HyperRNN: Deep Learning-Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO

TL;DR

This paper introduces HyperRNN, a deep learning architecture that leverages partial channel reciprocity and temporal correlation to improve downlink CSI acquisition in FDD massive MIMO systems, reducing pilot requirements and error.

Contribution

It presents a novel HyperRNN architecture that enhances end-to-end CSI acquisition by exploiting partial reciprocity and temporal correlation in FDD massive MIMO systems.

Findings

HyperRNN achieves lower NMSE compared to existing methods.

It reduces the required pilot lengths for CSI acquisition.

The approach effectively leverages both uplink-downlink reciprocity and temporal correlation.

Abstract

In order to unlock the full advantages of massive multiple input multiple output (MIMO) in the downlink, channel state information (CSI) is required at the base station (BS) to optimize the beamforming matrices. In frequency division duplex (FDD) systems, full channel reciprocity does not hold, and CSI acquisition generally requires downlink pilot transmission followed by uplink feedback. Prior work proposed the end-to-end design of pilot transmission, feedback, and CSI estimation via deep learning. In this work, we introduce an enhanced end-to-end design that leverages partial uplink-downlink reciprocity and temporal correlation of the fading processes by utilizing jointly downlink and uplink pilots. The proposed method is based on a novel deep learning architecture -- HyperRNN -- that combines hypernetworks and recurrent neural networks (RNNs) to optimize the transfer of long-term channel features from uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a lower normalized mean square error (NMSE) performance, and that it reduces requirements in terms of pilot lengths.