ADE-MI compresses each WiFi device's sensing spectrogram to a channel-capacity-sized feature vector and trains a server-side multi-view classifier, reporting 92% accuracy with roughly 10^4 lower upload latency than raw transmission.
A novel ISAC transmission framework based on spatially-spre ad orthog- onal time frequency space modulation,
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Channel Capacity-Aware Distributed Encoding for Multi-View Sensing and Edge Inference
ADE-MI compresses each WiFi device's sensing spectrogram to a channel-capacity-sized feature vector and trains a server-side multi-view classifier, reporting 92% accuracy with roughly 10^4 lower upload latency than raw transmission.