Successive Two-Way Relaying for Full-Duplex Users With Generalized Self-Interference Mitigation
Bibliographic record
Abstract
In this paper, we propose a novel successive two-way relaying (STWR) system that uses a pair of conventional half-duplex (HD) relays to mimic a full-duplex two-way relay (FD-TWR). Although classical FD-TWR is spectral efficient and expands cell coverage, the proposed STWR utilizes the existing HD infrastructure to boost the FD implementation and offers bi-directional data exchange and low-complexity residual self-interference (RSI) mitigation. To formulate STWR, we develop a unified signal model to facilitate the mitigation of the generalized self-interference (GSI). GSI consists of back-propagating interference due to two-way relaying, RSI of FD sources and inter-relay interference caused by the pairs of HD relays. Because the GSI channel matrix has a distinct row linearity, we propose an efficient digital approach to remove the GSI and design two low-complexity algorithms. These algorithms avoid RSI channel estimation, full-rank matrix, and complex matrix computation. Our analysis and simulations show that: 1) the proposed STWR achieves the multiplexing gain of the true FD-TWR; 2) the distance between the two HD relays should be optimized to achieve the highest spectral efficiency; and 3) the STWR system with two algorithms can achieve a diversity order of one or two, respectively. Therefore, the STWR concept achieves a flexible tradeoff between performance and complexity, potentially enabling large-scale relay deployments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".