Discrete Implementation and Linearization of a New Polar Modulator-Based Mixerless Wireless Transmitter Suitable for High Reconfigurability
Bibliographic record
Abstract
A novel polar modulator-based transmitter architecture that uses an analog RF variable gain amplifier (VGA) and an analog phase shifter is proposed and implemented using discrete elements. This architecture translates the baseband phase signal to RF without using mixers and quadrature up-converter circuits. Accordingly, spurs and distortions that are typically associated with the mixers are absent over a wide frequency band. As such, no filtering is needed at the output of the proposed transmitter. The absence of RF band pass filter provides wider RF bandwidth which would make the transmitter design reconfigurable and more suitable for integration. The proposed transmitter architecture has unusual signal distortion effects due to amplitude and phase nonlinearity in the VGA and the phase shifter. Indeed, this distortion consists not only in amplitude-to-amplitude (AM/AM) and amplitude-to-phase (AM/PM) distortions, but also includes phase-to-amplitude (PM/AM) and phase-to-phase (PM/PM) distortion. Hence, a new behavioral model using augmented complex memory polynomial is proposed to mitigate these complex nonlinear effects. The performance of the linearized transmitter is tested using long-term evolution (LTE) signals and the signal quality is assessed in terms of error vector magnitude (EVM). The measured EVM of the LTE signal of 1.4 MHz bandwidth at the output of the transmitter improved from 9.82% to 0.43% using digital predistortion technique. The measured adjacent channel leakage-power ratio (ACLR) of the proposed transmitter is 59 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".