Improve the Performance of LDPC Coded QAM by Selective Bit Mapping in Terrestrial Broadcasting System
Bibliographic record
Abstract
In this paper, we employ selective bit mapping to improve the performance of the LDPC coded QAM scheme for terrestrial DTV broadcasting system. The threshold of message-passing decoding can be considerably lowered by selectively mapping the binary components of LDPC codeword to the positions in the m-tuples to be mapped into$2^{m}{\rm QAM}$symbols. In our approach, the mapping pattern is described by bit-mapping polynomials, based on which density evolution can be applied. The optimization algorithm is developed with two implementation concerns, using the Chinese DTMB standard as an example. Numerical results illustrate that our proposed approach can improve the decoding threshold by 0.05 dB to 0.499 dB depending on the code rate and the order of QAM modulation. Simulation results show that the actual BER improvement varies from 0.09 dB to 0.6 dB with different code-modulation combinations in both single-carrier and OFDM modes.
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) | 0.000 | 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 | 0.001 |
| 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".