Cancelling Adjacent Channel Interference for In-Band Full-Duplex Communications
Bibliographic record
Abstract
When operating in the in-band full-duplex (IBFD) mode, wireless in-band distribution link (IDL) is a spectral efficient and cost-effective backhaul technology for Advanced Television Systems Committee (ATSC) 3.0 in a single-frequency network (SFN), since IDL shares the same frequency band allocated for the traditional broadcast services. Therefore, it is desirable to use high-order modulation, e.g., 1024QAM, to achieve a very high data rate for the IDL, such that more bandwidth can be reserved for broadcast service. This presents stringent requirements for the self-interference (SI) cancellation (SIC) at the IDL receiver where the leakage from the co-located transmitter as self-interference seriously corrupts the received signal. Such SI signal is nonlinearly distorted by the high power amplifier (HPA). Moreover, the transmitters broadcast multiple channels at the same time. Therefore, the SI signal also contains leakage from adjacent channels, which is also nonlinear in nature. Accurately cancelling the SI signal with HPA-induced nonlinear distortion and adjacent channel interference (ACI) is considered in this paper. Simulation results demonstrate that under the presence of nonlinear distortion and ACI, linear SIC fails to achieve satisfactory performance, while the previously proposed iterative successive nonlinear SI cancellation (ISNSIC) can effectively cancel the SI with both nonlinear distortion and ACI.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".