Robust Optimization of SC-FDE-Based Multihop DF Relay Systems With Imperfect CSI
Bibliographic record
Abstract
In this paper, we consider the robust transceiver optimization for a single-carrier frequency-domain equalization (SC-FDE)-based multihop half-duplex decode-and-forward (DF) relay system under imperfect channel state information (CSI). The goal is to maximize the system achievable bit rate (ABR) and to minimize the end-to-end bit error rate (BER) subject to a joint node power constraint. Due to the lack of analytical tractable expressions for ABR and BER under imperfect CSI, we resort to lower bounds of the hopwise ABRs and BERs and reformulate the original problem by using two approximated objective functions based on these lower bounds. For the reformulated problems, we show that the optimal equalization filters take the form of robust Wiener filters. Subsequently, we propose two efficient decentralized algorithms to obtain the optimal solutions for the resulting power-allocation problems. Numerical results are provided to confirm the ABR and BER performance of the proposed robust relaying schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".