Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems

• Published in: IEEE wireless communications letters Vol. 7; no. 5; pp. 852 - 855
• Main Authors: He, Hengtao, Wen, Chao-Kai, Jin, Shi, Li, Geoffrey Ye
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antenna arrays; beamspace MIMO; Chains; Channel estimation; Computer simulation; Deep learning; Lenses; Message passing; Millimeter wave; Millimeter waves; MIMO (control systems); MIMO communication; neural network; Neural networks; Noise measurement; Noise reduction; Radio frequency
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2018.2832128

Channel estimation is very challenging when the receiver is equipped with a limited number of radio-frequency (RF) chains in beamspace millimeter-wave massive multiple-input and multiple-output systems. To solve this problem, we exploit a learned denoising-based approximate message passing (LDAMP) n...