Downlink channel estimation for millimeter wave communication combining low-rank and sparse structure characteristics
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Downlink channel estimation for millimeter wave communication combining low-rank and sparse structure characteristics Jin Zhou 1 Received: 11 October 2019 / Accepted: 18 August 2020 # The Author(s) 2020
Abstract The acquisition of channel state information (CSI) is essential in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The mmWave channel exhibits sparse scattering characteristics and a meaningful low-rank structure, which can be simultaneously employed to reduce the complexity of channel estimation. Most existing works recover the low-rank structure of channels using nuclear norm theory. However, solving the nuclear norm-based convex problem often leads to a suboptimal solution of the rank minimization problem, thus degrading the accuracy of channel estimation. Previous contributions recover the channel using over-complete dictionary with the assumption that the mmWave channel can be sparsely represented under some dictionary. While over-complete dictionary may increase the computational complexity. To address these problems, we propose a channel estimation framework based on non-convex low-rank approximation and dictionary learning by exploring the joint low-rank and sparse representations of wireless channels. We surrogate the widely used nuclear norm theory with nonconvex low-rank approximation method and design a dictionary learning algorithm based on channel feature classification employing deep neural network (DNN). Our simulation results reveal the proposed scheme outperform the conventional dictionary learning algorithm, Bayesian framework algorithm, and compressed sensing-based algorithms. Keywords Sparse representation . Non-convex theory . Low-rank approximation . Channel state information . Deep neural network
1 Introduction With the rapid increase of demand for high-speed wireless transmission communication systems, massive multiple input and multiple output systems (MIMO) have attracted extensive attention in academy and industry due to their outstanding ability to improve system capacity and spectrum utilization rate [1, 2]. MIMO technology has been widely used in advanced communication standards, such as IEEE 802.11 ac [3], IEEE 802.16m [4], and 3GPP Long Term Evolution Networks [5, 6]. Owing to the extremely high attenuation and serious signal absorption at the mmWave frequency bands, mmWave communication systems employ large antenna arrays at the base station. Obtaining accurate channel state information is a prerequisite for gaining optimal system performance. In the aspect of CSI detection, Time Division * Jin Zhou [email protected] 1
School of Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222, China
Duplex (TDD) mode takes advantage of the reciprocity of the uplink and downlink link. In Frequency Division Duplex (FDD) mode where the channel reciprocity condition is no longer satisfied, the base station sends a downlink pilot signal, the mobile station receives and detects the pilot signal and then feeds back CSI to the base
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