Structured Compressive Sensing Based Superimposed Pilot Design in Downlink Large-Scale MIMO Systems

TL;DR

This paper introduces a spectrum-efficient superimposed pilot scheme for large-scale MIMO systems, leveraging structured compressive sensing and a novel SSP algorithm to improve channel estimation with reduced pilot overhead.

Contribution

It proposes a new superimposed pilot design and a structured compressive sensing-based channel estimation method tailored for large-scale MIMO systems.

Findings

The scheme approaches the theoretical performance bound.

It significantly reduces pilot overhead compared to traditional methods.

Simulation results validate the effectiveness of the proposed approach.

Abstract

Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large-scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, we propose a spectrum-efficient superimposed pilot design for downlink large-scale MIMO scenarios, where frequency-domain pilots of different transmit antennas occupy the completely same subcarriers in the freqency domain. Meanwhile, spatial-temporal common sparsity of large-scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realized by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound.