Outage Probability of Two-Way Full-Duplex AF Relay Systems over Nakagami-m Fading Channels
Bibliographic record
Abstract
In this paper, performance of two-way full-duplex cooperative systems under residual loop- interference (LI) is analyzed in terms of outage probability over Nakagami-$m$ fading channels. At the relay node of the proposed system, physical- layer-network-coding technique and variable-gain full-duplex amplify-and-forward relaying method are combined for two-way transmission. End-to-end signal-to-interference-plus-noise-ratio (SINR) expression is derived for different power transmissions at the source and relay nodes. New exact outage probability expression is obtained in a single-integral form by using cumulative distribution function of the end-to-end SINR. The analytical results are verified by Monte-Carlo simulations. We also provide lower-bound and asymptotic expressions in closed-form for the outage performance. It is shown that the outage performance of the system is enhanced as long as either the transmit power or the efficiency of LI cancellation process increases. We also observe that the outage probability converges to an error floor due to the residual LI component at the source and relay nodes.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| 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".