More Like this

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. 

   more » « less
2. Task-Based Quantization for Channel Estimation in RIS Empowered mmWave Systems

   https://doi.org/10.1109/TCOMM.2025.3624163  

   Lee, Gyoseung; Kim, In-Soo; Eldar, Yonina C; Lee_Swindlehurst, A; Lee, Hyeongtaek; Kim, Minje; Choi, Junil (January 2026, IEEE Transactions on Communications)

   In this paper, we investigate channel estimation for reconfigurable intelligent surface (RIS) empowered millimeter-wave (mmWave) multi-user single-input multiple-output communication systems using low-resolution quantization. Due to the high cost and power consumption of analog-to-digital converters (ADCs) in large antenna arrays and for wide signal bandwidths, designing mmWave systems with low-resolution ADCs is beneficial. To tackle this issue, we propose a channel estimation design using task-based quantization that considers the underlying hybrid analog and digital architecture in order to improve the system performance under finite bit-resolution constraints. Our goal is to accomplish a channel estimation task that minimizes the mean squared error distortion between the true and estimated channel. We develop two types of channel estimators: a cascaded channel estimator for an RIS with purely passive elements, and an estimator for the separate RIS-related channels that leverages additional information from a few semi-passive elements at the RIS capable of processing the received signals with radio frequency chains. Numerical results demonstrate that the proposed channel estimation designs exploiting task-based quantization outperform purely digital methods and can effectively approach the performance of a system with unlimited resolution ADCs. Furthermore, the proposed channel estimators are shown to be superior to baselines with small training overhead. 

   more » « less
3. On Optimal MMSE Channel Estimation for One-Bit Quantized MIMO Systems

   https://doi.org/10.1109/TSP.2025.3531779  

   Ding, Minhua; Atzeni, Italo; Tölli, Antti; Swindlehurst, A Lee (January 2025, IEEE Transactions on Signal Processing)

   This paper focuses on the minimum mean squared error (MMSE) channel estimator for multiple-input multiple-output (MIMO) systems with one-bit quantization at the receiver side. Despite its optimality and significance in estimation theory, the MMSE estimator has not been fully investigated in this context due to its general nonlinearity and computational complexity. Instead, the typically suboptimal Bussgang linear MMSE (BLMMSE) channel estimator has been widely adopted. In this work, we develop a new framework to compute the MMSE channel estimator that hinges on the computation of the orthant probability of a multivariate normal distribution. Based on this framework, we determine a necessary and sufficient condition for the BLMMSE channel estimator to be optimal and thus equivalent to the MMSE estimator. Under the assumption of specific channel correlation or pilot symbols, we further utilize the framework to derive analytical expressions for the MMSE estimator that are particularly convenient for the computation when certain system dimensions become large, thereby enabling a comparison between the BLMMSE and MMSE channel estimators in these cases. 

   more » « less
4. 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. 

   more » « less
5. 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. 

   more » « less