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Low-resolution analog-to-digital converters (ADCs) simplify the design of millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) basestations, but increase vulnerability to jamming attacks. As a remedy, we propose HERMIT (short for Hybrid jammER MITigation), a method that combines a hardware-friendly adaptive analog transform with a corresponding digital equalizer: The analog transform removes most of the jammer’s energy prior to data conversion; the digital equalizer suppresses jammer residues while detecting the legitimate transmit data. We provide theoretical results that establish the optimal analog transform as a function of the user equipments’ and the jammer’s channels. Using simulations with mmWave channel models, we demonstrate the superiority of HERMIT compared both to purely digital jammer mitigation as well as to a recent hybrid method that mitigates jammer interference with a nonadaptive analog transform. 

All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions. For all-digital architectures to be competitive with hybrid solutions in terms of power consumption, novel signal-processing methods and baseband architectures are necessary. In this paper, we demonstrate that adapting the resolution of the analog-to-digital converters (ADCs) and spatial equalizer of an all-digital system to the communication scenario (e.g., the number of users, modulation scheme, and propagation conditions) enables orders-of-magnitude power savings for realistic mmWave channels. For example, for a 256-BS-antenna 16-user system supporting 1 GHz bandwidth, a traditional baseline architecture designed for a 64-user worst-case scenario would consume 23 W in 28 nm CMOS for the ADC array and the spatial equalizer, whereas a resolution-adaptive architecture is able to reduce the power consumption by 6.7×. 

Next generation wireless communication systems are expected to combine millimeter-wave communication with massive multi-user multiple-input multiple-output technology. All-digital base-station implementations for such systems need to process high-dimensional data at extremely high rates, which results in excessively high power consumption. In this paper, we propose two-stage spatial equalizers that first reduce the problem dimension by means of a hardware-friendly, low-resolution linear transform followed by spatial equalization on a lower-dimensional signal. We consider adaptive and non-adaptive dimensionality reduction strategies and demonstrate that the proposed two-stage spatial equalizers are able to approach the performance of conventional linear spatial equalizers that directly operate on high-dimensional data, while offering the potential to reduce the power consumption of spatial equalization. 

Power consumption of multiuser (MU) precoding is a major concern in all-digital massive MU multiple-input multiple-output (MIMO) basestations with hundreds of antenna elements operating at millimeter-wave (mmWave) frequencies. We propose to replace part of the linear Wiener filter (WF) precoding matrix by a Finite-Alphabet WF Precoding (FAWP) matrix, which enables the use of low-precision hardware that consumes low power and area. To minimize the performance loss of our approach, we present methods that efficiently compute mean-square error (MSE)-optimal FAWP matrices. Our results show that FAWP matrices are able to approach infinite-precision error-rate and error vector magnitude performance with only 3-bit precoding weights, even when operating under realistic mmWave propagation conditions. Hence, FAWP is a promising approach to substantially reduce power consumption and silicon area in all-digital mmWave massive MU-MIMO systems.