LDPC-Coded LDM Systems Employing Non-Uniform Injection Level for Combining Broadcast and Multicast/Unicast Services
Bibliographic record
Abstract
Layered Division Multiplexing (LDM) is a Power-based Non-Orthogonal Multiplexing (P-NOM) technique that has been implemented in the Advanced Television System Committee (ATSC) 3.0 terrestrial TV physical layer to effectively multiplex services with different robustness and data rate requirements. As communication systems quickly evolve, the services to be delivered are becoming more diverse and versatile. Up to now, the LDM system adopted in the terrestrial TV system uses a uniform injection level for the lower-level (or Layer 2) signal injection. This paper investigates the non-uniform injection level LDM (NULDM). The proposed technique can explore the Unequal Error Protection (UEP) property of Low-Density Parity-Check (LDPC) codes and the flexible power allocation nature of the NULDM to improve the system performance and spectrum efficiency. NULDM enables the seamless integration of broadcast/multicast and unicast services in one RF channel, where the unicast signal can assign different resources (power, frequency, and time) based on the UE distance and service requirements. Meanwhile, more power could be allocated to improve the upper layer (or Layer 1) broadcast and datacast services. To make better use of the UEP property of LDPC codes in NULDM, the extended Gaussian mixture approximation (EGMA) method is used to design bit interleaving patterns. Additionally, inspired by the channel order of polar codes, this paper proposes an LDPC sub-block interleaving order (SBIO) scheme that performs similarly to the EGMA interleaving model, while better adapting to the diverse needs of proposed mixed service delivery scenarios for convergence of broadband wireless communications and broadcasting systems.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".