Multicarrier-Based QAPM Modulation System for the Low Power Consumption and High Data Rates
Bibliographic record
Abstract
Low power consumption and high data rate are the most important requirements for the communication system. Especially, very low power consumption modulation method is required for the short range communication systems such as the medical implantable communication devices or capsule endoscope, and so on. For the higher data rate, we like to combine the OFDM system into the QAPM since the OFDM system has higher bandwidth efficiency than a single-carrier system. In this paper, we like to propose a QAPM (Quadrature amplitude position modulation) method combined with the orthogonal frequency-division multiplexing (OFDM) system. Next, we analyze the performance of three low-power-consumption modulation schemes: the phase shift position modulation (PSPM), phase silence shift keying (PSSK), and QAPM using orthogonal frequency-division multiplexing (OFDM) system in the multi-path channel. These schemes have lower bandwidth efficiency and the higher power efficiency than the existing phase-shift keying (PSK) and quadrature amplitude modulation (QAM) schemes. It can be shown that they can achieve greater power efficiency because every modulation symbol has a zero-envelope period as in pulse-position modulation (PPM) techniques. Finally, we compare the performances of the PSPM, PSSK, and QAPM modulation combined with the OFDM system with regard to bit error rate performance and throughput.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".