Self-Interference Cancellation With Nonlinearity and Phase-Noise Suppression in Full-Duplex Systems
Bibliographic record
Abstract
This paper addresses the self-interference (SI) cancellation for full-duplex operation in the presence of imperfect radio-frequency (RF) components. In particular, we develop a new scheme to jointly estimate and cancel the in-phase/quadrature mixer imbalance, power amplifier nonlinearities, up-/down-conversion phase noise, and the SI channel. First, we develop a detailed baseband model that captures the most significant transceiver RF imperfections, for both separate- and common-oscillator structures used in the up- and down-conversions. Then, a basis expansion model is derived to approximate the time-varying phase noise and to transform the problem of estimating the time-varying phase noise into the estimation of a set of time-invariant coefficients. Subsequently, the likelihood function is derived in the presence of the unknown intended signal to formulate the joint estimation of the intended channel, SI channel, nonlinear impairments, and phase noise, under the maximum likelihood (ML) criterion. An iterative procedure is developed to find the ML estimate of the different parameters based on the known transmitted data, the known pilot symbols, and the statistics of the unknown intended signal received from the intended transmitter. The full use of the received signal significantly reduces the required number of pilot symbols as compared to training-based techniques. We consider the two pilot-insertion structures used in LTE for the frequency-multiplexed pilots and the time-multiplexed pilots. Simulation results indicate that the proposed ML algorithms can offer a superior SI-cancellation performance with the resulting intended-signal-to-SI-and-noise ratio very close to the intended-signal-to-noise ratio.
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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 | 0.001 |
| 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".