Spectral–Energy Efficiency Tradeoff in Full-Duplex Two-Way Relay Networks
Bibliographic record
Abstract
Owing to its high spectral efficiency (SE), two-way relaying (TWR) has aroused tremendous research interests. Recently, substantial progress in self-interference (SI) cancelation makes full-duplex (FD) TWR practical. For this new paradigm, analyzing spectral-energy efficiency (SE-EE) tradeoff is crucial, which has not been addressed in the existing works in the literature. In this paper, the SE-EE tradeoff in a FDTW relay network with amplify-and-forward (AF) relaying is studied by considering the residual SI at the relay. An optimization problem is formulated to maximize the EE under the SE requirement and the maximum transmission power constraints by adjusting the transmission power of the terminals and the amplification gain of the relay. A lower complexity iterative optimization algorithm is developed to solve the optimization problem. Simulation results show that: 1) the proposed algorithm can achieve optimal EE that is consistent to the one obtained by the exclusive searching method; 2) the FDTW relay network can achieve higher SE but lower optimal EE compared with the half-duplex (HD) one; and 3) the optimal EE is insensitive to the residual power of SI, when the relay is located near either terminal.
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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 | 0.000 |
| 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 | 0.000 |
| 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".