| Summary: | 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.
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