Adaptive linearization of transmitter in the presence of I/Q Imbalance using distributed spatio-temporal neural network
Bibliographic record
Abstract
For distortion-free data transmission, digital predistortion (DPD) is now a widely accepted method to linearize the power amplifier (PA) in a transmitter. DPD requires inverse modeling of PA and processing input through inverse model before feeding it to PA. However, when modulator in transmitter also have I/Q imbalance and local oscillator (LO) leakage, they cause extra intermodulation distortion (IMD) to appear at the PA output and conventional models such as Volterra series, memory polynomial, Weiner-Hammerstein and look up table methods are not able to model the inverse modeling properly and even worsen the spectral regrowth. This paper focuses on an adaptive distributed spatiotemporal neural network (DSTNN) for adaptive predistortion which has been shown to be robust to all the linear imperfections (i.e., gain/phase errors) as well as nonlinearity (i.e., IMD) to finally mitigate all the imperfections in the transmitter system in one step, due to its unique nonlinear mapping which does not depend on quadrature relation between I and Q components. DSTNN provides one-step solution for online adaptive application, which low cost in terms of floating point operations and does not require complex matrix operations such as matrix inversion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".