Novel Parallel-Processing-Based Hardware Implementation of Baseband Digital Predistorters for Linearizing Wideband 5G Transmitters
Bibliographic record
Abstract
In this article, a real-time digital predistortion (DPD) hardware architecture is presented for the linearization of fifth-generation (5G) transmitters with wideband modulation signals. To overcome the linearization bandwidth constraint imposed by the maximum clock frequency of the digital circuit, a new parallel-processing DPD engine architecture is devised to allow multiple samples to be processed per clock cycle. To minimize the complexity and power consumption of the transmitter-observation-receiver that typically scales with the linearization bandwidth, an undersampling scheme using a single low-speed ADC optimized for hardware implementation is devised. The proposed real-time DPD architecture is implemented in a commercial field-programmable gate array that achieves a scalable linearization bandwidth of up to 2.4 GHz with a 300-MHz core clock rate for the digital circuits. The linearization performance and bandwidth scalability of the proposed real-time DPD system were demonstrated experimentally using a silicon-based Doherty power amplifier with a 400-MHz wideband signal operating at 28 GHz and over-the-air measurements using a 64-element beamforming array with an 800-MHz wideband signal also at 28 GHz.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".