Comparison of carrier recovery techniques in M-QAM digital communication systems
Bibliographic record
Abstract
In digital wireless systems the frequency uncertainty between the transmitter and the receiver can take large values. The carrier recovery loop must be able to acquire lock in the presence of phase noise on the synthesized carrier. However, high level modulation formats such as 16, 32 and 64 QAM require very small steady state phase jitter. Unfortunately, these requirements are often incompatible in carrier recovery circuits. Practically, acquisition aiding techniques such as frequency sweeping or frequency detectors are the main solution to this problem. This paper addresses decision feedback carrier recovery loops for coherent demodulation of the 16 QAM signal format. Two types of decision-directed phase detector referred as basic or original phase detector (PD) and phase-frequency detector (PFD) are used in order to extend the acquisition range of the loop. Extensive and realistic simulation tests are performed to investigate the acquisition behavior of both phase detectors in the presence of phase noise. The best performance is obtained with the PFD structure. Compared with the original phase detector, the PFD structure leads to a 15-fold increase of the loop acquisition range.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".