Knowledge-distillation-aided Lightweight Neural Network for Massive MIMO CSI Feedback

Abstract

In massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) is required by the base station (BS) to achieve high-performance gains. In frequency division duplexing (FDD) systems, the downlink CSI matrix should be sent back to the BS; unfortunately, the computational and overhead cost of this task is inherently high. Recently, deep learning has been increasingly applied in the space of CSI feedback. However, neural networks entail extra memory and computational requirements, which undermines the deployment of CSI feedback neural networks at the user equipment (UE) side. The conventional lightweight methods such as pruning and quantization requires heavy workload of experiments and difficulty of individually designing training methods for each neural network (NN). In this paper, a novel network lightweight method utilizing knowledge distillation as a training method is introduced to lighten the computation burden of the encoder at the UEs. Knowledge distillation (KD) aims at transferring knowledge from a complex network to a simple network and improving the performance of the simple network close to the complex network. Our numerical experiments demonstrate that the performance of the proposed network can be improved with KD.