Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning

TL;DR

This paper introduces a novel deep learning framework that leverages partial FDD reciprocity and reconfigurable CSI-RS placement to reduce downlink CSI training overhead and enhance feedback efficiency in Massive MIMO systems.

Contribution

It proposes a new learning-based feedback architecture and CSI-RS placement scheme that significantly reduces training overhead and improves CSI feedback encoding efficiency.

Findings

Superior CSI recovery performance in indoor and outdoor scenarios.

Reduced computational power and storage requirements at user equipment.

Effective exploitation of partial FDD reciprocity and CSI correlations.

Abstract

Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed learning-based DL CSI estimation that lowers uplink feedback overhead. One often overlooked problem is the limited number of DL pilots available for CSI estimation. One proposed solution leverages temporal CSI coherence by utilizing past CSI estimates and only sending CSI-reference symbols (CSI-RS) for partial arrays to preserve CSI recovery performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to base stations with direct access to uplink CSI. In this work, we propose a new learning-based feedback architecture and a reconfigurable CSI-RS placement scheme to reduce DL CSI training overhead and to improve encoding efficiency of CSI feedback. Our results demonstrate superior performance in both indoor and outdoor scenarios by the proposed framework for CSI recovery at substantial reduction of computation power and storage requirements at UEs.