Evaluation of compressed sensing reconstruction algorithm in OFDM system
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Date
2017-06
Authors
Hwong Sing Pui
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Abstract
Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique that had been adapted widely by high data rate wireless communication systems. This is due to its ability to reduce multipath channel fading, increase bandwidth efficiency, and elimination of inter symbol interference (ISI) which are favorable conditions in wireless signal transmission. OFDM divides the frequency selective fading channels into many narrow band flat fading sub channels to ease the equalization. The idea of orthogonality of this modulation allows all the sub carriers to be independent of each other and provides no interference among the sub carriers, and hence, this explained why the transmitted information can still be separated from the sub carriers. Channel estimation (CE) in OFDM system is compulsory to obtain the channel state information (CSI) at the receiver. Conventional channel estimation (CE) methods had been introduced to estimate CSI, but they are not able to exploit the wireless channel sparsity which causes reduction in bandwidth efficiency. The implementation of compressive sensing methods in OFDM was initiated that able to exploit the sparsity property of the signal and therefore, outperforms the conventional CE. Consequently, various widely used compressive sensing reconstruction algorithm such as: Orthogonal Matching Pursuit (OMP), Compressed Sensing Matching Pursuit (CoSaMP), and Subspace Pursuit (SP) will be evaluated to test their efficacy sparse estimation performances in OFDM system.