APSIPA Transactions on Signal and Information Processing > Vol 7 > Issue 1

Identification method for OFDM signal based on fractal box dimension and pseudo-inverse spectrum

Wenlong Tang, College of Electronic Engineering, Naval University of Engineering, China, Hao Cha, College of Electronic Engineering, Naval University of Engineering, China, Min Wei, The Unit 31003 of PLA, China, Bin Tian, College of Electronic Engineering, Naval University of Engineering, China, sweetybox123@163.com , Xichuang Ren, The Unit 91469 of PLA, China
 
Suggested Citation
Wenlong Tang, Hao Cha, Min Wei, Bin Tian and Xichuang Ren (2018), "Identification method for OFDM signal based on fractal box dimension and pseudo-inverse spectrum", APSIPA Transactions on Signal and Information Processing: Vol. 7: No. 1, e16. http://dx.doi.org/10.1017/ATSIP.2018.19

Publication Date: 1/0/2018
© 2018 Wenlong Tang, Hao Cha, Min Wei, Bin Tian and Xichuang Ren
 
Subjects
 
Keywords
OFDM signalfractal box dimensionpseudo-inverse spectrumclassification feature
 

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In this article:
I. INTRODUCTION 
II. SYSTEM MODEL 
III. IDENTIFICATION ALGORITHM 
IV. RESULTS AND DISCUSSION 
V. CONCLUSION 

Abstract

Orthogonal frequency division multiplex (OFDM) system is a special cognitive radio system that is widely used in military and civilian applications. As a crucial aspect of spectrum monitoring and electronic countermeasures reconnaissance, it is important to identify the OFDM signal. An identification method based on fractal box dimension and pseudo-inverse spectrum (PIS) has been proposed in this paper for the recognition problem of OFDM signal under multipath channel. Firstly, by theoretically analyzing the fractal box dimension of OFDM signal and single carrier (SC) signal, it can be concluded that the fractal box dimension of OFDM signal and SC signal has obvious differences. Thus, the fractal box dimension of the two types of signal is used to discriminate OFDM signal and SC signal. Then, the PIS of an OFDM signal is constructed according to the characteristics of the OFDM signal. Through theoretical analysis and the experimental simulation, it illustrates that the classification feature could be extracted by detecting the periodical peak of the PIS of OFDM signal and used for identifying OFDM signal in the Gaussian noise. Simulation results demonstrate that the proposed algorithm has better performance than the conventional algorithm based on autocorrelation.

DOI:10.1017/ATSIP.2018.19