Is Channel Estimation Necessary to Select Phase-Shifts for RIS-Assisted Massive MIMO?

Reconfigurable intelligent surfaces (RISs) have attracted great attention as a potential beyond 5G technology. These surfaces consist of many passive elements of metamaterials whose impedance can be controllable to change the characteristics of wireless signals impinging on them. Channel estimation is a critical task when it comes to the control of a large RIS when having a channel with a large number of multipath components. In this paper, we propose novel channel estimation schemes for different RIS-assisted massive multiple-input multiple-output (MIMO) configurations. The proposed methods exploit spatial correlation characteristics at both the base station and the planar RISs, and other statistical characteristics of multi-specular fading in a mobile environment. Moreover, a novel heuristic for phase-shift selection at the RISs is developed. For the RIS-assisted massive MIMO, a new receive combining method and a fixed-point algorithm, which solves the max-min fairness power control optimally, are proposed. Simulation results demonstrate that the proposed uplink RIS-aided framework improves the spectral efficiency of the cell-edge mobile user equipments substantially in comparison to a conventional single-cell massive MIMO system. The impact of several channel effects are studied to gain insight about which RIS configuration is preferable and when the channel estimation is necessary to boost the spectral efficiency.Comment: 30 pages, 9 figures, submitted to IEEE Journa

Hardware Impairments in Large-scale MISO Systems: Energy Efficiency, Estimation, and Capacity Limits

The use of large-scale antenna arrays has the potential to bring substantial improvements in energy efficiency and/or spectral efficiency to future wireless systems, due to the greatly improved spatial beamforming resolution. Recent asymptotic results show that by increasing the number of antennas one can achieve a large array gain and at the same time naturally decorrelate the user channels; thus, the available energy can be focused very accurately at the intended destinations without causing much inter-user interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are still reasonable in the asymptotic regimes. This paper analyzes the fundamental limits of large-scale multiple-input single-output (MISO) communication systems using a generalized system model that accounts for transceiver hardware impairments. As opposed to the case of ideal hardware, we show that these practical impairments create finite ceilings on the estimation accuracy and capacity of large-scale MISO systems. Surprisingly, the performance is only limited by the hardware at the single-antenna user terminal, while the impact of impairments at the large-scale array vanishes asymptotically. Furthermore, we show that an arbitrarily high energy efficiency can be achieved by reducing the power while increasing the number of antennas.Comment: Published at International Conference on Digital Signal Processing (DSP 2013), 6 pages, 5 figure

Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with Imperfect CSI

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest

Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning

This paper considers the joint impact of non-linear hardware impairments at the base station (BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO (multiple-input multiple-output) in practical Rician fading environments. First, Bussgang decomposition-based effective channels and distortion characteristics are analytically derived and the spectral efficiency (SE) achieved by several receivers are explored for third-order non-linearities. Next, two deep feedforward neural networks are designed and trained to estimate the effective channels and the distortion variance at each BS antenna, which are used in signal detection. We compare the performance of the proposed methods with state-of-the-art distortion-aware and -unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise. Using the data generated by the derived effective channels for general order of non-linearities at both the BS and UEs, it is shown that the deep learning-based estimator provides better estimates of the effective channels also for non-linearities more than order three.Comment: 14 pages, 10 figures, to appear in IEEE Open Journal of the Communications Societ

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 Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with Imperfect CSI

National Natural Science Foundation of China (Grant No. 62101128, No. 62201137); Basic Research Project of Jiangsu Provincial Department of Science and Technology (Grant No. BK20210205); U.S. National Science Foundation under Grant CCF1908308

Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with Imperfect CSI

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest

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