Impact of Co-Channel Interference and Vehicles as Obstacles on Full-Duplex V2V Cooperative Wireless Network
Bibliographic record
Abstract
Vehicular communications, with their promise to provide drivers and passengers with a wide range of applications, are attracting significant attention from both research and industry. In this paper, we study the performance of full duplex amplify and forward (AF) relaying-based vehicle-to-vehicle (V2V) cooperative wireless communications over Nakagami-m fading channels. In such systems, in practical scenarios, the communication link inevitably suffers from co-channel interference, residual self-interference, and blockage from other vehicles on the road. In this context, we consider independent and not necessarily identically distributed (i.n.i.d) Nakagami-m fading channels and derive novel exact and asymptotic outage probabilities of the exact equivalent and approximated signal-to-interference-plus-noise ratio (SINR), respectively. Building on this, the end-to-end exact and asymptotic outage probabilities are expressed in terms of the blockage probability and then used to evaluate the throughput of the proposed system. In addition, a lower bound to the symbol error rate of the considered system is also derived. Monte-Carlo simulation results are provided to demonstrate the accuracy of the proposed analytical expressions. The results demonstrate the significant impact of the considered interference and blockage on the system performance. Precisely, it is shown that the system performance is degraded when the average height of the obstacles is increased. This highlights the importance of taking into account these phenomena in the performance evaluation in order to assess the practical limit of V2V cooperative wireless communications.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".