TY - JOUR
T1 - Channel Recovery for UPA-Assisted Massive MIMO Systems With Asymmetrical Uplink and Downlink Transceivers
AU - Yang, Xi
AU - Du, Dahong
AU - Liu, Ting
AU - Zhou, Binggui
AU - Ma, Shaodan
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026/4/1
Y1 - 2026/4/1
N2 - The asymmetrical uplink and downlink transceiver architecture has emerged as a promising solution to reduce hardware cost and complexity in massive multiple-input multiple-output (MIMO) systems, especially under scenarios with dense antenna deployments, such as uniform planar arrays (UPAs). However, accurate full-dimensional channel state information (CSI) recovery becomes more challenging than uniform linear array (ULA) scenarios due to the significantly reduced number of radio frequency (RF) chains. Directly extending the ULA channel recovery method into UPAs will not only result in high computational complexity but also introduce angle estimation ambiguity owing to the extra vertical array dimension. To address these challenges, we propose a channel recovery framework for UPA-assisted massive MIMO systems with asymmetrical transceiver architectures. First, we introduce the concept of the mixed angle to deal with the low elevation angular resolution originating from the compact array form, and a virtual array is then constructed based on the mixed angle via the spatial correlation matrix. After that, an antenna selection algorithm is designed to maximize the virtual array aperture with a minimal number of RF chains, and a low-complexity UPA-based modified Newtonized orthogonal matching pursuit (UPA-based mNOMP) channel recovery algorithm is developed to enable accurate full-dimensional CSI reconstruction. Finally, the imperfect spatial correlation matrix is considered, and an orthogonal rank-one matrix pursuit (OR1MP)-based spatial correlation matrix recovery algorithm is proposed to recover the spatial correlation matrix from its spatially sparse measurements by exploiting the low-rank property of massive MIMO channels. Simulation results validate the superiority of the proposed algorithms in achieving excellent full-dimensional channel recovery performance for asymmetrical transceiver-based massive MIMO systems with UPAs.
AB - The asymmetrical uplink and downlink transceiver architecture has emerged as a promising solution to reduce hardware cost and complexity in massive multiple-input multiple-output (MIMO) systems, especially under scenarios with dense antenna deployments, such as uniform planar arrays (UPAs). However, accurate full-dimensional channel state information (CSI) recovery becomes more challenging than uniform linear array (ULA) scenarios due to the significantly reduced number of radio frequency (RF) chains. Directly extending the ULA channel recovery method into UPAs will not only result in high computational complexity but also introduce angle estimation ambiguity owing to the extra vertical array dimension. To address these challenges, we propose a channel recovery framework for UPA-assisted massive MIMO systems with asymmetrical transceiver architectures. First, we introduce the concept of the mixed angle to deal with the low elevation angular resolution originating from the compact array form, and a virtual array is then constructed based on the mixed angle via the spatial correlation matrix. After that, an antenna selection algorithm is designed to maximize the virtual array aperture with a minimal number of RF chains, and a low-complexity UPA-based modified Newtonized orthogonal matching pursuit (UPA-based mNOMP) channel recovery algorithm is developed to enable accurate full-dimensional CSI reconstruction. Finally, the imperfect spatial correlation matrix is considered, and an orthogonal rank-one matrix pursuit (OR1MP)-based spatial correlation matrix recovery algorithm is proposed to recover the spatial correlation matrix from its spatially sparse measurements by exploiting the low-rank property of massive MIMO channels. Simulation results validate the superiority of the proposed algorithms in achieving excellent full-dimensional channel recovery performance for asymmetrical transceiver-based massive MIMO systems with UPAs.
KW - Antenna selection
KW - asymmetrical transceiver
KW - channel estimation and recovery
KW - mean square error
KW - uniform planar array (UPA)
UR - https://www.scopus.com/pages/publications/105028034083
U2 - 10.1109/JIOT.2025.3650653
DO - 10.1109/JIOT.2025.3650653
M3 - 文章
AN - SCOPUS:105028034083
SN - 2327-4662
VL - 13
SP - 14829
EP - 14843
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 7
ER -