In-Band Full-Duplex Communications in ATSC 3.0 Single Frequency Network
Bibliographic record
Abstract
Wireless in-band backhaul, a.k.a. in-band distribution links (IDL), is a spectrum-efficient and cost-effective enabling technology to the realization of Advanced Television Systems Committee (ATSC) 3.0 in the single frequency network (SFN) mode, where all the transmitter towers are synchronized to transmit the same broadcast signal in the same frequency band. Inter-tower communications network (ITCN) transforms the broadcast towers into a mesh network, thereby introducing datacasting capability to the traditional broadcast network. Both the ITCN and IDL can be operated in the most spectrum-efficient in-band full-duplex (IBFD) mode. In these situations, the ITCN/IDL receivers at the SFN towers receive the signal of interest (SOI) not only severely corrupted by the self-interference signal from its co-located transmitter, but also the co-channel interference signals from neighbouring transmitters. Moreover, the ITCN/IDL signals may be combined with the broadcast signal in the Layer Division Multiplexing (LDM) format to achieve better overall spectral efficiency. Therefore, the LDM inter-layer interference must also be mitigated. In this paper, the interferences for the ITCN/IDL signal in the SFN environment are analyzed, and a novel iterative successive signal cancellation scheme is proposed to effectively mitigate the interferences in the ITCN/IDL signal detection process in ATSC 3.0 SFNs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".