage probability, probability of frame capture, throughput, bit error rate (BER), and delay terms are attained. Moreover, simulation results are provided, which verify analytical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".