A Dynamic Array-of-Subarrays Architecture With Quantized Phase Shifters and DACs
Bibliographic record
Abstract
Existing design approaches for hybrid precoding with dynamic array of subarrays architecture (DAoSA) implicitly rely on the assumption of infinite-resolution phase shifters (PSs) and digital to analog converters (DACs). However, ideal PSs and DACs are impractical and deviation from this assumption may lead to significant performance degradation. In this paper, we investigate the design of a DAoSA hybrid precoder that employs low-resolution PSs and DACs with adjustable switch connections. The design aims to minimize the power consumption while achieving high spectral efficiency under quantization constraints. To solve this complex problem, we develop a joint optimization approach comprised of three interwined algorithms: element-by-element quantized PS (EBE-QPS), DAC bit allocation (DAC-BA) and switch network design (SND). Numerical simulations show that our proposed approach for DAoSA hybrid precoder design with finite-resolution PSs and DACs leads to reduced power consumption compared to existing benchmarks while maintaining the required spectral efficiency.
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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 | 0.000 | 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 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".