Impact of Co-Channel Interference and Vehicles as Obstacles on Full-Duplex V2V Cooperative Wireless Network
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it