Analog neural network–based wireless receiver

Abstract

Traditional wireless receivers underperform in complex propagation environments and in the presence of nonlinear interference. Although data-driven artificial intelligence models offer a promising alternative, their implementation on conventional digital processing platforms is often limited by energy consumption and latency. Here, we present an analog wireless receiver that leverages the feature extraction and learning capabilities of neural networks for channel equalization, filtering, signal routing, and symbol detection. This modular architecture eliminates the traditional mixer for down-conversion and simplifies its implementation using analog in-memory computing hardware, allowing direct decoding of data from high-frequency analog signals. We demonstrate the adaptability, energy efficiency and processing speed of the proposed receiver through a proof-of-concept hardware implementation using memristor crossbar arrays on a system-on-a-chip platform. The fully hardware-implemented receiver achieves a bit error rate of 10−6 in detecting randomly generated symbols from distorted analog signals across multiple modulation schemes and carrier frequencies, highlighting its potential for future wireless communication systems for bandwidth-intensive and latency-sensitive applications.