Direct Subsampling Digital Wireless Receiver Based On Quantized Analog Front-End
Bibliographic record
Abstract
This thesis explores the applications of quantized analog (QA) signal processing for developing a highly reconfigurable software-defined radio (SDR) receiver. By applying subsampling to the QA front end along with a proposed high-speed digital front end, the SDR is capable of direct digitizing a 600 MHz signal centred at 2.1 GHz with a sampling rate of 2.5 GS/s and performing RF-to-baseband down-conversion, channel selection, baseband signal filtering and decimation. This thesis proposes two SDR-feasible solutions to address out-of-band blocker and noise folding problems associated with subsampling separately. Results show that the proposed SDR achieves an $\rm{NF_{sensitivity}}$ of 4.4 dB and an $\rm{NF_{blocking}}$ of 6 dB with a -2 dBm blocker. A high-speed power-efficient digital front-end with wide-band channel selection is also presented, which is fabricated in Global-Foundries 22 nm technology with a power consumption of only 7.1 mW and a layout size of 0.15 mm×1.335 mm.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".