A 0.8 -3.4 GHz, Low-Power and Low-noise RF-to-BB-Current-Reuse Receiver Front-End for Wideband Local and Wide-area IoT Applications
Bibliographic record
Abstract
A low-noise, low-power and wideband RF-to-baseband (BB)-current-reuse quadrature (I/Q) receiver frontend using an active-inductor (AI) technique is proposed to support several local and wide-area wireless standards from 0.8 GHz to 3.4 GHz. To achieve low-power, a single supply and de bias current are shared between a low-noise transconductance amplifier (LNTA) and a transimpedance amplifier (TIA). A double-balanced current driven passive mixer with a 25% duty-cycle local oscillator (LO) to enhance the overall performance are used. To improve the gain and noise performance of the receiver, a high impedance AI is employed to isolate the RF signal path from the output node and BB signal that allows the receiver to operate at higher frequencies with minimum degradation. A gm-boosted capacitive cross-coupled common-gate (CCC-CG) LNTA topology is employed to enhance input-matching, gain and NF without consuming extra power. The proposed receiver is implemented in 130-nm TSMC CMOS process and occupies an active area of 0.025 mm2. In the default current setting, at an 4 MHz intermediate-frequency (IF) and a 2.4 GHz radiofrequency (RF), it achieves a voltage gain of 39.5 dB, a double-sideband (DSB) noise figure (NF) of 2.6 dB, S11of less than -15dB and an IIP3 of -28dBm while consuming 1.6mA from 1.2 V supply voltage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.003 | 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".