Digital Baseband Self-Interference Cancellation Using Fractionally-Spaced Finite-Impulse-Response Structure for Full-Duplex MIMO
Bibliographic record
Abstract
In this work, a fractionally spaced transversal finite impulse response (FS-FIR) structure is proposed for digital baseband (BB) self-interference cancellation (SIC) in full-duplex (FD) multiple-input multiple-output (MIMO) wireless communications systems. We develop three algorithms based on Minimum-Mean-Square-Error (MMSE), Least-Square (LS), and Maximum-Likelihood (ML) criteria, to estimate the self-interference channel impulse response and optimize the FS-FIR-SIC coefficients. Performance of the proposed FS-FIR-SIC is evaluated under different oversampling rates and FIR spans. Illustrative results reveal that the FS-FIR structure greatly improves the SIC performance with an oversampling rate larger than 1 by avoiding possible aliasing due to sampling at the symbol rate. Increasing FIR filter span can further enhance the SIC performance, although the improvement is reduced at a large FIR span. Meanwhile, the ML-FS-FIR-SIC outperforms the LS-FS-FIR-SIC and MMSE-FS-FIR-SIC, especially in the presence of the intended received signal.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".