A Deep Learning-Driven Solution to Limited-Feedback MIMO Relaying Systems

Kwadwo Boateng Ofori-Amanfo, Bridget Durowaa Antwi-Boasiako, Prince Anokye, Suho Shin and Kyoung-Jae Lee ()
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Kwadwo Boateng Ofori-Amanfo: Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Bridget Durowaa Antwi-Boasiako: Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Prince Anokye: Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Suho Shin: Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Kyoung-Jae Lee: Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea

Mathematics, 2025, vol. 13, issue 14, 1-13

Abstract: 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.

Keywords: multiple-input multiple-output (MIMO); deep learning (DL); deep neural network (DNN); limited feedback; relay; symbol error rate (SER); codebook (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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