Distributed Spatiotemporal Neural Network for Nonlinear Dynamic Transmitter Modeling and Adaptive Digital Predistortion
Bibliographic record
Abstract
This paper presents an adaptive neural network (NN) approach for the behavioral modeling of wireless transmitters exhibiting dynamic nonlinearities that are mainly caused by the power amplifier (PA). The proposed distributed spatiotemporal NN mimics the functionality of the mammal cerebellum, which is capable of very fast learning and contains features of interpolation. PAs' memory effects are modeled by using linear affine projection on a local function generated by preceding signal inputs. The applicability of the proposed model is validated in the frequency and time domains for forward and reverse modeling using a highly nonlinear Doherty amplifier and a class AB PA driven by wideband code division multiple access and WiMAX signals. The modeling performance is compared with existing techniques to establish it as a successful model that requires a relatively less demanding processing speed and memory requirement during the identification procedure. This model was found to be effective for adaptive applications such as baseband predistortion-based linearization of wireless transmitters.
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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) | 0.001 | 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".