A digital subspace-based self-interference cancellation in full-duplex MIMO transceivers
Bibliographic record
Abstract
This paper addresses the problem of digital self-interference (SI) cancellation in full-duplex systems. Under practical transmitter imperfections, the received SI is affected by transmitter nonlinearities and propagation channel, which need to be estimated in order to cancel the SI. The proposed estimation method is based on subspace decomposition. The major detriment of subspace technique is the need of oversampling or multisensor receiver to obtain a nondegerate noise subspace. We modify the traditional subspace techniques by exploiting the covariance and the pseudo-covariance of the received signal. This enables us to increase the dimension of the received signal without resulting to oversampling or multisensor receiver. The different parameters are estimated, up to an ambiguity term, without any knowledge of the intended signal. We develop a joint detection and ambiguity identification procedure that requires a considerably smaller number of pilots than standard training-based methods. Simulation results show that the proposed algorithm can properly estimate the SI channel coefficients and the nonlinear parameters without any pilot symbol from the intended transmitter.
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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 | 0.001 |
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".