Impact of Channel Estimation Error on the Performance of Amplify-and-Forward Two-Way Relaying
Bibliographic record
Abstract
In this paper, the impact of channel-state information (CSI) estimation error on the performance of an amplify-and-forward two-way multiple relay network has been investigated. In contrast to the existing literature, which assumes perfect self-interference cancellation, we consider imperfect self-interference cancellation at both sources that exchange information through multiple relays, and maximal-ratio combining is then applied to improve the decision statistics under imperfect signal detection. We derive the effective signal-to-noise ratio (SNR) subject to noisy channel estimation, and based on this SNR, the system outage probability is given. In addition, we derive the closed-form expression of the average system bit error rate (BER) and the asymptotic expressions for both outage probability and BER. Furthermore, instead of employing all relays, we examine the impact of imperfect CSI on a single relay selection (RS) scheme. To mitigate the negative impact of imperfect CSI, we show that power allocation (PA), by minimizing either the outage probability or the BER, can suitably be cast as the geometric-programming problem. Numerical results validate the correctness of the derived expressions and show that the adaptive-PA scheme outperforms the equal-PA scheme under the aggregated effect of imperfect CSI.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".