On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
Bibliographic record
Abstract
One of the major impediments in the design and operation of a full-duplex radio transceiver is the presence of self-interference (SI), that is, the transceiver’s transmitted signal, 60–100 dB stronger than the desired signal of interest. To reduce the SI signal below the receiver’s sensitivity before coupling it to the receiver, radio frequency (RF)/analog domain cancellation is carried out. Even after SI cancellation to the required level in the analog domain, the residual SI signal still exits and lowers the transceiver’s performance. For residual SI cancellation, a digital domain cancellation is carried out. RF impairments are the major obstacle in the residual SI cancellation path in the digital domain. Linearization of RF impairments such as IQ mixer imbalance in the transmitter and receiver chain, non-linear PA with memory, and non-linear LNA are also carried out. Performance evaluation of the proposed techniques is carried out based on SINR, the power of different SI signal components, PSD, output to input relationship, SNR vs. BER, spectrum analyzer, constellation diagram, and link budget analysis. The proposed techniques provide attractive RF/analog SI cancellation of up to 80–90 dB, digital residual SI cancellation of up to 35 to 40 dB, total SI cancellation of up to 110 to 130 dB, and an SINR improvement of up to 50 dB.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".