Residual self-interference after cancellation in full-duplex systems
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Bibliographic record
Abstract
We investigate the signal-to-residual-interference ratio (SIR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> ) in a full-duplex transceiver with analog self-interference cancellation in consideration of three major sources of imperfection: (i) self-interference channel estimation error, (ii) quantization error in the receiver analog-to-digital converter (ADC), and (iii) quantization error in the digital-to-analog converter (DAC) used to generate the self-interference replica. In particular, we first derive the Cramér-Rao lower bound on the variance of the self-interference channel estimation error, and use it to further develop a closed-form expression of the SIR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> . The resulting SIR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> expression facilitates a study of the limit of a full-duplex system and determines the minimum required resolution for the ADC and DAC in order to meet a given performance. The expression reveals that, with a sufficiently high number of bits, the effects of ADC and DAC are negligible, but the cancellation performance is limited by the thermal noise and, in the best case, we can obtain a SIR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> equal to the received signal-to-thermal-noise ratio (SNR).
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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 | 0.000 |
| 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