Robust CSI Estimation Under Complex Communication Environment

Zhang, Haipei; Xue, Jiang; Meng, Deyu; Zhao, Qian; Zhao, Xu

doi:10.1109/icc.2019.8761122

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…The authors of [17] model temporal channel variations as GMMs, e.g., in order to predict channel states. The authors of [18] propose to improve channel estimation techniques by using GMMs as a better characterization of the noise in a communications environments than it is given by the additive white Gaussian noise model. In [19], a GMM prior is used for the unknown data symbols in semi-blind channel estimation.…”

Section: B Gaussian Mixture Models In Channel Estimation Literaturementioning

confidence: 99%

An Asymptotically Optimal Approximation of the Conditional Mean Channel Estimator based on Gaussian Mixture Models

Koller¹,

Benedikt²,

Turan³

et al. 2021

Preprint

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This paper investigates a channel estimator based on Gaussian mixture models (GMMs) in the context of linear inverse problems with additive Gaussian noise. We fit a GMM to given channel samples to obtain an analytic probability density function (PDF) which approximates the true channel PDF. Then, a conditional mean estimator (CME) corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal CME based on the true channel PDF. This optimal CME cannot be calculated analytically because the true channel PDF is generally unknown. We present mild conditions which allow us to prove the convergence of the GMM-based CME to the optimal CME as the number of GMM components is increased. Additionally, we investigate the estimator's computational complexity and present simplifications based on common model-based insights. Further, we study the estimator's behavior in numerical experiments including multiple-input multiple-output (MIMO) and wideband systems.

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Section: B Gaussian Mixture Models In Channel Estimation Literaturementioning

confidence: 99%