Constrained Channel Decomposition-Based Hybrid Beamforming for mmWave Massive MIMO Systems
Bibliographic record
Abstract
In this article, we propose a hybrid beamforming design for multiuser mmWave massive MIMO systems. We adopt a two-stage approach for designing the analog and digital beamforming separately. The analog beamforming design is based on a constrained low-rank channel decomposition and aims to simultaneously harvest the array gain and reduce the intra- and inter-user interference. The digital beamforming design is conducted by using the regularized channel diagonalization method, which provides a better trade-off between multiuser interference suppression and transmit diversity, thus attaining a better performance in low-SNR scenarios or when communicating to many users or through many data streams. We validate the effectiveness of the proposed design through numerical simulations, which have shown that our design outperforms several other hybrid beamforming designs in the literature.
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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 | 0.002 | 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".