A deep learning-based channel estimation scheme for cell-free massive MIMO systems

Cell-free massive multiple-input-multiple-output (MIMO) is a technique that couples the cell-free network architecture and massive antenna arrays. In cell-free massive MIMO, multiple access points (APs) are collocated to serve fewer user equipment (UEs), which results in a system with more APs than UEs. To achieve optimum transmission performance, massive MIMO requires knowledge of accurate channel state information (CSI). However, the conventional method of CSI estimation, based on minimum mean square error, suffers from high computational complexity, pilot contamination, and noise interference, which degrade the performance of the system. In this paper, we propose a deep learning-based channel estimation approach that makes use of a deep neural network to provide a scalable and efficient channel estimation scheme. Simulation results showed that the proposed scheme consistently outperformed conventional cell-free massive MIMO, small cell network, and cellular massive MIMO architectures.