High-Dimensional CSI Acquisition in Massive MIMO: Sparsity-Inspired Approaches

TL;DR

This paper reviews sparsity-inspired techniques for acquiring high-dimensional channel state information in massive MIMO systems, aiming to reduce overhead and enhance performance in 5G networks.

Contribution

It provides a comprehensive discussion of mathematical theories and compares sparsity-based CSI acquisition methods for FDD and TDD massive MIMO systems.

Findings

Sparsity structures enable significant CSI acquisition overhead reduction.

Comparison of FDD and TDD approaches highlights trade-offs in system design.

Future research directions for practical high-dimensional CSI acquisition are identified.

Abstract

Massive MIMO has been regarded as one of the key technologies for 5G wireless networks, as it can significantly improve both the spectral efficiency and energy efficiency. The availability of high-dimensional channel side information (CSI) is critical for its promised performance gains, but the overhead of acquiring CSI may potentially deplete the available radio resources. Fortunately, it has recently been discovered that harnessing various sparsity structures in massive MIMO channels can lead to significant overhead reduction, and thus improve the system performance. This paper presents and discusses the use of sparsity-inspired CSI acquisition techniques for massive MIMO, as well as the underlying mathematical theory. Sparsity-inspired approaches for both frequency-division duplexing and time-division duplexing massive MIMO systems will be examined and compared from an overall system perspective, including the design trade-offs between the two duplexing modes, computational complexity of acquisition algorithms, and applicability of sparsity structures. Meanwhile, some future prospects for research on high-dimensional CSI acquisition to meet practical demands will be identified.