Utilizing IQ Mixers for Phase Noise Cancellation In Full-Duplex Architectures.
Bibliographic record
Abstract
Full-duplex transceivers are typically used in both RADAR and RFID applications, as this architecture transmits and receives at the same time, a problem referred to as self-interference occurs.The transmitted signal leaks into the receiver and the phase noise of the leaked signal interferes with the returned receive signal.This thesis proposes, analyzes and measures a novel method of self-interference cancellation in fullduplex transceivers through the use of an IQ mixer and system design constraints.The original Local Oscillator of the transmitter is coupled and used for down conversion, if the system can be assumed to be linear, and time-invariant the phase noise of the self-interference can be estimated, and significantly reduced.This method was shown to achieve cancellation of up to 32.6 dB, however theoretically the limits of this method are constrained by the sensitivity of the measurement and matching systems used.On-Off Keyed Signal . . . . .
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".