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1. Doubly 1-Bit Quantized Massive MIMO

   https://doi.org/10.1109/IEEECONF59524.2023.10476782  

   Atzeni, Italo; Tölli, Antti; Nguyen, Duy_H N; Swindlehurst, A Lee (October 2023, IEEE)

   Enabling communications in the (sub-)THz band will call for massive multiple-input multiple-output (MIMO) arrays at either the transmit- or receive-side, or at both. To scale down the complexity and power consumption when operating across massive frequency and antenna dimensions, a sacrifice in the resolution of the digital-to-analog/analog-to-digital converters (DACs/ADCs) will be inevitable. In this paper, we analyze the extreme scenario where both the transmit- and receive-side are equipped with fully digital massive MIMO arrays and 1-bit DACs/ADCs, which leads to a system with minimum radio-frequency complexity, cost, and power consumption. Building upon the Bussgang decomposition, we derive a tractable approximation of the mean squared error (MSE) between the transmitted data symbols and their soft estimates. Numerical results show that, despite its simplicity, a doubly 1-bit quantized massive MIMO system with very large antenna arrays can deliver an impressive performance in terms of MSE and symbol error rate. 

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2. Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems

   https://doi.org/10.1109/IEEECONF44664.2019.9048799  

   Castaneda, Oscar; Jacobsson, Sven; Durisi, Giuseppe; Goldstein, Tom; Studer, Christoph (November 2019, Asilomar Conference on Signals, Systems, and Computers)

   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. 

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3. 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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4. Spatial Sigma-Delta Massive MIMO: Improved Channel Estimation and Achievable Rates

   https://doi.org/10.1109/IEEECONF51394.2020.9443489  

   Rao, Shilpa; Pirzadeh, Hessam; Seco-Granados, Gonzalo; Lee Swindlehurst, A. (November 2020, 2020 54th Asilomar Conference on Signals, Systems, and Computers)

   null (Ed.)

   Spatial ΣΔ sampling has recently been proposed to improve the performance of massive MIMO systems with low-resolution quantization for cases where the users are confined to a certain angular sector, or the array is spatially oversampled. We derive a linear minimum mean squared error (LMMSE) channel estimator for the ΣΔ array based on an element-wise Bussgang decomposition that reformulates the nonlinear quantizer operation using an equivalent linear model plus quantization noise. Both the case of one- and two-bit quantization is considered. We then evaluate the achievable rate of the ΣΔ system assuming that a linear receiver based on the LMMSE channel estimate is used to decode the data. Our numerical results demonstrate that ΣΔ architecture is able to achieve superior channel estimates and sum spectral efficiency compared to conventional low-resolution quantized massive MIMO systems. 

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5. DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs

   https://doi.org/10.1109/ICC42927.2021.9501054  

   Nguyen, Ly V.; Nguyen, Duy H.; Swindlehurst, A. Lee (June 2021, 2021 - IEEE International Conference on Communications)

   null (Ed.)

   Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods. 

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