Optimal Quantized Multi-Cell MMSE Precoding With Low Resolution Data Converters
Bibliographic record
Abstract
This work considers a multi-cell multi-user multiple-input multiple-output (MIMO) system that employs low resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at each base station (BS) to limit the power consumption. Existing precoder designs for quantization-free systems are sub-optimal for such quantized systems, while the existing precoder designs for quantized systems consider single-cell settings and perfect channel state information (CSI). To address these gaps, we study the downlink linear precoder optimization problem in a cellular system under the distortions introduced by low resolution DACs based on a minimum mean square error (MMSE) approach, while accounting for imperfect CSI obtained in the uplink under distortions introduced by low resolution ADCs. The problem is analytically solved resulting in an optimal quantized multi-cell MMSE precoder that reduces both intra-cell and inter-cell interference under quantization errors, and yields better bit error rate performance than applying the existing conventional multi-cell and quantized single-cell linear precoders to a quantized multi-cell massive MIMO system.
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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 | 0.001 |
| Open science | 0.003 | 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 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".