Accelerated Learning-Based MIMO Detection through Weighted Neural Network Design

In this paper, we introduce a framework for a systematic acceleration of deep neural network (DNN) design for MIMO detection. A monotonically non-increasing function is used to scale the values of the layer weights such that only a certain fraction of the inputs is used for feedforward computation. This enables a dynamic weight scaling across and within the network layers, and it is termed as weight-scaling neural network-based MIMO detector (WeSNet). To increase the robustness against the changes in the activation patterns and additional enhancement in the detection accuracy for the same inference complexity, we introduce trainable weight-scaling functions. Experimental results show the superiority of our proposed method over the benchmark model (DetNet) and classical approaches based on semi-definite relaxation in terms of detection accuracy and computational efficiency.