TY - CHAP
T1 - Spatial Sigma-Delta Massive MIMO
T2 - Improved Channel Estimation and Achievable Rates
AU - Rao, Shilpa
AU - Pirzadeh, Hessam
AU - Seco-Granados, Gonzalo
AU - Lee Swindlehurst, A.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Spatial S 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 S 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 S system assuming that a linear receiver based on the LMMSE channel estimate is used to decode the data. Our numerical results demonstrate that S architecture is able to achieve superior channel estimates and sum spectral efficiency compared to conventional low-resolution quantized massive MIMO systems.
AB - Spatial S 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 S 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 S system assuming that a linear receiver based on the LMMSE channel estimate is used to decode the data. Our numerical results demonstrate that S architecture is able to achieve superior channel estimates and sum spectral efficiency compared to conventional low-resolution quantized massive MIMO systems.
UR - http://www.scopus.com/inward/record.url?scp=85107820325&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF51394.2020.9443489
DO - 10.1109/IEEECONF51394.2020.9443489
M3 - Chapter
AN - SCOPUS:85107820325
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 379
EP - 383
BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
A2 - Matthews, Michael B.
ER -