Multi-user Detection with Oversampled Large Antenna Arrays and Low-resolution ADCs
Why this work is in the frame
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Bibliographic record
Abstract
This paper delves into the uplink scenario of mmWave massive MIMO systems with dense ULAs and low-resolution ADCs. We focus on reducing power consumption and simplifying hardware while enhancing quantized system performance through spatial oversampling. We address the emergence of spatial thermal noise correlations and hardware imperfections, which profoundly affect signal recovery. To combat these challenges, we propose a non-linear inference method based on Vector Approximate Message Passing (VAMP) and Belief Propagation. Our algorithm aims to reconstruct transmitted signals from quantized measurements obtained by coupled antennas. We demonstrate that employing oversampling techniques significantly improves system performance, even when oversampled, highlighting spatial oversampling as an effective strategy for enhancing low-resolution ADC performance. Additionally, we analyze the impact of noise figure on recovery, underscoring its importance in system design.
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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 | 0.000 | 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 it