A Partially Dynamic Subarrays Structure for Wideband mmWave MIMO Systems
Bibliographic record
Abstract
Hybrid architecture is a promising candidate precoding scheme to balance the achievable spectral efficiency and power consumption in millimeter wave multiple input multiple output systems. A practical partially dynamic subarray-connected architecture is developed to improve the transmission performance. In this proposed architecture, the set of antennas in each subarray is fixed, but the subarrays connected to each radio frequency chain are dynamic. Moreover, we study how to optimize jointly the partially dynamic subarray structure and the hybrid precoders under the constraints of total transmit power and hardware limitation. This joint optimization problem is divided into two sub-problems. For the first sub-problem, a low-complexity algorithm is proposed to determine the partition of subarrays using the long-term spatial channel covariance. Then, the penalty decomposition method is adopted to design the hybrid precoders. Numerical results verify that the partially dynamic subarray design algorithm offers one or two orders of computation time saving compared with the existing algorithms, and the hybrid precoding algorithm outperforms the existing algorithms in terms of spectral efficiency. Moreover, compared with the fully dynamic subarray structure adopted in the existing algorithms, the proposed structure achieves spectral efficiency gain and energy efficiency gain using less hardware.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".