Design, Implementation and Performance Analysis of RF Power Amplifier for 5G Mobile Communication in the Sub-6 GHz Band Using Advanced Node 18nm FinFET Technology
Bibliographic record
Abstract
When it comes to the radio frequency (RF) frontend, power amplifier (PA) is one of the most important functional blocks for dependable wireless transmission.In order to provide the necessary output power, PAs boost and amplify the incoming signal, to ensure that the transmitter's signal reaches the receiver at the necessary distance.The PAs have not yet managed to find place within the transceiver circuit due to its bulky nature.Although rigorous efforts have been made to improve the linearity and efficiency of the PAs, it has come at the cost of increase in chip area.This paper focusses on design of an RFPA in an 18nm FinFET advanced node technology that is adaptable to the Sub-6 GHz frequency band of 5G communication standard so as to provide maximum output power at the operating frequency of 3.5GHz.The single stage PA, thus designed and simulated on Cadence Virtuoso provides a gain of 27.71 dB at a supply voltage of 1V.The bandwidth is 208 MHz, power gain is 25dB and the output power is 6.335 dBm.The simple design with a single transistor paves way for a considerable decrease in the chip area, thus making it possible to be placed within the transceiver chip.
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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 | 0.001 | 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".