Linearized Multi-Level $\Delta\Sigma$ Modulated Wireless Transmitters for SDR Applications Using Simple DLGA Algorithm
Bibliographic record
Abstract
This paper proposes a new linearization algorithm, discrete level gain adjustment (DLGA), for linearized high efficiency multi-level delta sigma modulator (ΔΣM)-based transmitter architectures adequate for wideband multi-standard software defined radio (SDR) applications. The new simple linearization DLGA algorithm is deployed instead of using a full digitally predistorted to maintain the linearity of the employed switching-mode power amplifier (SMPA) with a considerable decrease in the complexity of the digital signal processing (DSP) unit. The proposed architecture includes a multi-level envelope ΔΣM (EΔΣM) concurrently with a linearized SMPA, in order to achieve a better trade-off of power efficiency versus linearity. Based on DLGA, instead of envelope elimination and restoration (EER) configuration, three-level envelope LPΔΣM-based transmitter in phase elimination and restoration (PER) configuration was implemented. The bandwidth constraint of the EER configuration was relaxed. First, a multi-level Envelope EΔΣM-based transmitter was studied to determine the optimal number of quantizer levels that could be used. Through MATLAB simulation and measurement results, it was shown that the best performance was achieved with a discrete level signal that has three different power levels, including zero and regardless the phase. From the measurements, the linearized three-level PER-LPEΔΣM transmitter shows an efficiency of 36%, signal-to-noise distortion ratio of 43.8 dB and adjacent channel power ratio of 45 dB.
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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 | 0.001 |
| 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".