Linear CE and 1-bit Quantized Precoding With Optimized Dithering
Bibliographic record
Abstract
High power amplifiers (HPA), used at transmission, add nonlinear impairments to the output signals. Through Constant envelope (CE) transmission, distortion in the signal can be avoided without wasting power on PA linearization. A more restricted form of CE transmission, 1-bit quantized transmission, further simplifies the RF chain and reduces the DAC power consumption. In this paper, for CE transmission and 1-bit quantized transmission at the BS antennas, we analyze downlink transmission for low complexity linear precoding. We observe that for small numbers of users in the downlink, correlation among the quantization error components across BS antennas is high, deteriorating the performance rapidly as number of users become smaller. To improve performance for smaller numbers of downlink users, we propose the addition of correlated Gaussian dither to the precoded signal before quantization and subsequent transmission. We observe that the receive SQINR peaks for finite non-trivial dither power. For given value of transmit power, number of BS antennas and number of users, SQINR is maximized analytically by the transmitter, to find the optimum dither power, using the Bussgang decomposition. We observe that with the implementation of optimized dithering, the error floor in the coded BER at high transmit power, for CE and 1-bit quantized transmissions, is pushed down significantly. We also observe that optimum dither power increases monotonically with transmit power, with rate of increase decreasing with increasing transmit power. Further, the optimum dither power strictly increases with number of BS antennas.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".