Linear Transmit Precoding with Optimized Dithering
Bibliographic record
Abstract
Implementation of 1-bit Digital-to-analog converters (DACs) at base station (BS) antennas reduces power consumption and hardware complexity for a downlink massive MIMO system while introducing non-linear impairments in the transmitted signal. Through low complexity quantized linear precoding at the BS it is possible to mitigate detrimental effects of these impairments on the received signal. For a small number of users, however, quantization noise components across BS antennas become highly correlated, deteriorating per-user performance severely. In this paper, we improve performance and reduce correlation among quantization noise components by adding colored dither to the signal before DAC. Using the Bussgang decomposition up to the third order harmonics, we estimate the closed form asymptotic expression for per-user signal to quantization, interference and noise ratio (SQINR) of the received signal as a function of the dither power. We evaluate optimum dither power by maximizing SQINR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".