Mobile Cell-Free Massive MIMO: Challenges, Solutions, and Future Directions

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems, which exploit many geographically distributed access points to coherently serve user equipments via spatial multiplexing on the same time-frequency resource, has become a vital component of the next-generation mobile communication networks. Theoretically, CF massive MIMO systems have many advantages, such as large capacity, great coverage, and high reliability, but several obstacles must be overcome. In this article, we study the paradigm of CF massive MIMO-aided mobile communications, including the main application scenarios and associated deployment architectures. Furthermore, we thoroughly investigate the challenges of CF massive MIMO-aided mobile communications. We then exploit a novel predictor antenna, hierarchical cancellation, rate-splitting and dynamic clustering system for CF massive MIMO. Finally, several important research directions regarding CF massive MIMO for mobile communications are presented to facilitate further investigation.Comment: 9 pages, 4 figures, 2 tables, accepted by IEEE Wireless Communications Magazin

Predictor Antenna Systems: Exploiting Channel State Information for Vehicle Communications

Vehicle communication is one of the most important use cases in the fifth generation of wireless networks (5G).\ua0 The growing demand for quality of service (QoS) characterized by performance metrics, such as spectrum efficiency, peak data rate, and outage probability, is mainly limited by inaccurate prediction/estimation of channel state information (CSI) of the rapidly changing environment around moving vehicles. One way to increase the prediction horizon of CSI in order to improve the QoS is deploying predictor antennas (PAs).\ua0 A PA system consists of two sets of antennas typically mounted on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In realistic PA systems, however, the actual benefit is affected by a variety of factors, including spatial mismatch, antenna utilization, temporal correlation of scattering environment, and CSI estimation error. This thesis investigates different resource allocation schemes for the PA systems under practical constraints, with main contributions summarized as follows.First, in Paper A, we study the PA system in the presence of the so-called spatial mismatch problem, i.e., when the channel observed by the PA is not exactly the same as the one experienced by the RA. We derive closed-form expressions for the throughput-optimized rate adaptation, and evaluate the system performance in various temporally-correlated conditions for the scattering environment. Our results indicate that PA-assisted adaptive rate adaptation leads to a considerable performance improvement, compared to the cases with no rate adaptation. Then, to simplify e.g., various integral calculations as well as different operations such as parameter optimization, in Paper B, we propose a semi-linear approximation of the Marcum Q-function, and apply the proposed approximation to the evaluation of the PA system. We also perform deep analysis of the effect of various parameters such as antenna separation as well as CSI estimation error. As we show, our proposed approximation scheme enables us to analyze PA systems with high accuracy.The second part of the thesis focuses on improving the spectral efficiency of the PA system by involving the PA into data transmission. In Paper C, we analyze the outage-limited performance of PA systems using hybrid automatic repeat request (HARQ). With our proposed approach, the PA is used not only for improving the CSI in the retransmissions to the RA, but also for data transmission in the initial round.\ua0 As we show in the analytical and the simulation results, the combination of PA and HARQ protocols makes it possible to improve the spectral efficiency and adapt transmission parameters to mitigate the effect of spatial mismatch

Predictor Antenna Systems: Exploiting Channel State Information for Vehicle Communications

Vehicle communication is one of the most important use cases in the fifth generation of wireless networks (5G). The growing demand for quality of service (QoS) characterized by performance metrics, such as spectrum efficiency, peak data rate, and outage probability, is mainly limited by inaccurate prediction/estimation of channel state information (CSI) of the rapidly changing environment around moving vehicles. One way to increase the prediction horizon of CSI in order to improve the QoS is deploying predictor antennas (PAs). A PA system consists of two sets of antennas typically mounted on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In realistic PA systems, however, the actual benefit is affected by a variety of factors, including spatial mismatch, antenna utilization, temporal correlation of scattering environment, and CSI estimation error. This thesis investigates different resource allocation schemes for the PA systems under practical constraints.Comment: Licentiate thesis, Chalmers University of Technolog

Channel Estimation in Multi-user Massive MIMO Systems by Expectation Propagation based Algorithms

Massive multiple input multiple output (MIMO) technology uses large antenna arrays with tens or hundreds of antennas at the base station (BS) to achieve high spectral efficiency, high diversity, and high capacity. These benefits, however, rely on obtaining accurate channel state information (CSI) at the receiver for both uplink and downlink channels. Traditionally, pilot sequences are transmitted and used at the receiver to estimate the CSI. Since the length of the pilot sequences scale with the number of transmit antennas, for massive MIMO systems downlink channel estimation requires long pilot sequences resulting in reduced spectral efficiency and the so-called pilot contamination due to sharing of the pilots in adjacent cells. In this dissertation we first review the problem of channel estimation in massive MIMO systems. Next, we study the problem of semi-blind channel estimation in the uplink in the case of spatially correlated time-varying channels. The proposed method uses the transmitted data symbols as virtual pilots to enhance channel estimation. An expectation propagation (EP) algorithm is developed to iteratively approximate the joint a posterior distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. The distribution is then used for direct estimation of the channel matrix and detection of the data symbols. A modified version of Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize our algorithm. Simulation results demonstrate that channel estimation error and the symbol error rate of the proposed algorithm improve with the increase in the number of BS antennas or the number of data symbols in the transmitted frame. Moreover, the proposed algorithms can mitigate the effects of pilot contamination as well as time-variations of the channel. Next, we study the problem of downlink channel estimation in multi-user massive MIMO systems. Our approach is based on Bayesian compressive sensing in which the clustered sparse structure of the channel in the angular domain is exploited to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An EP algorithm is developed to approximate the intractable joint distribution on the sparse vector and its support with a distribution from an exponential family. This distribution is then used for direct estimation of the channel. The EP algorithm requires the model parameters which are unknown. We estimate these parameters using the expectation maximization (EM) algorithm. Simulation results show that the proposed combination of EM and EP referred to as EM-EP algorithm outperforms several recently-proposed algorithms in the literature

Predictor Antenna: A Technique to Boost the Performance of Moving Relays

In future wireless systems, a large number of users may access the networks via moving relays (MRs) installed on top of vehicles. One of the main challenges of MRs is rapid channel variation which may make channel estimation, and its following procedures, difficult. To address these issues, various schemes are designed, among which predictor antenna (PA) is a candidate. The PA setup refers to a system with two (sets of) antennas on top of a vehicle, where the PA(s) positioned in front of the vehicle is(are) utilized to predict the channel state information required for data transmission to the receive antennas (RAs) aligned behind the PA. In this paper, we introduce the concept and the potentials of PA systems. Moreover, summarizing the field trials for PAs and the 3GPP attempts on (moving) relays, we compare the performance of different PA and non-PA methods for vehicle communications in both urban and rural areas with the PA setup backhauled through terrestrial or satellite technology, respectively. As we show, with typical parameter settings and vehicle speeds, a single-antenna PA-assisted setup can boost the backhaul throughput of MRs, compared to state-of-the-art open-loop schemes, by up to 50%

Towards Context Information-based High-Performing Connectivity in Internet of Vehicle Communications

Internet-of-vehicles (IoV) is one of the most important use cases in the fifth generation (5G) of wireless networks and beyond. Here, IoV communications refer to two types of scenarios: serving the in-vehicle users with moving relays (MRs); and supporting vehicle-to-everything (V2X) communications for, e.g., connected vehicle functionalities. Both of them can be achieved by transceivers on top of vehicles with growing demand for quality of service (QoS), such as spectrum efficiency, peak data rate, and coverage probability. However, the performance of MRs and V2X is limited by challenges such as the inaccurate prediction/estimation of the channel state information (CSI), beamforming mismatch, and blockages. Knowing the environment and utilizing such context information to assist communication could alleviate these issues. This thesis investigates various context information-based performance enhancement schemes for IoV networks, with main contributions listed as follows.In order to mitigate the channel aging issue, i.e., the CSI becomes inaccurate soon at high speeds, the first part of the thesis focuses on one way to increase the prediction horizon of CSI in MRs: predictor antennas (PAs). A PA system is designed as a system with two sets of antennas on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In PA systems, however, the benefit is affected by a variety of factors. For example, 1) spatial mismatch between the point where the PA estimates the channel and the point where the RA reaches several time slots later, 2) antenna utilization efficiency of the PA, 3) temporal evolution, and 4) estimation error of the PA-base station (BS) channel. First, in Paper A, we study the PA system in the presence of the spatial mismatch problem, and propose an analytical channel model which is used for rate adaptation. In paper B, we propose different approximation schemes for the analytical investigation of PA systems, and study the effect of different parameters on the network performance. Then, involving PAs into data transmission, Paper C and Paper D analyze the outage- and the delay-limited performance of PA systems using hybrid automatic repeat request (HARQ), respectively. As we show in the analytical and the simulation results in Papers C-D, the combination of PA and HARQ protocols makes it possible to improve spectral efficiency and adapt the transmission parameters to mitigate the effect of spatial mismatch. Finally, a review of PA studies in the literature, the challenges and potentials of PA as well as some to-be-solved issues are presented in Paper E.The second part of the thesis focuses on using advanced technologies to further improve the MR/IoV performance. In Paper F, a cooperative PA scheme in IoV networks is proposed to mitigate both the channel aging effect and blockage sensitivity in millimeter-wave channels by collaborative vehicles and BS handover. Then, in Paper G, we study the potentials and challenges of dynamic blockage pre-avoidance in IoV networks