Robust Hybrid Analog/Digital Beamforming for Uplink Massive-MIMO with Imperfect CSI
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".