A Novel High-Pass Delta–Sigma Modulator-Based Digital-IF Transmitter With Enhanced Performance for SDR Applications
Bibliographic record
Abstract
This brief presents a new transmitter architecture based on digital-intermediate frequency (IF) high-pass delta-sigma modulator (HPDSM). The proposed digital-IF transmitter topology utilizes a novel complex HPDSM topology to address the high quantization-noise power problem in Cartesian band-pass BPDSM and HPDSM-based digital-IF transmitters, without increasing the oversampling ratio of the signal or the clock rate of the system. To evaluate the performance of the new digital-IF transmitter system, a comparison with its Cartesian band-pass and high-pass counterparts, in terms of signal to noise and distortion radio (SNDR) and coding efficiency, was established. A simulation using 8 dB peak to average power ratio long term evolution signals with 1.24-MHz bandwidth showed that by integrating the proposed second-order complex HPDSM digital-IF in the transmitter, the power of the quantization noise is significantly reduced. The transmitter digital blocks were implemented on the BEEcube software-defined radio prototyping platform. The input signal is encoded by the new HPDSM topology and is up-converted and then fed to the inverse class-F switch-mode power amplifier. An overall efficiency of 12% was achieved by the proposed topology. Moreover, the output signal SNDR reached 37.8 dB and the adjacent channel leakage ratio at the lower and upper 1.25-MHz offset frequencies measured is equal to -35 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) | 0.002 | 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".