Complex Delta–Sigma-Based Transmitter With Enhanced Linearity Performance Using Pulsed Load Modulation Power Amplifier
Bibliographic record
Abstract
This paper proposes a linear and efficient transmitter prototype based on pulsed load modulation (PLM) power amplifier (PA). The proposed transmitter setup utilizes the complex delta-sigma (DS) modulation as a signal processing technique instead of the envelope DS modulation for higher linearity performance. Using the complex DS modulation technique reduces the in-band quantization noise significantly at the output of the modulator and consequently, enhances the linearity of the transmitter. To validate the proposed technique, the linearity and efficiency performance of the complex DS modulator (CDSM)-based transmitter are compared with the performance of its envelope DS modulator (EDSM) counterpart in measurement. For this paper, an efficient and linear PLM PA is designed and fabricated using GaAs E-pHEMT transistors. For a Long-Term Evolution (LTE) uplink standard signal with 3-MHz bandwidth and 7-dB peak-to-average power ratio, the CDSM-based transmitter achieves the drain efficiency and power added efficiency of 46% and 42%, respectively, at an average output power of 25.1 dBm. The comparison measurement study of EDSM-based transmitter and the CDSM-based transmitter with the LTE uplink signal shows about 11-dB improvement in the signal-to-noise and distortion ratio of the output signal. The measurement results for LTE signals were able to pass the spectral requirements defined by the standard without applying predistortion techniques.
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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 | 0.001 |
| 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".