Uplink Performance of Wideband Massive MIMO With One-Bit ADCs

Analog-to-digital converters (ADCs) stand for a significant part of the total power consumption in a massive multiple-input multiple-output (MIMO) base station. One-bit ADCs are one way to reduce power consumption. This paper presents an analysis of the spectral efficiency of single-carrier and orthogonal-frequency-division-multiplexing (OFDM) transmission in massive MIMO systems that use one-bit ADCs. A closed-form achievable rate, i.e., a lower bound on capacity, is derived for a wideband system with a large number of channel taps that employ low-complexity linear channel estimation and symbol detection. Quantization results in two types of error in the symbol detection. The circularly symmetric error becomes Gaussian in massive MIMO and vanishes as the number of antennas grows. The amplitude distortion, which severely degrades the performance of OFDM, is caused by variations between symbol durations in received interference energy. As the number of channel taps grows, the amplitude distortion vanishes and OFDM has the same performance as single-carrier transmission. A main conclusion of this paper is that wideband massive MIMO systems work well with one-bit ADCs. Analog-to-digital converters (ADCs) stand for a significant part of the total power consumption in a massive multiple-input multiple-output (MIMO) base station. One-bit ADCs are one way to reduce power consumption. This paper presents an analysis of the spectral efficiency of single-carrier and orthogonal-frequency-division-multiplexing (OFDM) transmission in massive MIMO systems that use one-bit ADCs. A closed-form achievable rate, i.e., a lower bound on capacity, is derived for a wideband system with a large number of channel taps that employ low-complexity linear channel estimation and symbol detection. Quantization results in two types of error in the symbol detection. The circularly symmetric error becomes Gaussian in massive MIMO and vanishes as the number of antennas grows. The amplitude distortion, which severely degrades the performance of OFDM, is caused by variations between symbol durations in received interference energy. As the number of channel taps grows, the amplitude distortion vanishes and OFDM has the same performance as single-carrier transmission. A main conclusion of this paper is that wideband massive MIMO systems work well with one-bit ADCs.115520Ysciescopu

AdaBoost-Based Efficient Channel Estimation and Data Detection in One-Bit Massive MIMO

The use of one-bit analog-to-digital converter (ADC) has been considered as a viable alternative to high resolution counterparts in realizing and commercializing massive multiple-input multiple-output (MIMO) systems. However, the issue of discarding the amplitude information by one-bit quantizers has to be compensated. Thus, carefully tailored methods need to be developed for one-bit channel estimation and data detection as the conventional ones cannot be used. To address these issues, the problems of one-bit channel estimation and data detection for MIMO orthogonal frequency division multiplexing (OFDM) system that operates over uncorrelated frequency selective channels are investigated here. We first develop channel estimators that exploit Gaussian discriminant analysis (GDA) classifier and approximated versions of it as the so-called weak classifiers in an adaptive boosting (AdaBoost) approach. Particularly, the combination of the approximated GDA classifiers with AdaBoost offers the benefit of scalability with the linear order of computations, which is critical in massive MIMO-OFDM systems. We then take advantage of the same idea for proposing the data detectors. Numerical results validate the efficiency of the proposed channel estimators and data detectors compared to other methods. They show comparable/better performance to that of the state-of-the-art methods, but require dramatically lower computational complexities and run times

Massive MIMO Systems with Hardware Imperfections

Recent years have witnessed an unprecedented explosion in mobile data traffic, due to the expansion of numerous types of wireless devices. Moreover, each device needs a high throughput to support demanding applications such as real-time video, movie streaming and games. Thus, future wireless systems have to satisfy three main requirements: 1) having a high throughput; 2) simultaneously serving many users; and 3) less energy consumption. Massive multiple-input multiple-output (MIMO) systems meet the aforementioned requirements and is nowadays a well-established technology which forms the backbone of the fifth-generation (5G) cellular communication systems.\ua0However, massive MIMO systems, i.e. employing hundreds or even thousands of antennas, will be a viable solution in the future only if low-cost and energy-efficient hardware is deployed. Unfortunately, low-cost, low-quality hardware is prone to hardware impairments such as in-phase and quadrature imbalance (IQI) and phase noise.\ua0\ua0Moreover, one of the dominant sources of power consumption in massive MIMO systems are the data converters at the BS. The baseband signal at each radio-frequency (RF) chain is generated by a pair of analog-to-digital converters (ADCs). The power consumption of these ADCs increases exponentially with the resolution (in bits) and linearly with the bandwidth. In conventional multi-antenna systems, each RF port is connected to a pair of high-resolution ADCs (e.g., 10-bit or more). For massive MIMO systems this would lead to prohibitively high-power consumption due to the large number of required ADCs. Hence, the ADC resolution must be limited to keep the power budget within tolerable levels.In this thesis, we investigate the performance of massive MIMO systems in non-ideal hardware. We begin with by studying the impact of IQI on massive MIMO systems. Important insights are gained through the analysis of system performance indicators, such as channel estimation and achievable rates by deriving tractable approximations of the ergodic spectral efficiency.First, a novel pilot-based joint estimator of the uplink augmented MIMO channel matrix and receiver IQI coefficients is described and then, a low-complexity IQI compensation scheme is proposed which is based on the receiver IQI coefficients\u27 estimation and it is independent of the channel gain.\ua0Second, we investigate the impact of the transceiver IQI in massive MIMO considering a time division duplexing (TDD) system where we assume uplink/downlink channel reciprocity in the downlink precoding design. The uplink channel estimation accuracy and the achievable downlink rate of the regularized zero-forcing (RZF) and maximum ratio transmission (MRT) is studied when there is mismatch between the uplink and downlink channels.\ua0Finally, we analyze the quantization distortion in limited-precision ADCs in uplink massive MIMO systems whose channel state information (CSI) is not known a priori to transmitter and receiver. We show that even a small percentage of clipped samples at the receiver can downgrade considerably the systems performance and propose near-optimal low-complexity solutions to reconstruct the clipped signal

Spectral Efficiency of Mixed-ADC Massive MIMO

We study the spectral efficiency (SE) of a mixed-ADC massive MIMO system in which K single-antenna users communicate with a base station (BS) equipped with M antennas connected to N high-resolution ADCs and M-N one-bit ADCs. This architecture has been proposed as an approach for realizing massive MIMO systems with reasonable power consumption. First, we investigate the effectiveness of mixed-ADC architectures in overcoming the channel estimation error caused by coarse quantization. For the channel estimation phase, we study to what extent one can combat the SE loss by exploiting just N << M pairs of high-resolution ADCs. We extend the round-robin training scheme for mixed-ADC systems to include both high-resolution and one-bit quantized observations. Then, we analyze the impact of the resulting channel estimation error in the data detection phase. We consider random high-resolution ADC assignment and also analyze a simple antenna selection scheme to increase the SE. Analytical expressions are derived for the SE for maximum ratio combining (MRC) and numerical results are presented for zero-forcing (ZF) detection. Performance comparisons are made against systems with uniform ADC resolution and against mixed-ADC systems without round-robin training to illustrate under what conditions each approach provides the greatest benefit.Comment: To appear in IEEE Transactions on Signal Processin

On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems

Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is a favorable candidate for the fifth generation (5G) cellular systems. However, a key challenge is the high power consumption imposed by its numerous radio frequency (RF) chains, which may be mitigated by opting for low-resolution analog-to-digital converters (ADCs), whilst tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving the overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin

SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO systems

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones