Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems

• Published in: IEEE transactions on signal processing Vol. 66; no. 24; pp. 6390 - 6403
• Main Authors: Qian, Cheng, Fu, Xiao, Sidiropoulos, Nicholas D., Yang, Ye
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Arrays; Channel estimation; Computer simulation; Decomposition; Downlink; Dual polarization (waves); dual-polarized array; Filtration; identifiability; Identification methods; massive MIMO; Mathematical models; MIMO (control systems); MIMO communication; Parameter estimation; Parameter identification; Pilot training; Receiving antennas; Signal processing algorithms; Tensile stress; tensor factorization; Tensors
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2018.2873506

The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good balance between high-capacity communications and parsimonious channel modeling, and also brings limited feedback schemes for downlink channel state information within reach-since such channel can be fully characterized by several key parameters. However, most existing channel estimation work under the DD model has not yet considered DP arrays, perhaps because of the complex array manifold and the resulting difficulty in algorithm design. In this paper, we first reveal that the DD channel with DP arrays at the transmitter and receiver can be naturally modeled as a low-rank tensor, and thus the key parameters of the channel can be effectively estimated via tensor decomposition algorithms. On the theory side, we show that the DD-DP parameters are identifiable under mild conditions, by leveraging identifiability of low-rank tensors. Furthermore, a compressed tensor decomposition algorithm is developed for alleviating the downlink training overhead. We show that, by using judiciously designed pilot structure, the channel parameters are still guaranteed to be identified via the compressed tensor decomposition formulation even when the size of the pilot sequence is much smaller than what is needed for conventional channel identification methods, such as linear least squares and matched filtering. Extensive simulations are employed to showcase the effectiveness of the proposed method.