Domain Selective Precoding in 3-D Massive MIMO Systems
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Bibliographic record
Abstract
Three-dimensional multiple input multiple output (3-D MIMO) with a large number of active antennas equipped in a uniformly rectangular antenna array has a significant potential in improving the system capacity. In this paper, we present an efficient two-dimensional (2-D) downlink precoding scheme for single-cell 3-D massive MIMO systems. Considering instantaneous channel state information at the base station, we show that either the elevation or azimuth domain can be used for interference cancellation. We divide the interference into two components, one of which is canceled in the elevation domain, while the other is canceled in the azimuth domain. Based on this domain selective (DS) strategy, two DS precoding algorithms are proposed for the single-path scenario based on the zero forcing and signal-to-leakage-plus-noise ratio criteria. Next, we extend our DS precoding scheme to a multi-path scenario. In the proposed algorithms, the precoding vectors of different users are determined in parallel, so as to reduce the computational complexity. Simulation results are provided to demonstrate that the proposed algorithms can achieve better spectral efficiency performance with a low computational complexity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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