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Radio Techniques Incorporating Sparse Modeling
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2021/03/01
Online ISSN: 1745-1337
Type of Manuscript: INVITED SURVEY PAPER
Category: Digital Signal Processing
sparse modeling, compressed sensing, sparse Bayesian learning, DOA estimation, channel estimation,
Full Text: FreePDF(1.9MB)
Sparse modeling is one of the most active research areas in engineering and science. The technique provides solutions from far fewer samples exploiting sparsity, that is, the majority of the data are zero. This paper reviews sparse modeling in radio techniques. The first half of this paper introduces direction-of-arrival (DOA) estimation from signals received by multiple antennas. The estimation is carried out using compressed sensing, an effective tool for the sparse modeling, which produces solutions to an underdetermined linear system with a sparse regularization term. The DOA estimation performance is compared among three compressed sensing algorithms. The second half reviews channel state information (CSI) acquisitions in multiple-input multiple-output (MIMO) systems. In time-varying environments, CSI estimated with pilot symbols may be outdated at the actual transmission time. We describe CSI prediction based on sparse DOA estimation, and show excellent precoding performance when using the CSI prediction. The other topic in the second half is sparse Bayesian learning (SBL)-based channel estimation. A base station (BS) has many antennas in a massive MIMO system. A major obstacle for using the massive MIMO system in frequency-division duplex mode is an overhead for downlink CSI acquisition because we need to send many pilot symbols from the BS and to get the feedback from user equipment. An SBL-based channel estimation method can mitigate this issue. In this paper, we describe the outline of the method, and show that the technique can reduce the downlink pilot symbols.