Geographic Segmented Localcasting Co-Channel Interference Mitigation Using Iterative Joint Detection and Decoding
Bibliographic record
Abstract
Geographic Segmented Localcasting (GSL) is an emerging Digital Terrestrial Television Broadcast (DTTB) physical layer operating mode. The system utilizes LDM-SFN to enable both wide-area Single Frequency Network (SFN) coverage and localized broadcast/multicast services within a single Radio Frequency (RF) broadcast channel. By combining SFN (core layer) and localcasting (enhanced layer) via Layered Division Multiplexing (LDM), GSL improves spectrum efficiency but faces challenges such as co-channel interference and SFN vs. Localcasting Channel Profile Mismatch (LCPM), which limit localcasting coverage. This paper presents two receiving methods that facilitate LDPC-coded LDM signal reception. Method 1 is the Multiple localcasting signals Iterative Joint Detection and Decoding (IJDD), which can mitigate severe co-channel interference with required Channel Status Information (CSI) of nearby localcasting transmitters. Method 2 is the Constellation Rotated IJDD (CR-IJDD), which can mitigate severe LCPM without CSI. The proposed methods enable decoding of both desired and interfering signals under high SNR conditions, enhancing spectrum reuse. Additionally, an Early Extrinsic Information Exchange for LDPC Iteration Reduction (EEIE-LIR) scheme is introduced to accelerate convergence and reduce receiver complexity. Evaluations based on ATSC 3.0 ModCods demonstrate that the proposed methods significantly improve spectrum efficiency and mitigate the co-channel interferences. The proposed technologies and can be extended to other DTTB systems and cell-based broadband wireless networks (e.g., the fifth generation (5G)/the sixth generation (6G), supporting seamless integration of broadcast, multicast, and unicast services.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".