This code was used to generate the figures in the article
D. Neumann, T. Wiese, and W. Utschick, Learning the MMSE Channel Estimator, Accepted for publication in IEEE Transactions on Signal Processing, 2018.
We present a method for estimating 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 MMSE channel estimator can be reduced to O(M log M) floating point operations, where M 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.
This code is written in Julia (http://julialang.org) and uses Julia's TensorFlow package, which automatically installs TensorFlow (http://tensorflow.org). The code was tested with Julia version 0.6, TensorFlow version 1.4.1, and version 0.8 of the Julia package TensorFlow.
This code is licensed under 3-clause BSD License:
Copyright (c) 2018 D. Neumann and T. Wiese
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