An RF Self-Interference Cancellation method for In-Band Distribution Link in ATSC 3.0
Bibliographic record
Abstract
Wireless in-band backhaul technology has recently been proposed by the Advanced Television Systems Committee (ATSC) as a key enabling technology for next-generation digital broadcasting systems. It consists of in-band distribution links (IDL) and inter-tower communication networks (ITCN), both of which can operate in spectrum-efficient in-band full duplex (IBFD) mode. This is because the same frequency band is shared by IDL and traditional broadcast services. Thus, IDL is desired to achieve very high data rates, so that more bandwidth can be reserved for broadcast services. This places high demands on the self-interference cancellation (SIC) of IDL receivers. Self-interference (SI) consists mainly of leakage from co-located transmitters, and this SI signal is made more difficult to cancel effectively by the non-linear distortion of high power amplifier (HPA). Existing methods for SIC mainly include training-based SIC and blind SIC. Training-based SIC can achieve excellent performance, but the training step adds a significant amount of complexity. Blind SIC is able to achieve higher bandwidth efficiency by omitting the training phase. In this paper, we mainly consider using the process of gradually increasing the power of the HPA of the co-located transmitter to improve the elimination effect of the HPA nonlinear distortion in saturation region by more accurately estimating the channel state information (CSI) through the nonlinear distortion phase of the non-saturation region of the HPA that is not severe. Also, if it is found that the co-located transmitter is sending a large number of error messages that cannot be resolved, the HPA can be switched off and the transmitter power-on conditioning process can be repeated. This can improve the robustness of the single frequency network (SFN) system. This can improve the robustness of the single frequency network. Index Terms-In-band full duplex, self-interference cancellation, high power amplifier.
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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 | 0.000 |
| 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) | 0.002 | 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".