Towards Explainable Machine Learning: The Effectiveness of Reservoir
  Computing in Wireless Receive Processing
Shashank Jere Karim Said Lizhong Zheng Lingjia Liu
Deep learning has seen a rapid adoption in a variety of wireless
communications applications, including at the physical layer. While it has
delivered impressive performance in tasks such as channel equalization and
receive processing/symbol detection, it leaves much to be desired when it comes
to explaining this superior performance. In this work, we investigate the
specific task of channel equalization by applying a popular learning-based
technique known as Reservoir Computing (RC), which has shown superior
performance compared to conventional methods and other learning-based
approaches. Specifically, we apply the echo state network (ESN) as a channel
equalizer and provide a first principles-based signal processing understanding
of its operation. With this groundwork, we incorporate the available domain
knowledge in the form of the statistics of the wireless channel directly into
the weights of the ESN model. This paves the way for optimized initialization
of the ESN model weights, which are traditionally untrained and randomly
initialized. Finally, we show the improvement in receive processing/symbol
detection performance with this optimized initialization through simulations.
This is a first step towards explainable machine learning (XML) and assigning
practical model interpretability that can be utilized together with the
available domain knowledge to improve performance and enhance detection
reliability.