A Deep Learning-Driven Solution to Limited-Feedback MIMO Relaying Systems - Kwadwo Boateng Ofori-Amanfo; Bridget Durowaa Antwi-Boasiako; Prince Anokye; Suho Shin; Kyoung-Jae Lee (Mathematics)
In this work, we investigate a new design strategy for the implementation of a deep neural network (DNN)-based limited-feedback relay system by using conventional filters to acquire training data in order to jointly solve the issues of quantization and feedback. We aim to maximize the effective channel gain to reduce the symbol error rate (SER). By harnessing binary feedback information from the implemented DNNs together with efficient beamforming vectors, a novel approach to the resulting problem is presented. We compare our proposed system to a Grassmannian codebook system to show that our system outperforms its benchmark in terms of SER.