Performance Analysis for Downlink Transmission in Multiconnectivity Cellular V2X Networks
Bibliographic record
Abstract
With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge. Recently, multiconnectivity technology has become a promising network access paradigm for improving network performance and reliability for C-V2X in the 5G and B5G era. To this end, this article proposes an analytical framework for the performance of downlink in multiconnectivity C-V2X networks. Specifically, by modeling the vehicles and base stations (BSs) as 1-D Poisson point processes, we first derive and analyze the joint distance distribution of multiconnectivity. Then through leveraging the tools of stochastic geometry, the coverage probability and spectral efficiency are obtained based on the previous results for general multiconnectivity cases in C-V2X. Additionally, we evaluate the effect of the path-loss exponent and the density of downlink BS on system performance indicators. We demonstrate through extensive Monte Carlo simulations that multiconnectivity technology can effectively enhance network performance in C-V2X. Our findings have important implications for the research and application of multiconnectivity C-V2X in the 5G and B5G era.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".