Radiofrequency signal processing with a memristive system-on-a-chip

Abstract

The development of wireless communication technology and the Internet of Things requires radiofrequency communication systems with higher frequencies and faster communication speeds. However, traditional digital processing platforms—which involve high-speed analogue-to-digital converters, intensive data movement and complex digital computation in software-defined radio systems—suffer from high energy consumption and latency. Signal processing in the analogue domain using non-volatile memristive devices can reduce data movement and energy consumption, but the development of system-level designs remains limited. Here we report a radiofrequency signal processing system that is based on analogue in-memory computing within a multicore memristive system-on-a-chip. With the approach, we demonstrate an analogue discrete Fourier transform for spectrum analysis, a mixer-free demodulator for in-phase and quadrature demodulation, and analogue neural networks for radiofrequency transmitter identification and anomaly detection. The memristive system-on-a-chip offers an identification accuracy of over 90% and is up to 6.8 times more energy efficient and up to 6.2 times faster than traditional digital processing platforms.