Evolutionary Optimization of Residual Neural Network Architectures for
Modulation Classification
Abstract
Automatic Modulation Classification (AMC) receives significant interest
in the context of current and future wireless communication systems.
Deep learning emerged as a powerful AMC tool, as it allows for the joint
learning of discriminative features, and signal classification. However,
the optimization of Deep Neural Network (DNN) architectures for AMC is a
manual and time-consuming process that requires profound domain
knowledge and much effort. Moreover, most proposed solutions focus
mainly on classification accuracy, while optimization of network
complexity is neglected. In this paper, we propose a novel bi-objective
memetic algorithm, BO-NSMA, to search optimal DNN architectures for AMC
to maximize classification accuracy and minimize network complexity. We
show that BO-NSMA, with a small initial population of six individuals
and only ten generations, finds a DNN architecture that outperforms all
human-crafted State-of-the-Art (SoA) models. BO-NSMA discovered the
first low-complexity Convolutional Neural Network (CNN)-based model,
which achieves slightly better performance than costly Recurrent Neural
Network (RNN)-based approaches, allowing a 2.8-fold reduction in network
complexity with 0.7% performance improvement. Compared to its
counterparts from Network Architecture Search (NAS), BO-NSMA finds the
best architecture, which achieves up to 18.24% accuracy gain and up to
a 78.71-fold reduction in network complexity.