Deep Learning Based Fast Downlink Channel Reconstruction For FDD Massive MIMO Systems

Abstract

The spatial reciprocity enables the downlink channel reconstruction in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems by obtaining the frequency-independent parameters in the uplink. However, the algorithms to estimate these parameters are typically complex and time-consuming. In this paper, we regard the channel as an image and utilize you only look once (YOLO), an advanced deep learning-based object detection network, to locate the bright spots in the channel image, then the frequency-independent parameters can be estimated rapidly. Superior to the traditional algorithm that iteratively extracts the paths, YOLO can detect all the path simultaneously. Experimental results show that YOLO can greatly deplete the running time to obtain the frequency-independent parameters and reconstruct the FDD massive MIMO downlink channel with satisfactory accuracy.