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Dealing with nonlinear effects of the radio-frequency (RF) chain is a key issue in the realization of very large-scale multi-antenna (MIMO) systems. Achieving the remarkable gains possible with massive MIMO requires that the signal processing algorithms systematically take into account these effects. Here, we present a computationally-efficient linear precoding method satisfying the requirements for low peak-to-average power ratio (PAPR) and low-resolution D/Aconverters (DACs). The method is based on a sparse regularization of the precoding matrix and offers advantages in terms of precoded signal PAPR as well as processing complexity. Through simulation, we find that the method substantially improves conventional linear precoders. 

The one-bit spatial Sigma-Delta concept has recently been proposed as an approach for achieving low distortion and low power consumption for massive multi-input multi-output systems. The approach exploits users located in known angular sectors or spatial oversampling to shape the quantization noise away from desired directions of arrival. While reducing the antenna spacing alleviates the adverse impact of quantization noise, it can potentially deteriorate the performance of the massive array due to excessive mutual coupling. In this paper, we analyze the impact of mutual coupling on the uplink spectral efficiency of a spatial one-bit Sigma-Delta massive MIMO architecture, and compare the resulting performance degradation to standard one-bit quantization as well as the ideal case with infinite precision. Our simulations show that the one-bit Sigma-Delta array is particularly advantageous in space-constrained scenarios, can still provide significant gains even in the presence of mutual coupling when the antennas are closely spaced. 

The uplink performance of a mixed analog-to-digital converter (ADC) massive multiple-input multiple-output (MIMO) architecture with a space-constrained array at the base station (BS) is analyzed. We investigate the effect of spatial correlation and mutual coupling on the spectral efficiency (SE) of the system. First, we analyze to what extent adding a small number of high-resolution ADCs can impact the channel estimation accuracy. Then, we derive a closed-form approximation for the SE. Our analysis demonstrates how a space constraint on a uniform linear array (ULA) can affect the design of a massive MIMO system with low-resolution ADCs. It is shown that by equally spacing a small number of high-resolution ADCs over the array, one can dramatically reduce the performance gap between a system with all low-resolution and all high-resolution ADCs. 

For 5G it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omni- directional coverage with a similar effective antenna aperture compared to state-of-the-art cellular systems, an antenna array is required at both the mobile and basestation. Due to the large bandwidth and inefficient amplifiers available in CMOS for mmWave, the analog front-end of the receiver with a large number of antennas becomes especially power hungry. Two main solutions exist to reduce the power consumption: hybrid beam forming and digital beam forming with low resolution Analog to Digital Converters (ADCs). In this work we compare the spectral and energy efficiency of both systems under practical system constraints. We consider the effects of channel estimation, transmitter impairments and multiple simultaneous users for a wideband multipath model. Our power consumption model considers components reported in literature at 60 GHz. In contrast to many other works we also consider the correlation of the quantization error, and generalize the modeling of it to non- uniform quantizers and different quantizers at each antenna. The result shows that as the Signal to Noise Ratio (SNR) gets larger the ADC resolution achieving the optimal energy efficiency gets also larger. The energy efficiency peaks for 5 bit resolution at high SNR, since due to other limiting factors the achievable rate almost saturates at this resolution. We also show that in the multi- user scenario digital beamforming is in any case more energy efficient than hybrid beamforming. In addition we show that if mixed ADC resolutions are used we can achieve any desired trade-off between power consumption and rate close to those achieved with only one ADC resolution.