| dc.description.abstract | Large scale antenna or massive multiple input multiple output (MIMO) systems are one of the key enabling technologies for fifth generation (5G) of wireless communications networks and beyond. This technology offers huge advantages in terms of energy efficiency, spectral efficiency, robustness and reliability. However, there are some challenges that prevent the realization of full potential of massive MIMO technology. For instance, performance of massive MIMO systems heavily relies on accurate synchronization. While orthogonal frequency division multiplexing (OFDM) is commonly used as a multicarrier modulation technique in massive MIMO systems, and it is very sensitive to frequency synchronization errors. In contrast, filter-bank multicarrier modulation (FBMC) based waveforms are more robust against frequency offset. Thus, the application of FBMC pulse amplitude modulation (FBMC-PAM) to massive MIMO is proposed in this thesis as an alternative to OFDM. It is also demonstrated that due to the absence of cycle prefix (CP), FBMC-PAM can provide a better bit error rate (BER) performance than OFDM. In addition, it is observed that as the number of base station (BS) antennas increases, the performance of the massive MIMO system with FBMC-PAM saturates. In fact, in an asymptotic regime, the noise effect and multi-user interference (MUI) are averaged out, but some residual effects of multipath channel will remain. This saturation level, which is the upper bound for the system performance, is mathematically calculated and confirmed with simulations. Moreover, it is shown that by increasing the number of subcarriers, a higher upper bound can be achieved. On the other hand, the CP in OFDM systems effectively mitigates the effects of multipath channel, and there will be no saturation in an asymptotic regime. Following this results, we focus on OFDM-based massive MIMO systems for the rest of this study. Hence, to meet the requirement of accurate synchronization in these systems, a low-complexity frequency synchronization technique is proposed. It is shown that the phase information of the covariance of the received signals at the BS antennas is a function of carrier frequency offset (CFO), and if real-valued pilots are utilized, CFO can be simply calculated from the phase information. It should be noted that due to the spatial multiplexing in massive MIMO systems, all the users can simultaneously share the entire available bandwidth, and channel state information (CSI) of users is used to distinguish their signals. However, at the CFO estimation stage, the CSI knowledge is unknown. Thus, a set of rectangular-shaped real-valued pilots are designed to preserve the orthogonality of the users, and a closed form formula is calculated for CFO estimation. Since this technique has strict limitations on pilot design, another CFO estimation technique is proposed which is more general and can work with any pilots. In this technique, the desired user signal is separated from the received signal by using a matrix orthogonal to the space spanned by the pilot of that user. It is proved that the objective function of the proposed optimization problem is unimodal and can be simply solved by Golden search algorithm. Furthermore, it is noticed that the massive MIMO system with the time domain CFO compensation requires a separate receiver for each user which imposes a huge amount of computational complexity to the system. Therefore, a frequency domain CFO compensation technique is proposed which takes place after combining the received signals at the BS. Thus, one receiver is sufficient for all the users in the network, and the complexity of the receiver is considerably reduced. In addition, it is proved that by applying this CFO compensation technique, even in the presence of CFO estimation error, the scattering effect of CFO is removed and only a phase shift remains. Hence, two iterative error correction algorithms are proposed to improve the synchronization accuracy. Simulation results demonstrate that the BER of the system with this synchronization technique matches that of the perfect synchronous system.
We then move on to the challenge of CSI acquisition in massive MIMO systems. This is important because the CSI estimation becomes a bottleneck as the scale of the antenna array increases or the number of users in the network grows large. To avoid the pilot overhead, a deep learning (DL) aided blind channel estimation technique is proposed. First, the MUI is canceled by calculating the orthogonal complement space matrix of the MUI. Then, based on the asymptotic orthogonality of the massive MIMO channels, the first OFDM symbol of all the users is extracted as a virtual pilots. However, in practice, noise and MUI are not completely removed, and the remaining part is also intensified after this process. Therefore, denoising convolutional neural network (DnCNN) is deployed as a denoiser to deal with the remaining interference. Another DL-based algorithm called U-Net is also proposed to be used as a denoiser that can outperform DnCNN when the noise level is high. Moreover, a ResNet architecture followed by a feedforward neural network is proposed to force the network to converge to the expected values. This further enhances the performance of the virtual pilot detection. Finally, maximum likelihood (ML) estimator is employed to estimate the CSIs. | en |
