Efficient linearisation technique for crosstalk and power amplifier non‐linearity suitable for massive MIMO transmitters
Bibliographic record
Abstract
Massive multi‐input–multi‐output (MIMO) is expected to be an eminent technique to meet the demands of high system capacity and data rates of wireless networks in 5G wireless communication. However due to inherent imperfections of the transmitter such as power‐amplifier (PA) non‐linearity and crosstalk, practically, the signal quality suffers and does not reap sufficient benefits from the various MIMO techniques. Digital predistortion (DPD) is a popular technique for single‐input–single‐output transmission to enhance signal quality. This study examines the issue of high DPD's complexity in mitigating imperfections in MIMO transmitters. This work proposes a less complex, novel DPD model for linearising large‐scale MIMO transmitters along with its characterisation procedure. The proof‐of‐concept is provided with the measurement setup containing 4 1 MIMO transmitters in the presence of non‐linear crosstalk, linear crosstalk, and strong PA non‐linearity. The proposed model performs comparably to the state‐of‐art DPD models like parallel Hammerstein and dual‐input crosstalk mismatch with lower number of floating‐point operations (flops). The proposed model improves adjacent channel power ratio up to dBc and error vector magnitude up to 1.08% for LTE signal of 40 MHz bandwidth.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".