Digital Architectures for UWB Beamforming Using 2D IIR Spatio-Temporal Frequency-Planar Filters
Bibliographic record
Abstract
A design method and an FPGA-based prototype implementation of massively parallel systolic-array VLSI architectures for 2nd-order and 3rd-order frequency-planar beam plane-wave filters are proposed. Frequency-planar beamforming enables highly-directional UWB RF beams at low computational complexity compared to digital phased-array feed techniques. The array factors of the proposed realizations are simulated and both high-directional selectivity and UWB performance are demonstrated. The proposed architectures operate using 2's complement finite precision digital arithmetic. The real-time throughput is maximized using look-ahead optimization applied locally to each processor in the proposed massively-parallel realization of the filter. From sensitivity theory, it is shown that 15 and 19-bit precision for filter coefficients results in better than 3% error for 2nd- and 3rd-order beam filters. Folding together with Ktimes multiplexing is applied to the proposed beam architectures such that throughput can be traded forK-fold lower complexity for realizing the 2-D fan filter banks. Prototype FPGA circuit implementations of these filters are proposed using a Virtex 6 xc6vsx475t-2ff1759 device. The FPGA-prototyped architectures are evaluated using area (A), critical path delay (T), and metricsATandAT2. TheL2error energy is used as a metric for evaluating fixed-point noise levels and the accuracy of the finite precision digital arithmetic circuits.
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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) | 0.003 | 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".