On the Performance of NOMA-Enabled V2V Communications Under Joint Impact of Nodes Mobility and Channel Estimation Error
Bibliographic record
Abstract
In this paper, we study the joint impact of nodes’ mobility and channel estimation error (CEE) on the performance of non-orthogonal multiple access aided vehicular communication system. Specifically, we derive exact outage probability expressions for both near user and far user vehicles over Nakagami-m fading channels. We then analyze the asymptotic outage behavior in the high signal-to-noise ratio regime, from which valuable insights related to system’s diversity order are quantified. Furthermore, some special cases of interest are also formulated to quantify the impact of nodes’s mobility and/or CEE on the system’s outage performance. Analytical expressions of the average bit error rate and ergodic capacity for both near and far user vehicles are further derived under Nakagami-m fading channels. Finally, we corroborate our analytical findings with the numerical and simulation studies, and demonstrate the effects of transmit power, fading severity parameters, nodes’ mobility parameters, and CEE parameters on the system’s performance.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| 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".