JOINT COMPRESSIVE SENSING FRAMEWORK FOR SPARSE DATA/CHANNEL ESTIMATION IN NON-ORTHOGONAL MULTICARRIER SCHEME
Salah, M., Omer, O., Sayed Mohammed, U. (2016). JOINT COMPRESSIVE SENSING FRAMEWORK FOR SPARSE DATA/CHANNEL ESTIMATION IN NON-ORTHOGONAL MULTICARRIER SCHEME. EKB Journal Management System, 44(No 5), 537-554. doi: 10.21608/jesaun.2016.117615
Mostafa Salah; Osama A. Omer; Usama Sayed Mohammed. "JOINT COMPRESSIVE SENSING FRAMEWORK FOR SPARSE DATA/CHANNEL ESTIMATION IN NON-ORTHOGONAL MULTICARRIER SCHEME". EKB Journal Management System, 44, No 5, 2016, 537-554. doi: 10.21608/jesaun.2016.117615
Salah, M., Omer, O., Sayed Mohammed, U. (2016). 'JOINT COMPRESSIVE SENSING FRAMEWORK FOR SPARSE DATA/CHANNEL ESTIMATION IN NON-ORTHOGONAL MULTICARRIER SCHEME', EKB Journal Management System, 44(No 5), pp. 537-554. doi: 10.21608/jesaun.2016.117615
Salah, M., Omer, O., Sayed Mohammed, U. JOINT COMPRESSIVE SENSING FRAMEWORK FOR SPARSE DATA/CHANNEL ESTIMATION IN NON-ORTHOGONAL MULTICARRIER SCHEME. EKB Journal Management System, 2016; 44(No 5): 537-554. doi: 10.21608/jesaun.2016.117615

JOINT COMPRESSIVE SENSING FRAMEWORK FOR SPARSE DATA/CHANNEL ESTIMATION IN NON-ORTHOGONAL MULTICARRIER SCHEME

JES. Journal of Engineering Sciences
Article 3, Volume 44, No 5, September and October 2016, Page 537-554  XML PDF (1.5 MB)
Document Type: Research Paper
DOI: 10.21608/jesaun.2016.117615
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Authors
Mostafa Salah1; Osama A. Omer2; Usama Sayed Mohammed email 3
1Dept. of Electrical Engineering, Sohag University, Sohag, Egypt
2Dept. of Electrical Engineering, Aswan University Aswan 81542, Egypt
3Electrical Engineering Department,Faculty of Engineering,Assiut University, Assiut, Egypt
Abstract
Many wireless channel behavior exhibits approximate sparse modeling in time domain, therefore compressive sensing (CS) approaches are applied for more accurate wireless channel estimation than traditional least squares approach. However, the CS approach is not applied for multicarrier data information recovery because the transmitted symbol can be sparse neither in time domain nor in frequency domain. In this paper, a new Sparse Frequency Division Multiplexing (SFDM) approach is suggested to generate sparse multicarrier mapping in frequency domain based on the huge combinatorial domain. The subcarriers will be mapped in sparse manner according to data stream for taking advantages of multicarrier modulation with lower number of subcarriers. The number of activated subcarriers is designed to achieve the same as Orthogonal Frequency–Division Multiplexing data rate under lower signal-to-noise ratio. The proposed approach exploits the double sparsity of data symbol in the frequency domain, and channel sparsity in the time domain. The same CS approach for both data recovery and adaptive channel estimation in a unified sparsely manner is used. The suggested framework can be used with any non-orthogonal waveform shaping and can work efficiently without any prior information about neither the channel sparsity order nor searching for the optimum pilot patterns.
Keywords
Compressive sensing; sparse channel estimation; super-resolution; non-orthogonal waveforms; sparse frequency division multiplexing (SFDM); combinatorial sparsifying
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