Digital VLSI architectures for beam-enhanced RF aperture arrays
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Bibliographic record
Abstract
Beam-enhanced digital aperture arrays employ 2-D infinite-impulse-response (IIR) filters as a preprocessing stage for phased/timed-array beamformers to obtain lower side-lobe levels without compromising the array size or the main-lobe selectivity. A digital very-large-scale integration architecture is proposed for beam-enhanced linear aperture arrays. The proposed architecture consists of four subsystems: 2-D IIR prefiltering, beam steering via fast computation of filter coefficients, compensation for nonlinear phase, and phased/timed-array beamforming. Systolic-array architectures are used for first- and second-order 2-D IIR prefiltering subsystems, including fast computation of filter coefficients. The trade-off due to the nonlinear phase response of the 2-D IIR prefilter is partially compensated via fast Fourier transform-based complex phase rotations. Designs are implemented on a Xilinx Virtex-6 XC6VLX240T field-programmable gate-array device and verified using on-chip hardware cosimulation. Field-programmable gate-array designs for both 2-D IIR prefiltering and filter coefficient computation are mapped to standard-cell application-specific integrated circuits in 45 nm complementary metal-oxide semiconductor technology up to the synthesis level with supply VDC = 1.1 V. For a simulation having 64 antennas with binary phase-shift keying modulation, the beam-enhanced aperture array provides better than 10 dB improvement in bit error rate versus signal-to-interference ratio performance compared to phased/timed-array beamforming.
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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