Robust Hybrid Analog/Digital Beamforming for Uplink Massive-MIMO with Imperfect CSI
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Bibliographic record
Abstract
In this paper, we study the design of hybrid analog/digital beamformers for uplink connection in massive multiple-input multiple-output (MIMO) systems under imperfect channel state information (CSI). The norm-bounded channel error model is used to capture characteristics of imperfect CSI in practical systems. The objective function is formulated based on the minimum mean squared error (MMSE) worst-case robustness. We consider both single user (SU) and multiuser (MU) reception modes of a millimeter-Wave (mmWave) massive-MIMO base station (BS). For the SU scenario, we study hierarchical beamformer optimization as well as joint precoder/combiner optimization for users with limited and extended computational capabilities, respectively. These optimization techniques are subsequently extended to the MU case where a new hybrid robust combiner design is proposed. Simulation results are presented confirming the superiority of our designs when compared to recent robust hybrid designs in the literature.
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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 it