Residual Neural Networks for Learning the Full-Duplex Self-Interference
Bibliographic record
Abstract
Full-duplex (FD) is a key technology for enhancing the capacity of next-generation wireless systems by jointly maximizing the utilization of time and frequency resources, resulting in low latency and high spectral efficiency. However, the self-interference (SI), leaking to the receiver chain from its own transmitter chain, is the main issue that hinders reaping the key benefits of FD systems, and SI cancellation (SIC) is introduced to enable such benefits. Digital non-linear SIC is traditionally performed using model-driven approaches, such as polynomial models, which are of high complexity. Thus, data-driven machine learning (ML) approaches are introduced for learning the FD non-linear SI with lower complexity. This paper proposes an ML approach based on residual neural network (Res-NN) to learn the FD non-linear SI and relax the computational requirements of the traditional methods. Res-NN uses shortcut connections from the input/hidden layer to the output layer to enhance the learning capabilities of the SI cancelers. Simulation results show that an NN employing residual connections could effectively learn the FD SI and outperform the existing benchmarks in the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".