4.5 A Reconfigurable, Multi-Channel Quantized-Analog Transmitter with <-35dB EVM and <-51dBc ACLR in 22nm FDSOI
Bibliographic record
Abstract
The presence of multiple operative modes and standards in 5G/6G systems set two crucial requirements for the next generations of wireless transmitter (TX): flexibility and high spectral purity. While the former can be obtained by more digital systems such as a radio-frequency digital-to-analog converter (RF-DAC) TX [1, 2], mixer-based solutions still outperform RF-DACs in terms of spectral purity for a given power dissipation [3, 4]. The reason is that RF-DACs need a very large number of bits (up to 18bits) to lower the quantization noise, and a very high sampling rate to meet the spectral purity required by 5G/6G TXs [2]. This work aims to find a compromise between RF-DAC and analog-based TXs by using the Quantized Analog (QA) signal processing presented by Musayev et al. [5]. The signal is sliced in amplitude to feed an array of analog TXs, and recombined after the upconversion (Fig. 4.5.1). While keeping similar spectral purity as analog TXs, this approach has a higher level of flexibility by enabling multi-carrier operation, power scalability and an agile reconfiguration of the class of operations. Moreover, it will be shown how the QA approach is a very competitive solution in terms of area and power dissipation compared to RF-DAC-based solutions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".