An ultra‐low power and low jitter frequency synthesizer for 5G wireless communication and IoE applications
Bibliographic record
Abstract
Abstract This paper presents a fully integrated analog phase‐locked loop (PLL) fractional‐N frequency synthesizer for 5G wireless communication and Internet‐of‐Everything (IoE) applications. To demonstrate the effectiveness of this frequency synthesizer, we apply it to three wireless communication standards. Contrary to using Verilog or VHDL to implement the programmable frequency divider, we propose a new approach in the transistor level with a new divide‐by‐2/3 circuit, dynamic asynchronous resettable D and JK flip‐flops, and the OR & AND gates to customize the divider for low‐power, low‐jitter, and fast‐lock time applications. In addition, we have designed a new frequency phase detector (PFD) to overcome the dead region issue. An ultra‐low phase noise and low‐power voltage control oscillator (VCO) is exploited from our previous work with the flicker noise corner frequency around 10 kHz to achieve the lowest possible phase noise. The implementation is done in 180‐nm standard CMOS technology. It covers two frequency ranges including 2.4 to 2.48 GHz and 5 to 5.825 GHz for these wireless communication standards. According to simulations in the worst case, the lock time, rms‐jitter, in‐band fractional spur, power consumption, and jitter‐power figure‐of‐merit of the frequency synthesizer is 18 μs, 56 fs, −63 dBc, 4 mW, and −259, respectively.
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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 | 0.000 |
| 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 | 0.001 |
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".