A Blind OFDM Detection and Identification Method Based on ...

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Jun 6, 2009 - duration TGI, is added in each OFDM symbol to overcome the fading channel effect, thus, Ts is larger than the useful symbol duration Tu.
IEICE TRANS. COMMUN., VOL.E92–B, NO.6 JUNE 2009

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LETTER

A Blind OFDM Detection and Identification Method Based on Cyclostationarity for Cognitive Radio Application Ning HAN†a) , Sung Hwan SOHN† , Nonmembers, and Jae Moung KIM† , Member

SUMMARY The key issue in cognitive radio is to design a reliable spectrum sensing method that is able to detect the signal in the target channel as well as to recognize its type. In this paper, focusing on classifying different orthogonal frequency-division multiplexing (OFDM) signals, we propose a two-step detection and identification approach based on the analysis of the cyclic autocorrelation function. The key parameters to separate different OFDM signals are the subcarrier spacing and symbol duration. A symmetric peak detection method is adopted in the first step, while a pulse detection method is used to determine the symbol duration. Simulations validate the proposed method. key words: cognitive radio, spectrum sensing, OFDM, cyclic autocorrelation, signal detection and identification

1.

Introduction

Cognitive radio is defined as an intelligent wireless communication system that is aware of its surrounding environment, and learns from the environment and adapts its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters in real time [1]. Besides conventional methods [2], [3], the detection methods separating OFDM signal from other single carrier signal or random noise should be studied for cognitive radio applications mainly due to its importance for future wireless communication systems. [4] proposed to exploit the embedded periodicity among the subcarriers in OFDM signal. [5] developed several criteria based on the time domain periodicity introduced by the cyclic prefix (CP) in DVB-T OFDM symbol. These approaches require either the number of subcarriers or the spacing between consecutive subcarriers in a priori. However, different OFDM systems usually own their unique parameters due to various applications. Even in a single OFDM system, there are several operation modes with different parameters to achieve various transmission data rates. These make the detector almost impossible to know the information in advance. Thus, the existing methods are impractical to detect OFDM signal blindly. In this paper, we proposed a time domain cyclostationarity based approach to detect and identify OFDM signal from random noise. The key parameters to discriminate different OFDM signals are the subcarrier spacing and duration of guard interval. The proposed approach consists of Manuscript received October 7, 2008. Manuscript revised February 18, 2009. † The authors are with INHA-WiTLAB, Inha University, Incheon, Korea. a) E-mail: neil [email protected] DOI: 10.1587/transcom.E92.B.2235

two steps. The first step is to detect the OFDM signal from random noise simply by recognizing the symmetric peaks in the autocorrelations, which is a special case of the cyclic autocorrelation function. The subcarrier spacing is calculated as long as the OFDM signal is detected. In the second step, by identifying the symbol duration, the length of guard interval is calculated to recognize different OFDM signals. 2.

Cyclostationarity of OFDM Signal

A discrete-time baseband equivalent OFDM signal x(n) is modeled as follows, x (n) =

N−1 ∞  



c p,l e j N p(n−lT s ) g (n − lT s )

(1)

l=−∞ p=0

where g(n) is the transmitter pulse shaping filter, N is the number of sub-carrier, c p,l indicates the complex information symbols transmitted by the pth subcarrier during the lth symbol whose duration is defined as T S . Generally, CP, with duration TGI , is added in each OFDM symbol to overcome the fading channel effect, thus, T s is larger than the useful symbol duration T u . After transmitting through a frequency selective fading channel, the signal y(n) at receiver is y (n) =

L−1 

h (m) x (n − m) + w (n)

(2)

m=0

where h(n) is the impulse response of channel, L is channel order and w(n) indicates the white Gaussian noise. To reveal the cyclostationarity of the received signal, time varying correlation of the received signal y(n) is derived as [6], ry (n, τ) = y (n) · y∗ (n − τ) L−1  = σ2h(m) r x (n − m, τ) + rw (τ) m=0

= ry (n − m − lP, τ)

(3)

where σ2h(m) is the variance of h(n). For a fixed τ, ry (n, τ) is periodic in n with period P. Recall the definition of cyclostationarity [7], it has discrete Fourier series coefficients, which is a function of k and called cyclic correlation function (CAF), as follows, Ry (k, τ) =

∞ j2πkn 1  ry (n, τ)e− P P n=−∞

c 2009 The Institute of Electronics, Information and Communication Engineers Copyright 

IEICE TRANS. COMMUN., VOL.E92–B, NO.6 JUNE 2009

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Fig. 1

=

Fig. 2

CAF magnitude of OFDM signal.

N−1 ∞ L−1    2π  1 2 σh(m) σ2c e j N pτ g [n] g∗ [n−τ] P m=0 n=−∞ p=0

· e− j2παn + rw (τ)δ (k)

where α = k/P (4)

Considering Ry as a function of both τ and α, it can be divided to three parts, regardless of the effect of random noise, Rαy (τ) = cont · β · γ 1 2 σ σ2 , P m=0 h(m) c L−1

where

cont =

γ (α, τ) =

∞  

β (τ) =

N−1 



e j N pτ ,

p=0

 g (n) g∗ (n − τ) e− j2παn

(5)

n=−∞

where the first part is a constant which is independent of τ and α, while β and γ are the dominant terms. In the following analysis of this paper, we will focus on the dominant terms that affect the CAF of OFDM signal. Figure 1 is generated to demonstrate the CAF magnitude of OFDM signal. τ is normalized to T U and α takes the integer number of 1/T s . It exhibits two features. On one hand, the special case of cyclic autocorrelation, as well known as autocorrelation, is included as shown in Fig. 1, when α = 0. The dominant terms of R0y (τ) change to    N−1    2π  j N pτ  j2πΔ f τ sin (πNΔ f τ)    |β (τ)| =  e  = e  sin (πΔ f τ)   p=0   sinc (NΔ f τ)   (6) = N  sinc (Δ f τ)    ∞    g (n) g∗ (n − τ)  |γ (τ)| =   n=−∞ where Δ f is the subcarrier spacing. |β| determines the location of the peak that appears in the magnitude of CAF while |γ| limits the bound of the peaks as well as the magnitude of them. Ideally, the ratio between the major peak value and

CAF magnitude of three OFDM symbols when τ = T U .

the local one is determined by the ratio between T S and TGI . This phenomenon can be easily understood considering the CP is added in front of each OFDM symbol before transmission. However, it does not hold for the signals other then OFDM. On the other hand, when τ = T U , Rαy (T U ), as a function of α exhibit discrete peaks on the surface of CAF magnitude, as shown in Fig. 1. Since the value of τ is set, the term dominates CAF of OFDM signal is   ∞     − j2πnα  ∗  g (n) g (n − τ) e |γ (α)| =   n=−∞   ∞       Tu  T u − j2πnα     = G (α) e− j2π 2 α  e g n− =  2  n=−∞    = G (α) (7) where g (n) is a rectangular pulse with period TGI and G (α) is its Fourier transform which is a sinc function. It is noticeable that the CAF turns to be the summation of shifted sinc functions when receiving several OFDM symbols successively. Figure 2 clearly demonstrates the magnitude of Rαy (T U ), which is generated based on three received OFDM symbols. The local peaks appear at α = n/T S . 3.

Blind OFDM Signal Detection and Identification

In order to fit into the practical application, the parameters of OFDM signal should be treated as unknown factors at the detector. 3.1 OFDM Signal Detection The blind detection of OFDM signal aims to judge if the received signal is OFDM or single carrier signal and random noise. According to the analysis in the previous section, the criterion that can be used to separate the two hypotheses is the symmetric peak property exhibited in R0y (τ). It is well known that R0y (0) always takes the largest value for any received signal y(n). However, in the case of OFDM

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Fig. 3

CAF magnitude of OFDM signal when α = 0.

signal, besides this peak, there are other peaks distributed on both sides, namely R0y (±T U ), as shown in Fig. 3. Due to the comparatively short duration of CP, R0y (±T U ) is smaller than R0y (0) and their ratio can be calculated according to (6) easily, which is TGI /T S . Thus, we can detect the OFDM signal by finding these symmetric peaks and then T U can be determined. The detection is done by first calculating R0y (τ) of received signal. After that, detect the peak value on both sides of R0y (τ). And their positions are stored for the purpose of detection. Then, the decision is made based on if these two peaks are symmetrically distributed on both sides of R0y (τ). If the symmetry holds, the detector declares that the received signal is OFDM and the T U takes the τ values of the peaks. 3.2 OFDM Signal Identification Since subcarrier spacing, same as the useful symbol duration, is calculated after the OFDM signal is detected in the first step, the OFDM signal can be finally identified if we know the duration of guard interval. Therefore, symbol duration T S is the target of identification in the second step, according to the relation that TGI = T S − T U . Recall the analysis of Rαy (T U ) in Sect. 2, the only term that we concern is ∞   T u  − j2πnα g n − (8) γ (α) = e 2 n=−∞ It is easy to recognize that (8) is the Fourier transform of the rectangle pulse g (n). We can recover it by applying the inverse Fourier transform. If there are M consecutive OFDM symbols, the results of the inverse Fourier transform turns out to be N rectangle pulses equally separated by T S , as shown in Fig. 4 for M equals to 3. Based on this theory, the identification can be applied according to the following procedure, 1. After the detection of the OFDM signal and T U is known, we calculate Rαy (T U ) as a function of α. 2. Take the inverse Fourier transform of Rαy (T U ) according

Fig. 4

Inverse Fourier transform of Rαy (T U ) with 3 OFDM symbols.

to α. It exhibits several peaks whose number is identical to the number of OFDM symbols. 3. Determine the value of T S according to the duration between two consecutive peaks. Noted that, in OFDM system, the pulse shaping filter can be functions other than rectangle one. The peaks can be generated by applying a matched filter whose shape is the same as the pulse shaping filter of OFDM system. 4. The parameters such as T S and TGI are used to identify the type of OFDM signal. It is necessary to address that the proposed detection and identification method operate without knowing any a priori information on the OFDM signal. This blind method is based on the analysis of the cyclostationarity of the received signal. However, the calculation load is low compared to other methods mentioned in [4], mainly due to the fact that our method calculates several lines of the 3dimension cyclic correlation function instead of the whole surface. 4.

Simulations and Discussions

In the simulations, OFDM signal belongs to four modes with different subcarrier spacing and guard interval. The subcarrier spacing is expressed in terms of the number of subcarriers N in the same band, such as 1k and 2k, where 1k indicate the number of 1024. The length of guard interval is select from 1/4 and 1/8 of useful symbol duration. The detection and identification performances are evaluated among these OFDM signals under additive white Gaussian noise (AWGN). 4.1 Signal Detection Performance Detection performance using the peak detection is evaluated in terms of the detection and false alarm probabilities under different signal-to-noise ratios (SNRs). The results are shown in Fig. 5. It is clear that more subcarrier results in better detection performance. That is, 2k subcarriers with 1/4 length of guard interval achieves the best performance.

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is easier to be identified with the same number of OFDM symbols for identification. On the other hand, more symbols for identification will improve the detection performance in a certain level because the zero mean AWGN will be averaged out after the combination of several symbols. 5.

Fig. 5

Simulation results of OFDM signal detection.

Conclusions

In this paper a cyclic autocorrelation function based approach for detection and identification of OFDM signal is proposed. It first discriminates the OFDM signal from random noise and other single carrier signals. Then, by searching the cycle frequencies, the length of symbol duration is determined to recognize different OFDM signals. The proposed method requires low computation load. Simulations are carried out in AWGN and verified our method can satisfy the detection and identification requirement with a low false alarm probability. Acknowledgments

Fig. 6

Simulation results of OFDM signal identification.

This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the National Research Lab. Program funded by the Ministry of Education, Science and Technology (No. M10600000194-06J000019410). This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Ministry of Education, Science and Technology (MEST) (No. R01-2006-000-10266-0 (2008)). References

However, the detection time should be longer. For the target detection probability of 90%, even the OFDM with shortest symbol duration is able to be detected below −1 dB. This detection performance can be improved by increasing the observation time, since its symbol duration is shorter than that of other cases. It is also notable that the false alarm probabilities which are indicated by the blue curves are almost zeros for all the OFDM signal detections. It proves the fact that the method by detecting the symmetric peaks is reliable for the first step. 4.2 Signal Identification Performance The identification performance is evaluated through the probability of successful identification. Successful identification means the symbol duration calculated through the proposed method falls into the likelihood region of the actual value. An example is shown in Fig. 6 with 2 different OFDM signals. OFDM signal with longer symbol duration

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