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Tensor Train Representation of MIMO Channels using the JIRAFE Method

Abstract : MIMO technology has been subject of increasing interest in both academia and industry for future wireless standards. However, its performance benefits strongly depend on the accuracy of the channel at the base station. In a recent work, a fourth-order channel tensor model was proposed for MIMO systems. In this paper, we extend this model by exploiting additional spatial diversity at the receiver, which induces a fifth order tensor model for the channel. For such high orders, there is a crucial need to break the initial high-dimensional optimization problem into a collection of smaller coupled optimization sub-problems. This paper exploits new results on the equivalence between the canonical polyadic decomposition (CPD) and the tensor train (TT) decomposition for the multi-path scenario. Specifically, we propose a Joint dImensionality Reduction And Factor rEtrieval (JIRAFE) method to find the transmit and receive spatial signatures as well as the complex path gains (which also capture the polarization effects). Monte Carlo simulations show that our proposed TT-based representation of the channel is more robust to noise and computationally more attractive than available competing tensor-based methods, for physical parameters estimation.
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Submitted on : Monday, January 13, 2020 - 7:20:56 AM
Last modification on : Thursday, March 24, 2022 - 3:43:42 AM
Long-term archiving on: : Tuesday, April 14, 2020 - 1:10:43 PM


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  • HAL Id : hal-02436367, version 1


Yassine Zniyed, Remy Boyer, André de Almeida, Gérard Favier. Tensor Train Representation of MIMO Channels using the JIRAFE Method. Signal Processing, Elsevier, 2020, 171. ⟨hal-02436367⟩



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