Sum-rate performance and impact of self-interference cancellation on full-duplex wireless systems
Bibliographic record
Abstract
We consider full-duplex (FD) bidirectional communication between a pair of nodes and investigate the impact of residual self-interference on sum-rate performance. We first analyze a situation where channel state information is available only at receiver (CSIR). For this case, we derive an exact expression and a lower bound to the sum-rate performance of FD and hence characterize the effect of residual self-interference. The study shows that, FD sum-rate performance is limited by the effective signal-to-residual self-interference power ratio (effective SIR). In particular, for a fixed effective SIR, FD achieves almost twice the sum-rate of half-duplex (HD) in low signal-to-noise ratio (SNR) regimes whilst FD performance is surpassed by HD in high SNR regions. A closed-form approximation to this crossover SNR is derived. We then investigate the sum-rate of FD assuming channel state information is available to both transmitter and receiver (CSIT). Comparison of FD sum-rates of CSIR and CSIT shows that, in low SNR regions, a significant benefit can be achieved with CSIT while the gain is small in high SNR levels.
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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 | 0.004 |
| 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 | 0.001 |
| 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".