Highly Linear and Reconfigurable Three-Way Amplitude Modulation-Based Mixerless Wireless Transmitter
Bibliographic record
Abstract
A novel transmitter architecture that uses only envelope modulators is proposed. The complex baseband signal is decomposed into components using a three-coordinate decomposition technique. The individual components are translated to RF using three variable gain amplifiers (VGAs), which act as envelope modulators. The outputs of the VGAs are combined to generate the complex modulated RF signal. The proposed architecture does not have any phase modulator circuit and avoids phase noise and bandwidth expansion issues associated with polar topologies. This architecture avoids the use of any mixers or quadrature up-converters for frequency up-conversion. Accordingly, spurs that are associated with mixer circuits are avoided and, hence, no filtering is needed at the RF output of the transmitter. As RF filters are absent, this architecture offers wider RF bandwidth and would improve the design reconfigurability and integration capability. The signal at the output of the amplitude modulation-based transmitter has distortion due to gain and phase nonlinearity in the VGAs. A new memory polynomial-based modeling approach is used to linearize the transmitter. The performance of the implemented transmitter is evaluated using Long Term Evolution (LTE) signals and the measured error vector magnitude (EVM) and the adjacent channel leakage-power ratio (ACLR) are used to assess the signal quality. The measured EVM of the LTE signal of 1.4 MHz bandwidth, improved from 15% to 0.5% using digital predistortion technique, while the obtained ACLR is 54 dBc.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".