Priority based VM2M communications over LTE
Bibliographic record
Abstract
In this paper, we describe an VM2M overlay architecture where the overlay acts as the control channel (CCH) of VANET to transfer safety messages between vehicles and RSU. The VM2M overlay network is implemented over dedicated preamble codes in the physical layer and the medium access control (MAC) layer modeled as the IEEE 802.15.4 carrier sense multiple access (CSMA/CA) mechanism. In this paper, we implement priority based VM2M where higher priority is given to VM2M in the physical layer. To this end, VM2M preambles are transmitted with higher power than H2H. Due to large cell sizes and/or higher vehicle velocities in urban, sub-urban, and rural areas, and on highways, VM2M overlay is implemented using preamble format 2 with total duration of 2 ms. We compare the non-priority scenario, in which H2H and VM2M traffic access the base station simultaneously with the same power, to VM2M priority-based scenario where each preamble of M2M overlay is transmitted with higher power than any of non-overlay ones. The VM2M priority-based scenario is found to increase the capacity of VM2M subnetworks compared with non-priority scenario, without undue deterioration of the capacity left for H2H/SCH traffic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".