Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems

Excerpt

arXiv:2605.14331v2 Announce Type: replace-cross Abstract: Modern edge devices increasingly rely on neural networks for intelligent applications. However, conventional digital computing-based edge inference requires substantial memory and energy consumption. In analog radio frequency (RF) computing, a base station (BS) encodes the weights of the neural networks and broadcasts the RF waveforms to the clients. Each client reuses its passive mixer to multiply the received weight-encoded waveform wit