Abstract
The conventional Compressed Channel Sensing (CCS) methods are concerned with the static sparse channel which is modeled as time invariable path number and path delays, but they fail to work in a dynamic scenario which allows the channel parameters to vary over time. To solve this problem, we introduce a simple Dynamic Sparse Channel Estimation (DSCE) algorithm for Orthogonal Frequency-Division Multiplexing (OFDM) systems. Exact reconstruction is provided by interchanging the constrained part and the optimization part of minimum \(\ell _0 \)-norm recovery, and then minimum \(\ell _0 \)-norm recovery is transformed into a \(\ell _0 \)-norm constrained Kalman Filter (KF) problem. The key idea of the proposed algorithm is the linearization of the non-linear state constraint. In this case, four types of fictitious observations are developed to substitute for the \(\ell _0 \)-norm constraint based on four families of continuous functions which are used to approximate to the discontinuous \(\ell _0 \)-norm. Therefore, DSCE is performed by employing KF in a stand alone manner. Numerical evaluations are presented to demonstrate the effectiveness of the proposed algorithm in practice.
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Jing, N., Wang, L. (2015). Dynamic Sparse Channel Estimation Using \(\ell _0\)-constrained Kalman Filter in OFDM Systems. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_3
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DOI: https://doi.org/10.1007/978-3-319-22047-5_3
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