Practical RF signal processing pipelines routinely reduce input dimensionality for computational efficiency—
downsampling FFTs from 1024 to 256 bins, truncating or
interpolating sequences to 128 samples. While these optimizations improve latency and memory usage, they introduce
information loss that can degrade classification performance.
We provide a systematic measurement framework for quantifying resampling effects in modular RF ensembles. See
prior work on deep RF modulation recognition and multirate
DSP foundations [1], [2]. Our contributions include: (1) KL
divergence analysis of spectral distortion across FFT target
sizes, (2) accuracy curves for temporal resampling strategies,
(3) reproducible hooks into existing ensemble architectures,
and (4) trade-off guidance for operational parameter selection.