Real-time adaptive optical self-interference cancellation for in-band full-duplex transmission using SARSA(λ) reinforcement learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it