Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems
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 with a locally generated input-encoded waveform. This enables wireless receivers to perform the matrix-vector multiplications (MVMs) that account for most of the computation burden in edge inference with ultra-low energy consumption. Unlike conventional down...
| Authors | Wentao Yu, Vincent W. S. Wong |
|---|---|
| Primary category | eess.SP |
| Published | 14 May 2026 |
| Last updated at source | 2 October 2026 |
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