Sparse Channel Estimation in Nonlinear MIMO with Magnitude/Phase Measurements

• Main Authors: He, Mengxia, Wang, Shengchu
• Format: Conference Proceeding
• Language: English
• Subjects: Channel estimation; Detectors; MIMO communication; Phase measurement; Probability density function; Receivers; Training
• ISSN: 2166-9589
• DOI: 10.1109/PIMRC48278.2020.9217232

The spatio-temporal sparsity (STS) is exploited to improve the channel estimation (CE) for nonlinear multiple input multiple output (NL-MIMO), whose base station (BS) acquires only phases-or-magnitudes of the received complex signals through low-power and low-cost phase/envelope detectors. The sparse CE problem is formulated as the generalized linear mixing one and resolved by a modified generalized vector approximate message passing (GVAMP) algorithm with expectation maximization (EM) mechanism. The prior distribution of sparse channel is modeled as Bernoulli Gaussian-mixture (BGM). Consequently, NL-MIMO channel responses and unknown parameters including BGM parameters and noise variance are updated by the GVAMP and EM procedure alternatingly. A gradient descent (GD) method is proposed for EM update of noise variance in NL-MIMO, where closed-form solution is missed due to the complex formulas of likelihood under phase/magnitude observations. Monte Carlo integration technique is exploited to numerically derive the gra-dient of the noise variance under phase/magnitude observations. Simulation results show that the transmission power and pilot length are significantly saved after exploiting the STS, and both the EM-GVAMP and GD estimators are validated.