Impact of Channel Estimation Error on the Performance of Amplify-and-Forward Two-Way Relaying
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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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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 | 0.000 |
| 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