Deep Learning for Geometrically-Consistent Angular Power Spread Function Estimation in Massive MIMO

• Published in: IEEE Global Communications Conference (Online) pp. 1 - 6
• Main Authors: Song, Yi, Khalilsarai, Mahdi Barzegar, Haghighatshoar, Saeid, Caire, Giuseppe
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2020
• Subjects: angular power spread function (APSF); Channel estimation; Covariance matrices; Deep Neural Networks (DNNs); Estimation; geometrical consistency; group-sparsity; Massive MIMO; Noise measurement; Precoding; Scattering; specular and diffuse scattering
• ISSN: 2576-6813
• DOI: 10.1109/GLOBECOM42002.2020.9322427

In spatial channel models used in multi-antenna wireless communications, the propagation from a single-antenna transmitter (e.g. a user) to an M-antenna receiver (e.g. a Base Station) occurs through scattering clusters located in the far field of the receiving array. The angular power spread functio...