Outage Probability Performance Analysis and Prediction for Mobile IoV Networks Based on ICS-BP Neural Network
Bibliographic record
Abstract
In the field of transportation, the Internet of Vehicles (IoV) is an important component of the Internet of Things. The vehicle-to-vehicle communication is particularly challenging in mobile IoV networks because they are operated in complex and highly variable environments. The mobile IoV transmission interruption level can be evaluated by the outage probability (OP) performance. If the OP performance can be analyzed and predicted accurately, the Quality of Service (QoS) in the mobile IoV networks can be improved. However, the analysis and prediction of mobile IoV transmission channels is very challenging because they are highly dynamic. In this article, the analysis and prediction of the OP performance for mobile IoV networks are investigated. A hybrid decode-amplify-forward (HDAF) relaying scheme with transmit antenna selection (TAS) is considered. The exact OP expressions are derived in a closed form, and the analytical results are verified. To realize the real-time analysis of the OP performance, an intelligent OP prediction algorithm based on the improved cuckoo search (ICS) is presented. The proposed algorithm is compared with different methods and the results show that it has a better OP prediction performance. The prediction accuracy of ICS-BP can be increased by 51.8% compared with the existing algorithms.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".