SNOPS: Short Non-Orthogonal Pilot Sequences for Downlink Channel State Estimation in FDD Massive MIMO

TL;DR

This paper proposes a novel scheme using short, non-orthogonal pilot sequences and user feedback to efficiently estimate downlink CSI in FDD Massive MIMO systems, reducing pilot length while maintaining accuracy.

Contribution

It introduces a new non-orthogonal pilot sequence design leveraging spatial correlation and feedback, enabling shorter pilot sequences than the number of antennas.

Findings

Pilot sequences shorter than the number of antennas are feasible.

CSI estimation error can be made arbitrarily small.

The proposed method performs well in realistic channel scenarios.

Abstract

Channel state information (CSI) acquisition is a significant bottleneck in the design of Massive MIMO wireless systems, due to the length of the training sequences required to distinguish the antennas (in the downlink) and the users (for the uplink where a given spectral resource can be shared by a large number of users). In this article, we focus on the downlink CSI estimation case. Considering the presence of spatial correlation at the base transceiver station (BTS) side, and assuming that the per-user channel statistics are known, we seek to exploit this correlation to minimize the length of the pilot sequences. We introduce a scheme relying on non-orthogonal pilot sequences and feedback from the user terminal (UT), which enables the BTS to estimate all downlink channels. Thanks to the relaxed orthogonality assumption on the pilots, the length of the obtained pilot sequences can be strictly lower than the number of antennas at the BTS, while the CSI estimation error is kept arbitrarily small. We introduce two algorithms to dynamically design the required pilot sequences, analyze and validate the performance of the proposed CSI estimation method through numerical simulations using a realistic scenario based on the one-ring channel model.