Browsing by Subject "5G"

DeepRx is a deep learning receiver which replaces much of the functionality of a traditional 5G receiver. It is a deep model which uses residual connections and a fully convolutional architecture to process an incoming signal, and it outputs log-likelihood ratios for each bit. However, the deep model can be computationally too heavy to use in a real environment. Nokia Bell Labs has recently developed an iterative version of the DeepRx, where a model with fewer layers is used iteratively. This thesis focuses on developing a neural network which determines how many iterations the iterative DeepRx needs to use. We trained a separate neural network, the stopping condition neural network, which will be used together with the iterative model. It predicts the number of iterations the model requires to process the input correctly, with the aim that each inference uses as few iterations as possible. The model also stops the inference early if it predicts that the required number of iterations is greater than the maximum amount. Our results show that an iterative model with a stopping condition neural network has significantly fewer parameters than the deep model. The results also show that while the stopping condition neural network could predict with a high accuracy which samples could be decoded, using it also increased the uncoded bit error rate of the iterative model slightly. Therefore, using a stopping condition neural network together with an iterative model seems to be a flexible lightweight alternative to the DeepRx model.