Real-time adaptive optical self-interference cancellation for in-band full-duplex transmission using SARSA(λ) reinforcement learning
Bibliographic record
Abstract
Self-interference (SI) due to signal leakage from a local transmitter is an issue in an in-band full-duplex (IBFD) transmission system, which would cause severe distortions to a receiving signal of interest (SOI). By superimposing a local reference signal with the same amplitude and opposite phase, the SI signal can be fully canceled. However, as the manipulation of the reference signal is usually operated manually, it is difficult to ensure a high speed and high accurate cancellation. To overcome this problem, a real-time adaptive optical SI cancellation (RTA-OSIC) scheme using a SARSA(λ) reinforcement learning (RL) algorithm is proposed and experimentally demonstrated. The proposed RTA-OSIC scheme can automatically adjust the amplitude and phase of a reference signal by adjusting a variable optical attenuator (VOA) and a variable optical delay line (VODL) achieved through an adaptive feedback signal, which is generated by evaluating the quality of the received SOI. To verify the feasibility of the proposed scheme, a 5 GHz 16QAM OFDM IBFD transmission experiment is demonstrated. By using the proposed RTA-OSIC scheme, for an SOI at three different bandwidths of 200, 400, and 800 MHz, the signal can be adaptively and correctly recovered within 8 time periods (TPs), which is the required time of a single adaptive control step. The cancellation depth for the SOI with a bandwidth of 800 MHz is 20.18 dB. The short- and long-term stability of the proposed RTA-OSIC scheme is also evaluated. The experimental results indicate that the proposed approach could be a promising solution for real-time adaptive SI cancellation in future IBFD transmission systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".