Channel Estimation for Movable-Antenna MIMO Systems via Tensor Decomposition

In this letter, we investigate the channel estimation problem for MIMO wireless communication systems with movable antennas (MAs) at both the transmitter (Tx) and receiver (Rx). To achieve high channel estimation accuracy with low pilot training overhead, we propose a tensor decomposition-based method for estimating the parameters of multi-path channel components, thereby reconstructing the wireless channel between any pair of Tx and Rx MA positions in the Tx and Rx regions. First, we introduce a two-stage Tx-Rx successive antenna movement pattern for pilot training. Then, we obtain the factor matrices of the tensor via the canonical polyadic decomposition, and thereby estimate the angle/gain parameters for enabling the channel reconstruction between arbitrary Tx/Rx MA positions. In addition, we analyze the uniqueness condition of the tensor decomposition, which ensures the complete channel reconstruction between the whole Tx and Rx regions based on the channel measurements at only a finite number of Tx/Rx MA positions. Finally, simulation results are presented to evaluate the proposed tensor decomposition-based method as compared to existing methods, in terms of channel estimation accuracy and pilot overhead.

I. Introduction

By exploiting the spatial degrees of freedom (DoFs) with multiple antennas, multiple-input multiple-output (MIMO) technology has been significantly advanced over decades to enhance the performance of generations of wireless networks. However, due to the fixed geometry of conventional antenna arrays, MIMO systems cannot fully exploit the wireless channel spatial variation/DoFs, even for massive MIMO with a large number of fixed-position antennas [1], [2].