A Novel Probabilistic Shaping Scheme for 16QAM Modulated Polar Codes
Bibliographic record
Abstract
A new scheme is proposed for polar codes combined with 16 Quadrature Amplitude Modulation (QAM) modulation to obtain the Probabilistic Shaping (PS) gain. In this paper, a low-complexity Constellation Compression Modulation (CCM) system of polar codes is constructed and the corresponding expansion factor equation is derived by combining the characteristics of the proposed system. In the CCM-12QAM-QPSK scheme, label bits are used to solve the many-to-one (MTO) mapping problem in the CCM, and assist with the demodulation. The polarization channels and the MTO mapping relationship are used to design the corresponding interleaver. Due to the different occurrence probabilities of label bits constellation points, a CCM-12QAM-QPSK-N scheme based on the novel diamond Quadrature Phase Shift Keying (QPSK) constellation diagram is proposed to further obtain the shaping gain. Simulation shows that when the expansion factor is 1.2 and the Bit Error Rate (BER) is 1 × 10−4under the additive white Gaussian noise (AWGN) channels, the proposed scheme can obtain about 0.6 and 0.7 dB gain respectively in the superiority of the 16QAM scheme in the same code and information bits length; in addition, the CCM-12QAM-QPSK scheme is close to the performance of 16QAM scheme while getting more information bits, it means the receiver can know more useful information. The CCM-12QAM-QPSK-N scheme further gains at least 0.2 dB while receiving more information bits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".