International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume5,May 2018,

Topic : Alternative Optimization Algorithm for Channel and Clipping Amplitude Estimation in IoT-based Networks

Authors:Thasni mol.K ||

Abstract:- Internet of Things (IoT) is the idea to connect all devices to the internet. In IoT based devices the transmitter side should be low cost and low power. So the complexity of transmitter side should be transferred to the receiver side. Orthogonal Frequency Division Multiplexing (OFDM) is widely recognized as one of the key techniques for high data rate communications in wireless networks. The advantages of OFDM include high spectral efficiency, immunity against multipath fading and negligible inter-symbol interference. But it has a drawback of high PAPR. Among the PAPR reduction techniques clipping is selected because it is the simplest and cost-effective method. In this work, the joint estimation of clipping Amplitude and MMSE based channel estimation method has been proposed and evaluated. Here the channel is estimated by using MMSE technique and an iterative amplitude reconstruction method is used to estimate the clipping amplitude. An alternative optimization algorithm is used for the joint estimation of channel and clipping amplitude which is based on frequency domain block type training symbols. Here the channel is estimated by using LS and MMSE and made a comparison between them. The simulation results show that the MMSE has better performance than LS channel estimation in terms of SNR and MSE. The efficiency of these algorithms is evaluated by using CRLB calculation and achieves that lower bound at medium SNR. The simulation result shows a good performance without any prior information..

Keywords: Channel estimation, Clipping amplitude, CRLB, IAR, MMSE, LS, OFDM, PAPR.

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DOI: 01.1617/vol5/iss5/pid15347

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Dr. Allon Guez
Professor, Drexel University,

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
Frequency : monthly
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