Self-interference cancellation for full-duplex MIMO transceivers
Bibliographic record
Abstract
Full-duplex operation requires effective self-interference (SI) cancellation that in turn needs reliable SI channel estimation. In this paper, we develop two estimation algorithms suitable for a 2-stage SI cancellation structure. By exploiting the sparsity of the SI channel, we first derive a compressed sensing-based SI channel estimation algorithm to be used in the first SI cancellation stage at radio-frequency (RF) to reduce the SI. We then develop a subspace-based algorithm to jointly estimate the residual SI channel, the intended channel and the transmitter nonlinearities for the second SI cancellation stage at baseband. Including the intended received signal in the estimation process is the main advantage of the proposed algorithm as compared to previous works that assume it as additive noise. Simulation results show that the proposed algorithms outperform the least-square (LS) algorithm and offer higher signal-to-residual-interference-and-noise ratio (SINR) over a large received signal-to-noise ratio (SNR) range.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".