Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems

Abstract

We consider downlink (DL) channel estimation for frequency division duplex based massive MIMO systems under the multipath model. Our goal is to provide fast and accurate channel estimation from a small amount of DL training overhead. Prior art tackles this problem using compressive sensing or classic array processing techniques (e.g., ESPRIT and MUSIC). However, these methods have challenges in some scenarios, e.g., when the number of paths is greater than the number of receive antennas. Tensor factorization methods can also be used to handle such challenging cases, but it is hard to solve the associated optimization problems. In this work, we propose an efficient channel estimation framework to circumvent such difficulties. Specifically, a structural training sequence that imposes a tensor structure on the received signal is proposed. We show that with such a training sequence, the parameters of DL MIMO channels can be provably identified even when the number of paths largely exceeds the number of receive antennas---under very small training overhead. Our approach is a judicious combination of Vandermonde tensor algebra and a carefully designed conjugate-invariant training sequence. Unlike existing tensor-based channel estimation methods that involve hard optimization problems, the proposed approach consists of very lightweight algebraic operations, and thus real-time implementation is within reach. Simulation results are carried out to showcase the effectiveness of the proposed methods.

References in corpus (12)

• Massive MIMO for Next Generation Wireless Systems
• An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
• Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
• Tensor Decomposition for Signal Processing and Machine Learning
• Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference
• Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
• Efficient Transmit Beamspace Design for Search-free Based DOA Estimation in MIMO Radar
• Subspace Leakage Analysis and Improved DOA Estimation with Small Sample Size
• Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems
• Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems
• Limited Feedback Channel Estimation in Massive MIMO with Non-uniform Directional Dictionaries
• Preliminary Results on 3D Channel Modeling: From Theory to Standardization

Cited by in corpus (4)

• Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation
• Towards Overfitting Avoidance: Tuning-free Tensor-aided Multi-user Channel Estimation for 3D Massive MIMO Communications
• A Tensor-based Approach to Joint Channel Estimation / Data Detection in Flexible Multicarrier MIMO Systems
• Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm