Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits

The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain. Recent works in the field of massive multiple-input multiple-output (MIMO) show that the user channels decorrelate when the number of antennas at the base stations (BSs) increases, thus strong signal gains are achievable with little inter-user interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are reasonable in this asymptotic regime. This paper considers a new system model that incorporates general transceiver hardware impairments at both the BSs (equipped with large antenna arrays) and the single-antenna user equipments (UEs). As opposed to the conventional case of ideal hardware, we show that hardware impairments create finite ceilings on the channel estimation accuracy and on the downlink/uplink capacity of each UE. Surprisingly, the capacity is mainly limited by the hardware at the UE, while the impact of impairments in the large-scale arrays vanishes asymptotically and inter-user interference (in particular, pilot contamination) becomes negligible. Furthermore, we prove that the huge degrees of freedom offered by massive MIMO can be used to reduce the transmit power and/or to tolerate larger hardware impairments, which allows for the use of inexpensive and energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/massive-MIMO-hardware-impairment

Two-Timescale Design for RIS-Aided Massive MIMO Systems

The emerging technology, reconfigurable intelligent surface (RIS), could support high data rate while maintaining low costs and energy consumption. Besides, it can constructively reflect the signal from the base station (BS) to users which helps solve the blockage problem in the urban area. Due to these benefits, RIS could be an energy-efficient and cost-effective complement to conventional massive multiple-input multiple-output (MIMO) systems. Focusing on the underload network in far-field outdoor scenarios with fixed users, this thesis investigates the theoretical performance and optimisation design of uplink RIS-aided massive MIMO systems under different detectors and different channel state information (CSI). A novel two-timescale transmission scheme is exploited where the BS detectors and RIS phase shifts are designed based on fast-changing instantaneous CSI and slow-changing statistical CSI, respectively, which achieves a good trade-off between the system performance and the channel estimation overhead. First, this thesis analyses the RIS-aided massive MIMO system with low-complexity maximal-ratio combination (MRC) detectors under the general Rician fading channel model. Closed-form expressions for the achievable rate are derived with blocked and unblocked direct links, based on which the power scaling laws, the rate scaling orders, and the impact of Rician factors are revealed, respectively. A genetic algorithm (GA)-based method is proposed for the design of the RIS phase shifts relying only on the statistical CSI. Simulation results demonstrate the benefit of integrating the RIS into conventional massive MIMO systems. Second, the RIS-aided massive MIMO system is investigated in the presence of the channel estimation error. Following the two-timescale strategy, a low-overhead channel estimation method is proposed to estimate the instantaneous aggregated CSI, whose quality and properties are analysed to shed light on the benefit brought by the RIS. With MRC detectors and the channel estimation results, the achievable rate is derived and a comprehensive framework for the power scaling laws with respect to the number of BS antennas and RIS elements is given. The superiority of the proposed two-timescale scheme over the instantaneous-CSI scheme is validated. Third, the more general scenario in the presence of spatial correlation and electromagnetic interference (EMI) is studied. The channel estimation result is revisited which shows that the RIS could play more roles with spatial correlation. Then, the closed-form expression of the achievable rate is derived and the negative impact of the EMI is analysed. To maximise the minimum user rate, the phase shifts of the RIS are designed based on an accelerated gradient ascent method, which has low computational complexity and relies only on the statistical CSI. Fourth, to solve the severe multi-user interference issue, a zero-forcing (ZF) detector-based design is considered for the RIS-aided massive MIMO system. After tackling the challenging matrix inversion operator, the closed-form ergodic rate expression is derived. Then, the promising properties of introducing ZF detectors into RIS-aided massive MIMO systems are revealed. Fifth and last, the RIS-aided massive MIMO system with ZF detectors and imperfect CSI is analysed. A minimum mean-squared error (MMSE) channel estimator is proposed and analysed. The closed-form expression of the ergodic rate is derived and two insightful upper and lower bounds are proposed, which unveil the rate scaling orders and prove that the considered structure is promising for enhanced mobile broadband, green communications, and the Internet of Things. Besides, both the sum user rate maximisation and the minimum user rate maximisation problems are solved based on the low-complexity majorization-minimisation (MM) algorithms

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure

Intelligent Reflecting Surface Enhanced Wireless Network: Two-timescale Beamforming Optimization

Intelligent reflecting surface (IRS) has drawn a lot of attention recently as a promising new solution to achieve high spectral and energy efficiency for future wireless networks. By utilizing massive low-cost passive reflecting elements, the wireless propagation environment becomes controllable and thus can be made favorable for improving the communication performance. Prior works on IRS mainly rely on the instantaneous channel state information (I-CSI), which, however, is practically difficult to obtain for IRS-associated links due to its passive operation and large number of elements. To overcome this difficulty, we propose in this paper a new two-timescale (TTS) transmission protocol to maximize the achievable average sum-rate for an IRS-aided multiuser system under the general correlated Rician channel model. Specifically, the passive IRS phase-shifts are first optimized based on the statistical CSI (S-CSI) of all links, which varies much slowly as compared to their I-CSI, while the transmit beamforming/precoding vectors at the access point (AP) are then designed to cater to the I-CSI of the users' effective channels with the optimized IRS phase-shifts, thus significantly reducing the channel training overhead and passive beamforming complexity over the existing schemes based on the I-CSI of all channels. For the single-user case, a novel penalty dual decomposition (PDD)-based algorithm is proposed, where the IRS phase-shifts are updated in parallel to reduce the computational time. For the multiuser case, we propose a general TTS optimization algorithm by constructing a quadratic surrogate of the objective function, which cannot be explicitly expressed in closed-form. Simulation results are presented to validate the effectiveness of our proposed algorithms and evaluate the impact of S-CSI and channel correlation on the system performance.Comment: 15 pages, 12 figures, accepted for publication in IEEE Transactions on Wireless Communication