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1. 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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2. SPADE: Sparsity-Adaptive Equalization for MMwave Massive MU-MIMO

   https://doi.org/10.1109/SSP49050.2021.9513825  

   Mirfarshbafan, Seyed Hadi; Studer, Christoph (July 2021, IEEE Statistical Signal Processing Workshop (SSP))

   We propose SParsity-ADaptive Equalization (SPADE), a novel approach to reduce the effective number of multiplications in sparse inner products by adaptively skipping multiplications that have little to no effect on the result. We apply SPADE to beamspace linear minimum mean square error (LMMSE) spatial equalization in all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-output (MU-MIMO) systems. We propose a SPADE-based architecture that mutes insignificant multiplications to offer power savings. We use simulation results with line-of-sight (LoS) and non-LoS mmWave channel models to demonstrate that SPADE-LMMSE performs on par with state-of-the-art beamspace equalizers in terms of bit error-rate, while requiring significantly lower preprocessing complexity. 

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3. Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems

   https://doi.org/10.1109/TSP.2024.3429009  

   Nguyen, Ly V; Swindlehurst, A Lee; Nguyen, Duy_H N (July 2024, IEEE Transactions on Signal Processing)

   Massive multiple-input multiple-output (MIMO) communications using low-resolution analog-to-digital converters (ADCs) is a promising technology for providing high spectral and energy efficiency with affordable hardware cost and power consumption. However, the use of low-resolution ADCs requires special signal processing methods for channel estimation and data detection since the resulting system is severely non-linear. This paper proposes joint channel estimation and data detection methods for massive MIMO systems with low-resolution ADCs based on the variational Bayes (VB) inference framework. We first derive matched-filter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Then we extend these methods to the joint channel estimation and data detection (JED) problem and propose two methods we refer to as MF-QVB-JED and LMMSE-QVB-JED. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to float the elements of the noise covariance matrix as unknown random variables that are used to account for both the noise and the residual inter-user interference. We also present practical aspects of the QVB framework to improve its implementation stability. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods. 

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4. Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems

   https://doi.org/10.1109/SPAWC48557.2020.9154213  

   Gallyas-Sanhueza, Alexandra; Mirfarshbafan, Seyed Hadi; Ghods, Ramina; Studer, Christoph (May 2020, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   We propose sparsity-adaptive beamspace channel estimation algorithms that improve accuracy for 1-bit data converters in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. Our algorithms include a tuning stage based on Stein's unbiased risk estimate (SURE) that automatically selects optimal denoising parameters depending on the instantaneous channel conditions. Simulation results with line-of-sight (LoS) and non-LoS mmWave massive MIMO channel models show that our algorithms improve channel estimation accuracy with 1-bit measurements in a computationally-efficient manner. 

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5. Mitigation of Jamming Attack in Massive MIMO With One-Bit FBB Sigma-Delta ADCs

   https://doi.org/10.1109/IEEECONF44664.2019.9048666  

   Pirzadeh, Hessam; Seco-Granados, Gonzalo; Swindlehurst, A. Lee (November 2019, Proc. 53rd Asilomar Conference on Signals, Systems, and Computers)

   We study the uplink performance of a massive multiple-input multiple-output (MIMO) system with one-bit analog to digital converters (ADCs) in the presence of a disruptive jammer. We propose spatial Sigma-Delta (ΣΔ) quantization with an interference cancellation feedback beamformer (FBB ΣΔ) to mitigate the adverse impact of the jammer on the system performance. Then we analyze the performance of this architecture by adopting an appropriate linear model and present a recursive algorithm to calculate the power of the quantization noise. Simulation results show that the spatial FBB ΣΔ architecture can achieve the same symbol error rate as in systems with high-resolution ADCs. 

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