Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin

Waveforms for the Massive MIMO Downlink: Amplifier Efficiency, Distortion and Performance

In massive MIMO, most precoders result in downlink signals that suffer from high PAR, independently of modulation order and whether single-carrier or OFDM transmission is used. The high PAR lowers the power efficiency of the base station amplifiers. To increase power efficiency, low-PAR precoders have been proposed. In this article, we compare different transmission schemes for massive MIMO in terms of the power consumed by the amplifiers. It is found that (i) OFDM and single-carrier transmission have the same performance over a hardened massive MIMO channel and (ii) when the higher amplifier power efficiency of low-PAR precoding is taken into account, conventional and low-PAR precoders lead to approximately the same power consumption. Since downlink signals with low PAR allow for simpler and cheaper hardware, than signals with high PAR, therefore, the results suggest that low-PAR precoding with either single-carrier or OFDM transmission should be used in a massive MIMO base station

Performance Comparison of Constant Envelope and Zero-forcing Precoders in Multiuser Massive MIMO

In this article, the adoption and performance of a constant envelope (CE) type spatial precoder is addressed in large-scale multiuser MIMO based cellular network. We first formulate an efficient computing solution to obtain the antenna samples of such CE precoder. We then evaluate the achievable CE precoder based multiuser downlink (DL) system performance and compare it with the corresponding performance of more ordinary zero-forcing (ZF) spatial precoder. We specifically also analyze how realistic highly nonlinear power amplifiers (PAs) affect the achievable DL performance, as the individual PA units in large-array or massive MIMO systems are expected to be small, cheap and operating close to saturation for increased energy-efficiency purposes. It is shown that the largely reduced peak-to-average power ratio (PAPR) of the PA input signals in the CE precoder based system allows for pushing the PA units harsher towards saturation, while allowing to reach higher signal-to-interference-plus-noise ratio (SINRs) at the intended receivers compared to the classical ZF precoder based system. The obtained results indicate that the CE precoder can outperform the ZF precoder by up to 5-6 dBs, in terms of the achievable SINRs, when the PA units are pushed towards their saturating region. Such large gains are a substantial benefit when seeking to improve the spectral and energy-efficiencies of the mobile cellular networks.Comment: Accepted for publication at IEEE WCNC 201

Distortion-Aware Linear Precoding for Massive MIMO Downlink Systems with Nonlinear Power Amplifiers

We introduce a framework for linear precoder design over a massive multiple-input multiple-output downlink system in the presence of nonlinear power amplifiers (PAs). By studying the spatial characteristics of the distortion, we demonstrate that conventional linear precoding techniques steer nonlinear distortions towards the users. We show that, by taking into account PA nonlinearity, one can design linear precoders that reduce, and in single-user scenarios, even completely remove the distortion transmitted in the direction of the users. This, however, is achieved at the price of a reduced array gain. To address this issue, we present precoder optimization algorithms that simultaneously take into account the effects of array gain, distortion, multiuser interference, and receiver noise. Specifically, we derive an expression for the achievable sum rate and propose an iterative algorithm that attempts to find the precoding matrix which maximizes this expression. Moreover, using a model for PA power consumption, we propose an algorithm that attempts to find the precoding matrix that minimizes the consumed power for a given minimum achievable sum rate. Our numerical results demonstrate that the proposed distortion-aware precoding techniques provide significant improvements in spectral and energy efficiency compared to conventional linear precoders.Comment: 30 pages, 10 figure

On Out-of-Band Emissions of Quantized Precoding in Massive MU-MIMO-OFDM

We analyze out-of-band (OOB) emissions in the massive multi-user (MU) multiple-input multiple-output (MIMO) downlink. We focus on systems in which the base station (BS) is equipped with low-resolution digital-to-analog converters (DACs) and orthogonal frequency-division multiplexing (OFDM) is used to communicate to the user equipments (UEs) over frequency-selective channels. We demonstrate that analog filtering in combination with simple frequency-domain digital predistortion (DPD) at the BS enables a significant reduction of OOB emissions, but degrades the signal-to-interference-noise-and-distortion ratio (SINDR) at the UEs and increases the peak-to-average power ratio (PAR) at the BS. We use Bussgang's theorem to characterize the tradeoffs between OOB emissions, SINDR, and PAR, and to study the impact of analog filters and DPD on the error-rate performance of the massive MU-MIMO-OFDM downlink. Our results show that by carefully tuning the parameters of the analog filters, one can achieve a significant reduction in OOB emissions with only a moderate degradation of error-rate performance and PAR.Comment: Presented at the 2017 Asilomar Conference on Signals, Systems, and Computers, 6 page

A Spatial Sigma-Delta Approach to Mitigation of Power Amplifier Distortions in Massive MIMO Downlink

In massive multiple-input multiple-output (MIMO) downlink systems, the physical implementation of the base stations (BSs) requires the use of cheap and power-efficient power amplifiers (PAs) to avoid high hardware cost and high power consumption. However, such PAs usually have limited linear amplification ranges. Nonlinear distortions arising from operation beyond the linear amplification ranges can significantly degrade system performance. Existing approaches to handle the nonlinear distortions, such as digital predistortion (DPD), typically require accurate knowledge, or acquisition, of the PA transfer function. In this paper, we present a new concept for mitigation of the PA distortions. Assuming a uniform linear array (ULA) at the BS, the idea is to apply a Sigma-Delta ($\Sigma \Delta$) modulator to spatially shape the PA distortions to the high-angle region. By having the system operating in the low-angle region, the received signals are less affected by the PA distortions. To demonstrate the potential of this spatial $\Sigma \Delta$ approach, we study the application of our approach to the multi-user MIMO-orthogonal frequency division modulation (OFDM) downlink scenario. A symbol-level precoding (SLP) scheme and a zero-forcing (ZF) precoding scheme, with the new design requirement by the spatial $\Sigma \Delta$ approach being taken into account, are developed. Numerical simulations are performed to show the effectiveness of the developed $\Sigma \Delta$ precoding schemes

Massive Multi-Antenna Communications with Low-Resolution Data Converters

Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in future cellular communication systems. In massive MU-MIMO systems, the number of antennas at the base station (BS) is scaled up by several orders of magnitude compared to traditional multi-antenna systems with the goals of enabling large gains in capacity and energy efficiency. However, scaling up the number of active antenna elements at the BS will lead to significant increases in power consumption and system costs unless power-efficient and low-cost hardware components are used. In this thesis, we investigate the performance of massive MU-MIMO systems for the case when the BS is equipped with low-resolution data converters.First, we consider the massive MU-MIMO uplink for the case when the BS uses low-resolution analog-to-digital converters (ADCs) to convert the received signal into the digital domain. Our focus is on the case where neither the transmitter nor the receiver have any a priori channel state information (CSI), which implies that the channel realizations have to be learned through pilot transmission followed by BS-side channel estimation, based on coarsely quantized observations. We derive a low-complexity channel estimator and present lower bounds and closed-form approximations for the information-theoretic rates achievable with the proposed channel estimator together with conventional linear detection algorithms. Second, we consider the massive MU-MIMO downlink for the case when the BS uses low-resolution digital-to-analog converters (DACs) to generate the transmit signal. We derive lower bounds and closed-form approximations for the achievable rates with linear precoding under the assumption that the BS has access to perfect CSI. We also propose novel nonlinear precoding algorithms that are shown to significantly outperform linear precoding for the extreme case of 1-bit DACs. Specifically, for the case of symbol-rate 1-bit DACs and frequency-flat channels, we develop a multitude of nonlinear precoders that trade between performance and complexity. We then extend the most promising nonlinear precoders to the case of oversampling 1-bit DACs and orthogonal frequency-division multiplexing for operation over frequency-selective channels.Third, we extend our analysis to take into account other hardware imperfections such as nonlinear amplifiers and local oscillators with phase noise.The results in this thesis suggest that the resolution of the ADCs and DACs in massive MU-MIMO systems can be reduced significantly compared to what is used in today\u27s state-of-the-art communication systems, without significantly reducing the overall system performance

Optimizing multi-antenna M-MIMO DM communication systems with advanced linearization techniques for RF front-end nonlinearity compensation in a comprehensive design and performance evaluation study

The study presented in this research focuses on linearization strategies for compensating for nonlinearity in RF front ends in multi-antenna M-MIMO OFDM communication systems. The study includes the design and evaluation of techniques such as analogue pre-distortion (APD), crest factor reduction (CFR), multi-antenna clipping noise cancellation (M-CNC), and multi-clipping noise cancellation (MCNC). Nonlinearities in RF front ends can cause signal distortion, leading to reduced system performance. To address this issue, various linearization methods have been proposed. This research examines the impact of antenna correlation on power amplifier efficiency and bit error rate (BER) of transmissions using these methods. Simulation studies conducted under high signal-to-noise ratio (SNR) regimes reveal that M-CNC and MCNC approaches offer significant improvement in BER performance and PA efficiency compared to other techniques. Additionally, the study explores the influence of clipping level and antenna correlation on the effectiveness of these methods. The findings suggest that appropriate linearization strategies should be selected based on factors such as the number of antennas, SNR, and clipping level of the system