Dissertation

Channel Estimation for Massive Mimo Systems

Liao, Mingduo

2022

Overview

Massive multiple input multiple output (MIMO) systems can significantly improve the channel capacity by deploying multiple antennas at the transmitter and receiver. Massive MIMO is considered as one of key technologies of the next generation of wireless communication systems. However, with the increase of the number of antennas at the base station, a large number of unknown channel parameters need to be dealt with, which makes the channel estimation a challenging problem. Hence, the research on the channel estimation for massive MIMO is of great importance to the development of the next generation of communication systems. The wireless multipath channel exhibits sparse characteristics, but the traditional channel estimation techniques do not make use of the sparsity. The channel estimation based on compressive sensing (CS) can make full use of the channel sparsity, while use fewer pilot symbols. In this work, CS channel estimation methods are proposed for massive MIMO systems in complex environments operating in multipath channels with static and time-varying parameters. Firstly, a CS channel estimation algorithm for massive MIMO systems with Orthogonal Frequency Division Multiplexing (OFDM) is proposed. By exploiting the spatially common sparsity in the virtual angular domain of the massive MIMO channels, a dichotomous-coordinate-decent-joint-sparse-recovery (DCD-JSR) algorithm is proposed. More specifically, by considering the channel is static over several OFDM symbols and exhibits common sparsity in the virtual angular domain, the DCD-JSR algorithm can jointly estimate multiple sparse channels with low computational complexity. The simulation results have shown that, compared to existing channel estimation algorithms such as the distributed-sparsity-adaptive-matching-pursuit (DSAMP) algorithm, the proposed DCD-JSR algorithm has significantly lower computational complexity and better performance. Secondly, these results have been extended to the case of multipath channels with time-varying parameters. This has been achieved by employing the basis expansion model to approximate the time variation of the channel, thus the modified DCD-JSR algorithm can estimate the channel in a massive MIMO OFDM system operating over frequency selective and highly mobile wireless channels. Simulation results have shown that, compared to the DCD-JSR algorithm designed for time-invariant channels, the modified DCD-JSR algorithm provides significantly better estimation performance in fast time-varying channels.

• Publisher: ProQuest Dissertations & Theses
• Subject: Sparsity