A Partial Reciprocity-based Channel Prediction Framework for FDD Massive MIMO with High Mobility
Ziao Qin, Haifan Yin, Yandi Cao, Weidong Li, David Gesbert

TL;DR
This paper introduces a novel FDD massive MIMO channel prediction framework leveraging partial reciprocity and angle-delay-Doppler structure, significantly improving performance in high mobility scenarios.
Contribution
It proposes a joint angle-delay-Doppler precoder and a CSI acquisition method exploiting partial reciprocity, addressing the mobility challenge in FDD massive MIMO systems.
Findings
CSI prediction error approaches zero with more antennas and bandwidth
Framework performs well at high speeds up to 350 km/h
Effective under large CSI delays and noisy channels
Abstract
Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the ``curse of mobility''. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband precoder is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil (MP) method. It then computes the wideband JADD…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
