ISAC with Co-Prime Arrays: Virtual-Aperture Sensing and uplink downlink communications

What the math gives to ML

The paper's transferable asset is a co-prime sparse geometry whose cross-sum produces many distinct virtual positions from only two short physical subarrays. In a neural sequence model, the same construction can generate a large, structured receptive field using two sparse mixing stages with offsets drawn from coprime dilation sets, rather than dense attention over all intermediate positions. The key engineering hypothesis is that coprimality gives better long-range coverage per edge while preserving hardware-friendly regular sparsity. This is most promising as a drop-in sparse-attention or token-mixing block, with experiments comparing quality and attention FLOPs against local, dilated, and block-sparse baselines.