Deep learning‐based massive multiple‐input multiple‐output channel state information feedback with data normalisation using clipping

Abstract

Abstract Massive multiple‐input multiple‐output (MIMO) can provide real‐time high‐capacity data transmission service to the user equipment (UE). However, the use of massive MIMO exponentially increases the channel state information (CSI) feedback overhead. Deep learning‐based approaches have been proposed to reduce CSI feedback overhead with significant CSI accuracy. Data normalisation is a very important part of deep learning, because the performance of the deep learning highly depends on the data normalisation. In this letter, an efficient data normalisation method for deep learning‐based CSI feedback in a massive MIMO system is proposed, where the proposed method uses a clipping technique based on the received signal strength. Simulation results show that the proposed normalisation with the clipping decreases the CSI feedback error and increases the accuracy of the beamforming vector in deep learning‐based CSI feedback.

Keywords

• Signal processing and detection
• Radio links and equipment
• Digital signal processing
• Communications computing
• Neural nets