Parallel-Processing-Based Digital Predistortion Architecture and FPGA Implementation for Wide-band 5G Transmitters
Bibliographic record
Abstract
This paper presents a bandwidth-scalable and hardware-efficient parallel-processing-based D PD architecture for wide-band 5G transmitters. By computing multiple data samples at each clock cycle in parallel, the proposed DPD architecture extends the bandwidth of a conventional serial DPD architecture, as limited by the maximum FPGA clock rate, to a much higher rate that is proportional to the number of parallel data paths. With a cross-bar structure devised to reroute the intermediate computation results between the parallel data paths, it allows advanced DPD model with memory and cross-terms to be constructed efficiently. F or proof-of-concept, the pruned Complexity-Reduced-Volterra (CRV) DPD with four parallel data paths has been implemented using an Xilinx Ultrascale+ FPGA to achieve a total linearization bandwidth of 1.25 GHz. Subsequently, a 28 GHz power amplifier modulated with 400 MHz QAM64 signals has been successfully linearized in the proposed DPD system in real-time.
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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) | 0.003 | 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".