Finite wordlength design for FFT/IFFT in UWB-OFDM systems
Bibliographic record
Abstract
Orthogonal frequency division multiplexing (OFDM) has been proposed for use in ultra-wideband (UWB) communication systems. In a UWB-OFDM transceiver, resource efficient FFT/IFFT hardware is a necessity due to minimal silicon area and low power requirements. Resource requirements can be reduced by using finite precision arithmetic in the FFT/IFFT algorithms. However, this introduces round-off and overflow noise, resulting in a degradation in BER performance. To address this problem, the finite precision arithmetic and wordlengths should be optimally designed at every stage of the FFT/IFFT. In this paper, we first present a mixed-radix FFT/IFFT algorithm for a UWB-OFDM transceiver with low multiplicative complexity. A round-off noise propagation model is derived and used to determine the optimal wordlength of the outputs and twiddle factors at each stage. The performance of the resulting system is compared to that with infinite precision arithmetic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".