A Frequency-Agile RF Frontend Architecture for Multi-Band TDD Applications
Bibliographic record
Abstract
Emerging wireless standards specify dozens of bands spanning several octaves, which need to be supported in form-factor and energy constrained mobile devices targeting ubiquitous connectivity. However, in current multi-band radio implementations, significant redundancy is still the norm in the RF frontend. This work introduces an improved architecture for multi-band, time-division duplexed (TDD) radios, which replaces multiple narrowband frontend components with a frequency-agile solution, tunable over a wide frequency range. A highly digital architecture is adopted, leading to a fully integrated solution wherein both efficiency and achievable frequency range benefit from CMOS scaling. A prototype is integrated in 45 nm SOI CMOS. Peak PA output power is 27.7 ±0.5 dBm from 1.3 to 3.3 GHz, with up to 30% total efficiency at 2 V. For TDD LTE applications, better than -30 dBc ACLR and -30 dB EVM is measured with 64 QAM, 20 MHz signals from 1.44 to 3.41 GHz, with up to 17.2% average efficiency and 23.4 dBm average power. The LNA achieves AV ≥ 14 dB, NF = 4.4 ±1.6 dB and IIP 3 ≥ -7 dBm from 1.3 to 3.3 GHz while drawing just 6 mA from 1 V. The demonstrated frequency range covers a total of 11 TDD bands .
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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 | 0.000 | 0.001 |
| 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".