Aggregated V2I Communications for Improved Energy Efficiency Using Non-Orthogonal Multiplexed Modulation
Bibliographic record
Abstract
Nowadays, data traffic in-car communication is increasing dramatically, due to the emerging technology of self-driving and on-board infotainment applications. The direct connections between vehicles and cellular infrastructures will introduce significant signalling overhead and excessive energy consumption, especially for congested and fast moving traffic. In order to improve energy efficiency and achieve green networking, an heterogeneous network, 5G-Vehicular Ad Hoc Network (5GVANET) is presented in this paper, which coupling the high data rates of VANET and the wide coverage area of 5G. In this integrated architecture, vehicles are clustered accordingly, and one vehicle in each cluster is selected as a gateway to support aggregated traffic. To ensure the capacity of the trunk link between the gateway and base station, a Non- orthogonal Multiplexed Modulation (NOMM) scheme is proposed in this paper to effectively aggregate the Vehicle-to-Infrastructure (V2I) traffic and further improve energy efficiency. NOMM splits data stream of each user into multi-layers and modulate them simultaneously. Sparse spreading code is also applied in partially superposing the modulated symbols on several resource blocks. Furthermore, we analyzed the energy efficiency of proposed NOMM scheme and traditional M-QAM theoretically. It was also validated by simulation results that NOMM provides less power consumption than M-QAM modulation.
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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 | 0.000 | 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) | 0.001 | 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".