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arXiv Machine Learning By Wentao Yu, Vincent W. S. Wong

Analog RF Computing for Integrated Data and AI Flows Over Wireless Networks

Read the original on arXiv Machine Learning →

The paper proposes using analog radio frequency (RF) computing to enable edge devices to perform AI inference by receiving a waveform that encodes neural network weights from a base station. Clients then mix this waveform with their own input-encoded signals to perform matrix‑vector multiplications with minimal power and memory. In a multi‑user MIMO downlink setting, the authors formulate a joint beamforming optimization to balance data and AI flows, showing that the aggregate data rate loss is below 5% under realistic 3GPP channel conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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