Reconfigurable Digital Delta-Sigma Modulation Transmitter Architecture for Concurrent Multi-Band Transmission
Bibliographic record
Abstract
This paper presents a reconfigurable delta-sigma modulation (DSM) architecture for concurrent multi-band transmission. The reconfigurability in terms of carrier spacing and the number of simultaneous carrier transmission is useful for applications such as carrier aggregation in 5G. This paper uses 4thorder reconfigurable multi-band DSM (RMB-DSM) such that the zeros of the noise transfer function can be reconfigured to fall at multiple frequencies, where the carriers are being aggregated. The quantization noise between the transmission bands is a critical issue in the case of multi-band transmission. Therefore, a multi-band additional noise shaping (ANS) function is also introduced, which generates notches around each carrier and reduces the noise level between the multiple pass-bands. The proposed scheme has been validated in simulation, as well as in experiment for aggregating up to four 15 MHz long term evolution (LTE) signals with an overall aggregated bandwidth of 60 MHz. Measurement results show a 10-25% improvement in coding efficiency and 15-35 dB improvement in noise level near the operating frequency band using the proposed multi-band augmented noise shaping technique, as compared to the standard DSM. The corresponding improvement of 8% in the overall efficiency is observed by using the proposed multi-band augmented noise shaping technique.
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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 | 0.001 | 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".