Neuromorphic Computing in Communications

Brain-Inspired computing, such as Reservoir Computing, provides a new paradigm of data-driven algorithm design for communication systems. The rich dynamics behavior of Reservoir Computing may help build simplified signal detection algorithms using efficient training techniques.

Future communication systems, such as the 5G/6H wireless networks, face many new design and implementation challenges. For example, traditional model-based algorithms may not scale well with massive MIMO antenna systems and have model mismatch problems in real-world environments. Furthermore, their high complexity hinders power efficiency for mobile and IoT applications.

We have adopted Echo State Networks (ESNs) and demonstrated their superior performance in a real-time Software-Defined Radio (SDR) testbed. Our ESN-based MIMO-OFDM symbol detection system is more resilient and power efficient than conventional algorithms widely used in the current 5G systems. We are also exploring other types of Neuromorphic Computing techniques for applications in wireless systems.

Currently involved students

• Chunxiao (Charles) Lin
• Yibin (Donald) Liang