Linearization of Fully-Connected Hybrid Beamforming Transmitters Using Analytical Multi-Input Models for Millimeter-Wave Communications
Bibliographic record
Abstract
In recent years, fully-connected hybrid beamforming (FC-HBF) architecture has aroused widespread interest for millimeter wave (mmWave) massive multi-input multi-output (MIMO) communication systems. However, the FC-HBF structure suffers from significant linearity deterioration, limiting its applications in actual mmWave transmitters. To resolve this issue, an effective digital predistortion (DPD) method utilizing analytical multi-input behavioral models is proposed in this paper for linearizing the FC-HBF system. Based on the nonlinearity analysis and behavioral modeling of the array response, three analytical multi-input models are derived by embedding the priori beamforming information in the predistorter. The complexity of the proposed analytical models is significantly reduced compared to the state-of-the-art. Numerical simulations and experimental measurement are carried out on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times 64$ </tex-math></inline-formula> mmWave FC-HBF array and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 16$ </tex-math></inline-formula> quasi-test platform respectively to validate the performance of the proposed DPDs against the state-of-the-art DPDs, which show significant linearization abilities to compensate for the nonlinear distortions of beam signals. The proof-of-the-concept validations in this paper indicate that the proposed scheme is fully capable of linearizing an mmWave FC-HBF array.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".