Channel Estimation for Massive MIMO Systems

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

Channel Modelling and Estimation in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing Wireless Communication Systems

In wireless communications, the demands for high data rates, enhanced mobility, improved coverage, and link reliability have enormously increased in recent years and are expected to further increase in the near future. To meet these requirements, new concepts and technologies are needed. Theoretical studies have shown that using multiple antennas at the transmitter and receiver, known as multiple-input multipleoutput (MIMO) technology, can dramatically increase the capacity, coverage, and link reliability of a communication system. Orthogonal frequency-division multiplexing (OFDM) is an attractive technique for high data rates transmission over frequency-selective fading channels, due to its capability in combating the intersymbol interference (ISI). The combination of MIMO and OFDM results in a powerful technique that incorporates the advantages of both MIMO and OFDM, and is a strong candidate for fourth generation (4G) wireless communication systems. In this thesis, two issues related to realizing practical mobile MIMO OFDM communication systems are addressed. The first issue is about MIMO channel modeling and effect of realistic channels on the theoretical capacity. For this target, a geometrically-based three-dimensional (3-D) scattering MIMO channel model is developed. The correlation expressions are derived and analytically evaluated. The impact of spatial correlation on MIMO channel capacity is investigated under different antenna array configurations, angular energy distributions, and parameters. Analytical and numerical results have shown that the elevation angle has considerable effect on the spatial correlation and consequently on the MIMO channel capacity for the case when the antenna array of the mobile station (MS) is vertically oriented. This has led to a conclusion that 3-D scattering MIMO channel modeling is necessary for accurate prediction of MIMO system performance. The second issue addressed in this thesis is the channel estimation in MIMO OFDM systems. New time-domain (TD) adaptive estimation methods based on recursive least squares (RLS) and normalized least-mean squares (NLMS) algorithms are proposed. These estimators are then extended to blindly track the time-variations of the channel in the decision-directed (DD) mode. Simulation results have shown that TD adaptive channel estimation and tracking in MIMO OFDM systems is very effective in slow to moderate time-varying fading channels. It was observed that the performance of the DD RLS-based estimator always outperform that of the DD NLMS estimator at low mobility and low SNR. In contrast, it was found that the DD NLMS estimator gives better tracking performance at moderate mobility and higher SNR. However, as the training rate is reduced, comparable performance with both estimators is obtained at high SNR. Finally, it has been shown that channel estimation in TD is more accurate with less complexity compared to its counterpart in frequency-domain (FD)

A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels

In this paper, we establish a general framework on the reduced dimensional channel state information (CSI) estimation and pre-beamformer design for frequency-selective massive multiple-input multiple-output MIMO systems employing single-carrier (SC) modulation in time division duplex (TDD) mode by exploiting the joint angle-delay domain channel sparsity in millimeter (mm) wave frequencies. First, based on a generic subspace projection taking the joint angle-delay power profile and user-grouping into account, the reduced rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived for spatially correlated wideband MIMO channels. Second, the statistical pre-beamformer design is considered for frequency-selective SC massive MIMO channels. We examine the dimension reduction problem and subspace (beamspace) construction on which the RR-MMSE estimation can be realized as accurately as possible. Finally, a spatio-temporal domain correlator type reduced rank channel estimator, as an approximation of the RR-MMSE estimate, is obtained by carrying out least square (LS) estimation in a proper reduced dimensional beamspace. It is observed that the proposed techniques show remarkable robustness to the pilot interference (or contamination) with a significant reduction in pilot overhead