Low-power integrated CMOS RF transceiver circuits for short-range applications
Bibliographic record
Abstract
This paper discusses our efforts in designing different low-power RF transceiver blocks, starting with the LNA and power amplifier (PA). The paper discusses the effect of four different input matching methodologies on the gain of narrow-band LNAs. Measurement results of two LNAs fabricated in a 0.18 μm CMOS technology are also presented. Two ultra-wideband (UWB) LNA designs that aim for low-voltage and low-power operation are also discussed in this paper. The UWB LNAs consume a power of 5.8 mW from a 0.8 V supply voltage, while achieving a maximum gain of 12.5 dB and an input matching better than −10 dB from 2–10 GHz with a NF of 3.5 dB. A fully integrated, 2.4 GHz class-E PA, with a class-F driver stage is also discussed in this work, demonstrating the feasibility of using CMOS class-E PAs for low-transmit power applications. The circuit was fabricated in a standard 0.18 μm CMOS technology with a maximum drain efficiency of 53 %. When operating from a 1.2 V supply, the PA delivers an output power of 14.5 mW with a power-added efficiency (PAE) of 51 %. The supply voltage can go down to 0.6 V with an output power of 3.5 mW and a PAE of 43 %. Finally, the paper also discusses a simple transmitter and receiver front-end, in addition to a single-block simplified, lowpower PLL transmitter design.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".