Power Allocation for Buffer-Aided Full-Duplex Relaying With Imperfect Self-Interference Cancelation and Statistical Delay Constraint
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Bibliographic record
Abstract
This paper considers source and relay power allocation for buffer-aided full-duplex (B-FD) relaying network, assuming constant data rate arrivals at the source buffer. Statistical delay constraint is imposed, where the end-to-end queue length is allowed to exceed a pre-defined queue-length threshold with a maximum acceptable queue-length-outage probability. We assume imperfect self-interference (SI) cancelation, where the non-zero residual SI power is modeled to be proportional to the relay transmit power. We investigate two power allocation problems for source arrival rate maximization: 1) B-FD relaying with adaptive power allocation (B-FD-APA) when the instantaneous channel state information at the transmitters (CSIT) is available and 2) B-FD relaying with static power allocation (B-FD-SPA) when only the statistical CSIT is available. To solve the problems, we first employ asymptotic delay analysis to transform the statistical delay constraint into more tractable constraints. Then, the optimal solutions are derived using Lagrangian approach. In addition, solutions for various special cases of residual SI and delay constraint are presented. With B-FD-APA, the relay can opportunistically switch between half-duplex (HD) and FD operation modes according to the channel conditions. With B-FD-SPA, the relay always employs FD mode. Numerical results are performed to compare the capacities of the proposed B-FD, non-buffer FD, and buffer-aided HD relaying schemes, as well as direct transmission (DT) under various settings, demonstrating the effectiveness of B-FD relaying to support delay-constrained communications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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