Performance Enhancement of Massive MIMO Using Deep Learning-Based Channel Estimation
Hany Helmy, S El Daysti, Hazem Shatila, Mohamed A. Aboul-Dahab
Abstract
Abstract Massive Multiple-Input Multiple-Output (massive MIMO) system relies on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. However, transmission of massive MIMO system is subject to excessive feedback overhead. In this paper, we propose a Deep Learning (DL) approach-based channel estimation technique to enhance the performance of massive MIMO system. This technique is used to enhance recovery quality and improve trade-off between compression ratio (CR) and complexity of massive MIMO system. The proposed technique is based upon using the Channel State Information Network combined with gated recurrent unit (CsiNet-GRU). Moreover, the dropout method is used in the proposed technique to reduce overfitting during the learning process. The simulation results demonstrate that the proposed CsiNet-GRU technique results in a significant improvement in performance when compared with existing techniques used in conjunction with massive MIMO systems.