Channel estimation in massive MIMO systems

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance

Millimeter Wave Systems for Wireless Cellular Communications

This thesis considers channel estimation and multiuser (MU) data transmission for massive MIMO systems with fully digital/hybrid structures in mmWave channels. It contains three main contributions. In this thesis, we first propose a tone-based linear search algorithm to facilitate the estimation of angle-of-arrivals of the strongest components as well as scattering components of the users at the base station (BS) with fully digital structure. Our results show that the proposed maximum-ratio transmission (MRT) based on the strongest components can achieve a higher data rate than that of the conventional MRT, under the same mean squared errors (MSE). Second, we develop a low-complexity channel estimation and beamformer/precoder design scheme for hybrid mmWave systems. In addition, the proposed scheme applies to both non-sparse and sparse mmWave channel environments. We then leverage the proposed scheme to investigate the downlink achievable rate performance. The results show that the proposed scheme obtains a considerable achievable rate of fully digital systems. Taking into account the effect of various types of errors, we investigate the achievable rate performance degradation of the considered scheme. Third, we extend our proposed scheme to a multi-cell MU mmWave MIMO network. We derive the closed-form approximation of the normalized MSE of channel estimation under pilot contamination over Rician fading channels. Furthermore, we derive a tight closed-form approximation and the scaling law of the average achievable rate. Our results unveil that channel estimation errors, the intra-cell interference, and the inter-cell interference caused by pilot contamination over Rician fading channels can be efficiently mitigated by simply increasing the number of antennas equipped at the desired BS.Comment: Thesi

MIMO signal processing in offset-QAM based filter bank multicarrier systems

Next-generation communication systems have to comply with very strict requirements for increased flexibility in heterogeneous environments, high spectral efficiency, and agility of carrier aggregation. This fact motivates research in advanced multicarrier modulation (MCM) schemes, such as filter bank-based multicarrier (FBMC) modulation. This paper focuses on the offset quadrature amplitude modulation (OQAM)-based FBMC variant, known as FBMC/OQAM, which presents outstanding spectral efficiency and confinement in a number of channels and applications. Its special nature, however, generates a number of new signal processing challenges that are not present in other MCM schemes, notably, in orthogonal-frequency-division multiplexing (OFDM). In multiple-input multiple-output (MIMO) architectures, which are expected to play a primary role in future communication systems, these challenges are intensified, creating new interesting research problems and calling for new ideas and methods that are adapted to the particularities of the MIMO-FBMC/OQAM system. The goal of this paper is to focus on these signal processing problems and provide a concise yet comprehensive overview of the recent advances in this area. Open problems and associated directions for future research are also discussed.Peer ReviewedPostprint (author's final draft

Wireless communication, sensing, and REM: A security perspective

The diverse requirements of next-generation communication systems necessitate awareness, flexibility, and intelligence as essential building blocks of future wireless networks. The awareness can be obtained from the radio signals in the environment using wireless sensing and radio environment mapping (REM) methods. This is, however, accompanied by threats such as eavesdropping, manipulation, and disruption posed by malicious attackers. To this end, this work analyzes the wireless sensing and radio environment awareness mechanisms, highlighting their vulnerabilities and provides solutions for mitigating them. As an example, the different threats to REM and its consequences in a vehicular communication scenario are described. Furthermore, the use of REM for securing communications is discussed and future directions regarding sensing/REM security are highlighted

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

Compensation of Physical Impairments in Multi-Carrier Communications

Among various multi-carrier transmission techniques, orthogonal frequency-division multiplexing (OFDM) is currently a popular choice in many wireless communication systems. This is mainly due to its numerous advantages, including resistance to multi-path distortions by using the cyclic prefix (CP) and a simple one-tap channel equalization, and efficient implementations based on the fast Fourier and inverse Fourier transforms. However, OFDM also has disadvantages which limit its use in some applications. First, the high out-of-band (OOB) emission in OFDM due to the inherent rectangular shaping filters poses a challenge for opportunistic and dynamic spectrum access where multiple users are sharing a limited transmission bandwidth. Second, a strict orthogonal synchronization between sub-carriers makes OFDM less attractive in low-power communication systems. Furthermore, the use of the CP in OFDM reduces the spectral efficiency and thus it may not be suitable for short-packet and low-latency transmission applications. Generalized frequency division multiplexing (GFDM) and circular filter-bank multi-carrier offset quadrature amplitude modulation (CFBMC-OQAM) have recently been considered as alternatives to OFDM for the air interface of wireless communication systems because they can overcome certain disadvantages in OFDM. Specifically, these two systems offer a flexibility in choosing the shaping filters so that the high OOB emission in OFDM can be avoided. Moreover, the strict orthogonality requirement in OFDM is relaxed in GFDM and CFBMC-OQAM which are, respectively, non-orthogonal and real-field orthogonal systems. Although a CP is also used in these two systems, the CP is added for a block of many symbols instead of only one symbol as in OFDM, which, therefore, improves the spectral efficiency. Given that the performance of a wireless communication system is affected by various physical impairments such as phase noise (PN), in-phase and quadrature (IQ) imbalance and imperfect channel estimation, this thesis proposes a number of novel signal processing algorithms to compensate for physical impairments in multi-carrier communication systems, including OFDM, GFDM and CFBMC-OQAM. The first part of the thesis examines the use of OFDM in full-duplex (FD) communication under the presence of PN, IQ imbalance and nonlinearities. FD communication is a promising technique since it can potentially double the spectral efficiency of the conventional half-duplex (HD) technique. However, the main challenge in implementing an FD wireless device is to cope with the self-interference (SI) imposed by the device's own transmission. The implementation of SI cancellation (SIC) faces many technical issues due to the physical impairments. In this part of research, an iterative algorithm is proposed in which the SI cancellation and detection of the desired signal benefit from each other. Specifically, in each iteration, the SI cancellation performs a widely linear estimation of the SI channel and compensates for the physical impairments to improve the detection performance of the desired signal. The detected desired signal is in turn removed from the received signal to improve SI channel estimation and SI cancellation in the next iteration. Results obtained show that the proposed algorithm significantly outperforms existing algorithms in SI cancellation and detection of the desired signal. In the next part of the thesis, the impact of PN and its compensation for CFBMC-OQAM systems are considered. The sources of performance degradation are first quantified. Then, a two-stage PN compensation algorithm is proposed. In the first stage, the channel frequency response and PN are estimated based on the transmission of a preamble, which is designed to minimize the channel mean squared error (MSE). In the second stage the PN compensation is performed using the estimate obtained from the first stage together with the transmitted pilot symbols. Simulation results obtained under practical scenarios show that the proposed algorithm effectively estimates the channel frequency response and compensates for the PN. The proposed algorithm is also shown to outperform an existing algorithm that implements iterative PN compensation when the PN impact is high. As a further development from the second part, the third part of the thesis considers the impacts of both PN and IQ imbalance and proposes a unified two-stage compensation algorithm for a general multi-carrier system, which can include OFDM, GFDM and CFBMC-OQAM. Specifically, in the first stage, the channel impulse response and IQ imbalance parameters are first estimated based on the transmission of a preamble. Given the estimates obtained from the first stage, in the second stage the IQ imbalance and PN are compensated in that order based on the pilot symbols for the rest of data transmission blocks. The preamble is designed such that the estimation of IQ imbalance does not depend on the channel and PN estimation errors. The proposed algorithm is then further extended to a multiple-input multiple-output (MIMO) system. For such a MIMO system, the preamble design is generalized so that the multiple IQ imbalances as well as channel impulse responses can be effectively estimated based on a single preamble block. Simulation results are presented and discussed in a variety of scenarios to show the effectiveness of the proposed algorithm