Analysis of Static and Dynamic Scenarios of MIMO Systems for Physical Layer Modeling for Vehicular Communication
Bibliographic record
Abstract
In this paper, the physical layer for a multi-antenna vehicular communication channel has been investigated considering the effects of antenna types, antennas' orientation and position through a ray-tracing simulation. The simulation software utilized is Wireless InSite® from Remcom Inc. Both static and dynamic scenarios are considered. Multiple transmitters and receivers were spread on the rear, center and front of two vehicles separated by 10m distance. A car with a height more than transmitting and receiving end car was placed in between them to study the vehicular channel behavior in complete Non-Line of Sight (N-LOS) scenarios. To simulate the dynamic scenarios, the response of each receiver with respect to each transmitter was analyzed at a static situation. Then, the entire set up (transmitting, receiving and blocking cars) was moved to a new position, to examine the variation in the Signal to Noise Ratio (SNR) as well as multipath contributions in the changes in channel capacity. Both Omni directional and directional antennas were studied. Different antenna orientations were adopted in the case of directive horn antennas to analyze the effect of antenna directivity on improving the channel efficiency. Furthermore, incorporating the concept of Multiple Input and Multiple Output (MIMO), antenna selection both at the transmitter and receiver sides were used to evaluate the channel capacity of a Vehicle to Vehicle (V2V) communication system in respect to antenna position and car locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".