Geographic Segmented Localcasting Co-Channel Interference Mitigation Using Iterative Joint Detection and Decoding
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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