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1. Beam-Slicing for Jammer Mitigation in mmWave Massive MU-MIMO

   https://doi.org/10.1109/SiPS52927.2021.00039  

   Castaneda, Oscar; Marti, Gian; Studer, Christoph (October 2021, IEEE Workshop on Signal Processing Systems (SiPS))

   Millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) technology promises unprecedentedly high data rates for next-generation wireless systems. To be practically viable, mmWave massive MU-MIMO basestations (BS) must (i) rely on low-resolution data-conversion and (ii) be robust to jammer interference. This paper considers the problem of mitigating the impact of a permanently transmitting jammer during uplink transmission to a BS equipped with low-resolution analog-to-digital converters (ADCs). To this end, we propose SNIPS, short for Soft-Nulling of Interferers with Partitions in Space. SNIPS combines beam-slicing—a localized, analog spatial transform that focuses the jammer energy onto a subset of all ADCs—together with a soft-nulling data detector that exploits knowledge of which ADCs are contaminated by jammer interference. Our numerical results show that SNIPS is able to successfully serve 65% of the user equipments (UEs) for scenarios in which a conventional antenna-domain soft-nulling data detector is only able to serve 2% of the UEs. 

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2. High Dynamic Range mmWave Massive MU-MIMO with Householder Reflections

   V. Palhares; G. Marti; O. Castañeda; C. Studer (October 2023, Asilomar Conference on Signals, Systems, and Computers)

   All-digital massive multiuser (MU) multiple-input multiple-output (MIMO) at millimeter-wave (mmWave) frequencies is a promising technology for next-generation wireless systems. Low-resolution analog-to-digital converters (ADCs) can be utilized to reduce the power consumption of all-digital basestation (BS) designs. However, simultaneously transmitting user equipments (UEs) with vastly different BS-side receive powers either drown weak UEs in quantization noise or saturate the ADCs. To address this issue, we propose high dynamic range (HDR) MIMO, a new paradigm that enables simultaneous reception of strong and weak UEs with low-resolution ADCs. HDR MIMO combines an adaptive analog spatial transform with digital equalization: The spatial transform focuses strong UEs on a subset of ADCs in order to mitigate quantization and saturation artifacts; digital equalization is then used for data detection. We demonstrate the efficacy of HDR MIMO in a massive MU-MIMO mmWave scenario that uses Householder reflections as spatial transform. 

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3. Massive MIMO Channel Estimation with 1-Bit Spatial Sigma-delta ADCs

   https://doi.org/10.1109/ICASSP.2019.8682842  

   Rao, Shilpa; Swindlehurst, A. Lee; Pirzadeh, Hessam (May 2019, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   We consider channel estimation for an uplink massive multiple input multiple output (MIMO) system where the base station (BS) uses a first-order spatial Sigma-Delta (Σ△) analog-to-digital converter (ADC) array. The Σ△ array consists of closely spaced sensors which oversample the received signal and provide a coarsely quantized (1-bit) output. We develop a linear minimum mean squared error (LMMSE) estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. The performance of the proposed Σ△ LMMSE estimator is compared via simulation to channel estimation using standard 1-bit quantization and also infinite resolution ADCs. 

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4. Space-Constrained Mixed-ADC Massive MIMO

   https://doi.org/10.1109/SPAWC.2019.8815459  

   Pirzadeh, Hessam; Swindlehurst, A. Lee; Nossek, Josef A. (July 2019, Proc. IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   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. 

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5. The Impact of SAR-ADC Mismatch on Quantized Massive MU-MIMO Systems

   J. Guichemerre; C. Studer (October 2023, Conference record Asilomar Conference on Signals Systems Computers)

   Low-resolution analog-to-digital converters (ADCs) in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems can significantly reduce the power, cost, and interconnect data rates of infrastructure basestations. Thus, recent research on the theory and algorithm sides has extensively focused on such architectures, but with idealistic quantization models. However, real-world ADCs do not behave like ideal quantizers, and are affected by fabrication mismatches. We analyze the impact of capacitor-array mismatches in successive approximation register (SAR) ADCs, which are widely used in wireless systems. We use Bussgang's decomposition to model the effects of such mismatches, and we analyze their impact on the performance of a single ADC. We then simulate a massive MU-MIMO system to demonstrate that capacitor mismatches should not be ignored, even in basestations that use low-resolution SAR ADCs. 

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