FPGA Implementation and Performance Evaluation of a Digital Carrier Synchronizer Using Different Numerically Controlled Oscillators
Bibliographic record
Abstract
The emerging escalation in telecommunication systems has created a heightened impact on the digital integrated circuit (IC) design industry. In this day and age, Field programmable gate arrays (FPGAs) are often engaged to implement digital communication systems instead of ASICs due to the speed, performance, reliability and flexibility. Digital communication systems such as MODEM (modulation-demodulation) and M-PSK require the application of carrier synchronization in phase and frequency. Our work attains the successful FPGA implementation and emulation of a digital carrier synchronizer (DCS), which is a phase-locked loop (PLL), realized using digital circuits. The key role of this DCS depends largely on the numerically controlled oscillator (NCO). There are numerous configurations to realize NCOs that are employed in the architectural model of DCS. This paper evaluates the performance of three different NCOs realization in DCS for modem (modulator-demodulator) application using FPGA based design solutions. The configurations adopted are look up table (LUT), CORDIC and Xilinx ROM based NCO. There are advantages and trade-offs associated with each configuration. From our investigation it can be stated that the configuration of DCS involving Xilinx ROM based NCO 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 | 0.001 |
| 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 | 0.000 |
| 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".