Noise Shaping for Phased Array Beamforming
Why this work is in the frame
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Bibliographic record
Abstract
Quantization of phase and/or amplitude has far-reaching effects on the radiation characteristics of the phased array (PA), including gain, minor lobe level, and point deviation. Traditionally, one common method to address such a nonlinear distortion is using random-phasing to interrupt the error periodicity. Here, we show that the distortion due to the quantization can be better remediated by spectrally shaping the error compared to the random-phasing (dithering) approaches. We adapted the method for phase-only and amplitude-phase synthesis of planar array designed based on analog beamforming (ABF). To do that, for the first time, 2-D real- and complex-coefficient minimum-phase digital finite impulse response (FIR) filters are designed based on the discrete Hilbert transform (DHT) method. In particular, the digital filter design for phase-only synthesis is comprehensively investigated, respecting the error spectra in the beamspace domain. It is shown that by pushing the error out of the so-called visible region, the decrease of antenna directivity due to the quantization can be compensated to some extent, which provides a quite advantage over the uniform distribution of error. For some cases, pushing the error out of the visible region might be impossible. For such cases, we proposed using the spaced-notches filter. It is also shown that the method is on maximum efficacy when both phase and amplitude of the excitation signal are controllable. Thus, complex-valued noise shaping (CV-NS) can be exploited for the phase-amplitude synthesis of the PA, showing quite promising performance.
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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