Self-Interference Pricing-Based MIMO Full-Duplex Precoding
Bibliographic record
Abstract
Precoding designs for bidirectional full-duplex (FD) systems have been proposed as potential ways to suppress the effects of self-interference and improve the spectral efficiency of wireless systems. This letter proposes the self-interference pricing based full-duplex precoding (FDP-SIP) algorithm that applies pricing-based precoding at the transmitter to suppress the self-interference prior to the receiver low-noise amplifier (LNA) and analog-to-digital converter (ADC) to avoid overloading while ensuring the receiver linearity. The proposed FDP-SIP algorithm can be implemented without the need of active cancellation at the receiver. Simulation results demonstrate the effectiveness of the proposed FDP-SIP algorithm using both channel models and measured data. In particular, based on the measured data, FDP-SIP algorithm provides the sum-rate nearly 1.8 times that of an optimized half-duplex (HD).
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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) | 0.000 | 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) | 0.001 | 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".