Energy Efficient Hybrid Precoding for Adaptive Partially-Connected mmWave Massive MIMO: A Decomposition-Based Approach
Bibliographic record
Abstract
For the prominent superiority in supporting high-data-rate applications with reduced hardware complexity and energy consumption, hybrid precoding in millimeter-wave (mmWave) massive multi-input multi-output (MIMO) system has attracted considerable attentions recently. To adequately utilize the available antenna resources and reduce power consumption, we investigate energy efficient hybrid precoding for an adaptive partially-connected structure in mmWave massive MIMO system with antenna group overlapping to achieve array gain. Specifically, the energy efficient hybrid precoding is firstly formulated as an energy efficiency (EE) maximizing problem, where the number of active phase shifters, the connection relationship between radio frequency chains and antennas, and precoding matrix are jointly optimized. Then, utilizing the diagonal characteristic of digital precoding in a partially-connected structure, we propose a decomposition-based low-complexity approach to tackle the large-scale mix-integer non-convex EE optimization problem in the fully adaptive hybrid precoding scheme. Besides, to provide further insights on how the EE-oriented design affects the system performance, a partially adaptive scheme is also introduced. Finally, simulation results verify the viability and effectiveness of the proposed schemes.
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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 | 0.001 | 0.001 |
| 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 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".