Full-duplex decode-and-forward relaying with joint relay-antenna selection
Bibliographic record
Abstract
Abstract This paper is concerned with wireless relay networks that employ K full-duplex (FD) decode-and-forward relays to help a source to communicate with a destination. Each FD relay is equipped with multiple antennas, some for receiving and some for transmitting. The paper considers joint relay-antenna selection schemes that are based on the instantaneous channel conditions for two cases of antenna configurations, namely fixed antenna configuration (FAC) and adaptive antenna configuration (AAC). Under FAC, the transmit and receive antennas at each relay are fixed, whereas in the case of AAC an antenna at a relay can be configured to be either a transmit or a receive antenna.In addition to equal power allocation between the source and selected relay, a power scaling approach to counteract the effect of residual self-interference is also examined. Closed-form expressions of the outage probability and average capacity are obtained and provide important insights on the system performance. The accuracy of the obtained expressions are corroborated by simulation results. In particular, it is shown that under FAC and without power scaling, the diversity order approaches K as the self-interference (SI) level gets smaller, while it approaches zero whenever the SI level is nonzero and the SNR increases without bound. Under FAC and with power scaling, the diversity order approaches K for any SI level. For the case of AAC and without power scaling, the diversity order approaches 2K for small SI level. When power scaling is applied in AAC, the diversity order approaches 2K at any SI level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".