Distributed Structured Compressive Sensing-Based Time-Frequency Joint Channel Estimation for Massive MIMO-OFDM Systems

Abstract

In massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, accurate channel state information (CSI) is essential to realize system performance gains such as high spectrum and energy efficiency. However, high-dimensional CSI acquisition requires prohibitively high pilot overhead, which leads to a significant reduction in spectrum efficiency and energy efficiency. In this paper, we propose a more efficient time-frequency joint channel estimation scheme for massive MIMO-OFDM systems to resolve those problems. First, partial channel common support (PCCS) is obtained by using time-domain training. Second, utilizing the spatiotemporal common sparse property of the MIMO channels and the obtained PCCS information, we propose the priori-information aided distributed structured sparsity adaptive matching pursuit (PA-DS-SAMP) algorithm to achieve accurate channel estimation in frequency domain. Third, through performance analysis of the proposed algorithm, two signal power reference thresholds are given, which can ensure that the signal can be recovered accurately under power-limited noise and accurately recovered according to probability under Gaussian noise. Finally, pilot design, computational complexity, spectrum efficiency, and energy efficiency are discussed as well. Simulation results show that the proposed method achieves higher channel estimation accuracy while requiring lower pilot sequence overhead compared with other methods.

Channel state information acquisition method of internet of things based on deep learning

“…Traditional CSI acquisition methods, such as vector quantization or codebook‐based methods, can no longer match the current large‐scale MIMO high‐quality communication requirements. In recent years, some scholars have proposed to process channel state information based on Compressed Sensing (CS) [17]. Reference [18] adopts the common sparse characteristic of each sub‐carrier channel in the imaginary angle domain and the time‐correlation characteristic of the sparse support set in the orthogonal frequency division multiplexing system to achieve the purpose of reducing the channel dimension.…”

A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE

“…Most of the work that has been done in the area of iterative receivers performing joint detection and channel estimation for massive MIMO is based on OFDM [17,18]. This article focusses on SC-FDE, using an iterative receiver specific of this transmission technique.…”