Compressive sensing-based channel estimation for SC-FDE system

Abstract

The channel of wireless broadband communication system usually features time domain sparsity. The channel estimation of single carrier frequency domain equalization (SC-FDE) system was modeled as compressive sensing (CS)-based reconstruction of sparse signal. Matching pursuit algorithms based on greedy search were adopted to reconstruct channel information. Under the premise of reduced reference signal overhead, the system reliability can be effectively enhanced. This paper proposes a priori information aided scheme. By inserting the cyclic prefix of the first training sequence to a SC-FDE transmission frame, which is used as guard interval, the priori information including more accurate channel length and sparsity can be obtained with inter-block-interference (IBI)-free training sequence. The proposed scheme shows obvious superiority in slow fading channels. It effectively improves the reconstruction of probability, thereby improving the accuracy of channel estimation, meanwhile sacrificing less spectral efficiency.

An Improved Orthogonal Matching Pursuit Algorithm for CS-Based Channel Estimation

“…Compared with conventional algorithms, better estimation performance is obtained by employing the proposed scheme. Since both methods fully utilize the IBI-free region signal as an observation vector, the proposed method in this research is able to achieve the same performance gains as the method in reference [22], but it does not require additional pilots. Furthermore, since matching pursuit algorithms are all based on obtaining accurate path delay estimates before calculating the path gain, the proposed method is also applicable to other matching pursuit algorithms.…”

“…The frame structure of the single-carrier frequency domain equalization (SC-FDE) system is very similar to that of TDS-OFDM. Reference [22] investigated the application of the OMP algorithm to sparse channel estimation in SC-FDE systems. The algorithm proposed in reference [20] required a small amount of frequency domain pilots, and reference [22] proposed inserting the cyclic prefix (CP) of the TS in the header of a frame.…”

“…Reference [22] investigated the application of the OMP algorithm to sparse channel estimation in SC-FDE systems. The algorithm proposed in reference [20] required a small amount of frequency domain pilots, and reference [22] proposed inserting the cyclic prefix (CP) of the TS in the header of a frame. All these methods required additional pilots, which sacrificed spectral efficiency while obtaining superior performance.…”

“…However, in practical applications, L is unknown and can only be obtained by estimating the realistic maximum delay of the channel, which is usually greater than the actual channel length. Reference [22] proposed a PIA-OMP algorithm based on a priori information, which improved OMP reconstruction accuracy and, thus, channel estimation performance. By inserting the cyclic prefix of the first TS as a guard Interval in the header of a transmitted frame, this method obtains a priori information such as the channel length and sparsity, but it sacrifices some spectral efficiency.…”

The research on channel estimation and signal‐noise ratio estimation based on minimum error entropy kalman filter for single carrier frequency domain equalization system

“…In recent years, the research on channel estimation mainly focuses on the following aspects: channel estimation based on deep learning, 11,12 channel estimation based on iteration, [13][14][15] and channel estimation based on compressive sensing (CS). 16 For example, a novel approach to learn a low-complexity channel estimator was presented in David Neumann, 12 which is motivated by the structure of the MMSE estimator; to obtain accurate estimation of the channel parameters for a fluctuant multipath channel, a novel channel estimation algorithm based on iteration is proposed in Li et al 14 ; and the channel estimation of SC-FDE system was modeled as CS-based reconstruction of sparse signal in Si et al, 16 which match pursuit algorithms based on greedy search were adopted to reconstruct channel information. We suggest that the reader is referred in particular to those by Souto et al, 17 Yang et al, 18 and Liu et al, 19 whose research works are very forward-looking.…”