University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 8 2 2017 08 23 ‌Basis Expansion Model Design for Sparse Doubly Selective Channel Estimation Using Dictionay Learning ‌Basis Expansion Model Design for Sparse Doubly Selective Channel Estimation Using Dictionay Learning 25 40 21740 10.22108/isee.2017.21740 FA Somayeh Mahmoodi Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran Mohamadjavad Omidi Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran Foroghsadat Tabataba Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran Journal Article 2017 02 06 In this paper, sparse double selective channel estimation using compressed sensing (CS) theory for OFDM systems is investigated. This theory helps to reduce the required pilot ratio and equivalently increases the spectral efficiency to achieve a constant mean square error. This is of great importance especially for double selective channels in which the required number of unknowns to be estimated and also the required number of pilot symbols are high. To take the advantage of compressed sensing, it is proposed that the sparsity enhancement of the coefficients of basis expansion model (BEM) should be considered in BEM design. It is also proposed to use K-SVD algorithm that is one of the most popular dictionary learning algorithms. Moreover, in this paper clustered pilot symbols are used to avoid inter-carrier interference. It is noteworthy that the channel coefficients representing inter-carrier interference are also estimated to be used in equalization. Numerical experiments have shown that the compressed sensing estimator employing the proposed basis, outperforms the one employing DFT-DPSS in terms of NMSE and system BER. <br />  In this paper, sparse double selective channel estimation using compressed sensing (CS) theory for OFDM systems is investigated. This theory helps to reduce the required pilot ratio and equivalently increases the spectral efficiency to achieve a constant mean square error. This is of great importance especially for double selective channels in which the required number of unknowns to be estimated and also the required number of pilot symbols are high. To take the advantage of compressed sensing, it is proposed that the sparsity enhancement of the coefficients of basis expansion model (BEM) should be considered in BEM design. It is also proposed to use K-SVD algorithm that is one of the most popular dictionary learning algorithms. Moreover, in this paper clustered pilot symbols are used to avoid inter-carrier interference. It is noteworthy that the channel coefficients representing inter-carrier interference are also estimated to be used in equalization. Numerical experiments have shown that the compressed sensing estimator employing the proposed basis, outperforms the one employing DFT-DPSS in terms of NMSE and system BER. <br />  https://isee.ui.ac.ir/article_21740_04fe2116f87e011be884bc277689b926.pdf