Complex artificial neural network with applications to wireless communications

We have developed a complex artificial neural network based radio receiver technology. This is based on novel complex neurons and deep networks of them. This type of radio does not have MIMO decoder or demodulator in a classical sense but instead the radio learns to demodulate through training - and can potentially adapt to jamming through a learning process. This type of radio can continuously adapt to changing environment and can potentially increase interference resistance spectral efficiency of modern radio networks. Complex neurons can natively operate on complex numbers, including multiplying (scale, rotation) of the input data. These networks, however, have proven chaotic and the convergence has proven to be highly problematic. The difficulty of determining complex coefficients, i.e. training, has made the practical use of these networks difficult or impossible. In this research, a new type of neuron is used with a network architecture that avoids most of the issues plaguing the convergence and training of the network. Combination of a new neuron weight update algorithm and the use of layers with nonlinear selection functions and methods to avoid large single error values in backpropagation allow complex neural network to be trained with fast convergence. (C) 2021 Elsevier Inc. All rights reserved.