Robust MMSE design for full-duplex decode-and-forward SC-FDE relay systems
Bibliographic record
Abstract
In this paper, we consider the robust transceiver design for a two-hop full-duplex decode-and-forward relay system employing single-carrier transmission with frequency-domain equalization (SC-FDE). The design problem for the transmit precoding and receive equalization is formulated as an optimization problem with the objective to minimize the sum mean-squared error (MSE) of the two hops subject to separate node transmit power constraints. We show that the equalization filters can be optimized individually at the receiving nodes and take the form of robust Wiener filters. However, due to the loopback interference, the transmissions in the two hops are coupled and the transmit precoding problem boils down to a non-convex power allocation problem in the frequency domain. An alternating optimization approach is proposed to obtain the power allocation where convex programming problems and difference of convex programming problems are solved in an alternating manner. Numerical results are provided to validate the MSE and the achievable rate of the proposed robust schemes, showing that significant performance gains can be achieved compared to conventional half-duplex systems and non-robust full-duplex designs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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".