Performance Evaluation of Relaying with Different Relay Selection Schemes in 5G NR V2X Communications
Bibliographic record
Abstract
This paper presents the performance of relay selection schemes in 5G New Radio (NR) network. The 5G NR is anticipated to enable reliable vehicular networking, with emphasis on Vehicle-to-Everything (V2X) communications. Here, 5G NR is required to support V2X Side Link (SL) communications for C-Plane signaling via wireless Uu interface, which naturally suffers from fading and path loss. Therefore, relaying can address this concern by deploying a low power relay node (RN) in 5G NR network. This paper presents the end-to-end (e2e) performance of dual-hop relaying under four different relay selection schemes and different RN density/deployment scenarios. These selection schemes comprised of the combinations of average and/or instantaneous wireless channel conditions of the two links involved in the transmission, wireless backhaul relay link (RL) and access link (AL). Simulation results demonstrate that when relay selection is performed relying on the instantaneous e2e link conditions, the best performance in terms of average e2e data rate can be obtained. Moreover, higher RN density in network, yields enhanced performance, due to the availability of additional paths to select from, especially, when RNs are randomly deployed rather than at fixed locations.
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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 | 0.005 |
| 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 | 0.000 |
| 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".