Learning the MMSE Channel Estimator

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Authors David Neumann, Thomas Wiese, W. Utschick
Journal/Conference Name IEEE Transactions on Signal Processing
Paper Category
Paper Abstract We present a method for estimating conditionally Gaussian random vectors with random covariance matrices, which uses techniques from the field of machine learning. Such models are typical in communication systems, where the covariance matrix of the channel vector depends on random parameters, e.g., angles of propagation paths. If the covariance matrices exhibit certain Toeplitz and shift-invariance structures, the complexity of the minimum mean squared error (MMSE) channel estimator can be reduced to <inline-formula><tex-math notation="LaTeX">${\mathcal O}(M\log M)$ </tex-math></inline-formula> floating point operations, where <inline-formula><tex-math notation="LaTeX">$M$</tex-math> </inline-formula> is the channel dimension. While in the absence of structure the complexity is much higher, we obtain a similarly efficient (but suboptimal) estimator by using the MMSE estimator of the structured model as a blueprint for the architecture of a neural network. This network learns the MMSE estimator for the unstructured model, but only within the given class of estimators that contains the MMSE estimator for the structured model. Numerical simulations with typical spatial channel models demonstrate the generalization properties of the chosen class of estimators to realistic channel models.
Date of publication 2018
Code Programming Language Julia

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