Digital VLSI architectures for beam-enhanced RF aperture arrays
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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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".