Analysis and Performance Evaluation of a Digital Carrier Synchronizer for Modem Applications
Bibliographic record
Abstract
Digital communication systems such as modulation-demodulation and M-PSK require the use of carrier synchronization in phase and frequency. This work addresses the implementation and analysis of a digital carrier synchronizer (DCS), which is a phase-locked loop (PLL), realized using digital circuits. This novel methodology highlights implementation promises towards some of the critical issues associated with the design of its analog counterpart, usually known as PLL. The principle function of this DCS is heavily dependent on the numerically controlled oscillator (NCO) and the loop filter (LF). There are various methods to implement NCOs and LFs that are used in the architectural model of DCS. This paper examines the performance of two different NCOs and LFs realization in DCS for modem (modulator-demodulator) application. The methods presented are look up table (LUT) and Xilinx ROM based NCO in one hand, and 1st order and 2nd order based LF. Each has its own merits and de-merits. The paper also developed a mathematical model of DCS for stability analysis. Furthermore, the authors analyzed the performance of this two implementations based on three performance metrics i.e. stability, locking-time and tracking range. From the analysis, Xilinx ROM based NCO with 2nd order LF performs better and are more suited for modem's DCS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".