ICCCCT-10

FPGA implementation of Spectrum Sensing based on Energy detection for Cognitive Radio

S.Srinu, Samrat L. Sabat School of Physics

University of Hyderabad Hyderabad, 500046

Email: ph08pe02@uohyd.ernet.in.slssp@uohyd.ernet.in

Abstract-Spectrum sensing is a critical component of the Cognitive Radio that detects the presence of primary user signal in a channel. In this paper energy detection technique based on Neyman-pearson criterion is used to detect the presence of deterministic primary user (PU) signals in the channel. We have considered three different kinds of modulated signal such as BPSK, QPSK , DVB-T (2K mode) under additive white Gaussian noise (AWGN) and Rayleigh fading channel environment as specified in IEEE 802.22 standard for validating the algorithm. The simulation result shows that the energy detector achieves the desired probability of detection (Pd20.9) with probability of false alarm (Pf:S:O.l) at low signal to noise ratio (SNR) up to -8dB for QPSK and DVB-T modulated signal with sample size of 64. The algorithm is also implemented in Xilinx Virtex2pro XC2VP30 (FFG896-7) Field Progranunable Gate Array (FPGA). Hardware in loop (HIL) technique is used for verifying the algorithm in FPGA. The implementation result reveals that the algorithm fits into the Virtex2pro FPGA and can execute with operating frequency between 110 to 138 MHz for different sample size of primary user signals.

Index Terms-Cognitive Radio, Spectrum Sensing, Energy detection, FPGA, Hardware Co-simulation.

I. INTRODUCTION

THE advent of new high data rate wireless applications, as well as rapid development of existing wireless services, leads to an increase in demand for additional bandwidth. On the other hand, statistics shows that the utilization of spectrum resources is at a very low level in terms of different dimensions such as frequency, time, geographical space, code, angle and signal polarization [1]. In the recent past Cognitive Radio is emerged as a promising technology to balance between spectrum congestion and spectrum under-utilization. Cognitive radio uses the concept of dynamic spectrum access, in which an unlicensed user termed as secondary user (SU) can use the spectrum of a licensed user termed as primary user (PU) with a constraint that it should not interfere to the power level of PU [2]. The Main functions of CR are spectrum sensing, spectrum management, spectrum sharing and spectrum mobility [3]. The objective of spectrum sensing is to detect activity of the PU signal in a channel. Broadly, spectrum sensing can be done in two ways i.e., passive techniques and active techniques. The main disadvantages of passive technique is that it takes more time for sensing a channel. To overcome the problem of sensing time, active sensing techniques are more popular [4]. Different signal processing techniques are being

used for active sensing. The popular methods are based on energy detection,matched filtering and cyclo-stationary feature detection [5] [6]. According to the IEEE 802.22 standard, the specified detection time should be less than or equal to 2sec [7]. Matched filtering is an optimal way for signal detection in communication systems. However it requires prior knowledge of the licensed user signal which may not be available. Energy detection is often used to determine the presence of signals without prior knowledge of signal. However, limitation for energy detection is the decision threshold is subject to changing signal to noise ratio(SNR). This is one fundamental limit for detection of weak signals below the S N Rwau. SN Rwall defined as the minimum SNR threshold due to noise uncertainty below which a detector unable to identify a primary signal reliably regardless of sensing time [8]. Cyclo­stationary detection techniques is often used to determine the presence of signals without prior knowledge and it can able to discriminates the noise energy from the modulated signal energy but it involves more computation intense operations.

In this paper we have studied and implemented energy detection technique because of agile spectrum sensing [9].The performance of this scheme is represented by two essential parameters i.e., probability of detection (Pd) and probabillity of false alarm (P f). The design of detector often involves constructing the solution to an optimization problem. We used Neyman-Pearson detection, to fix P f at some tolerable level G, and maximize Pd constraint that P f :s: G. Naymen­Pearson (NP) criterion leads to a likelihood ratio test identical to that of bayesian detection, except that the threshold (..\) is determined by the desired value of probability of false alarm (P f) [10]. In reacent past FPGAs are being popularly used for signal processing applications because of parallel processing,high speed, reconfiguration features and less power dissipation compared to microprocessors (DSP Processoers). In this paper enrgy detection technique is implemented in Xilinx Vertex2pro XC2VP30 (FFG896-7C) FPGA [11]. The primary user signal such as BPSK, QPSK and DVB-T(uses 64QAM) are observed for detection under the AWGN and Rayleigh fading channel environment.

Rest of the paper is organized as follows: Section 2 presents Energy detection algorithm for detecting the PU's signals. Section3. presents simulation and hardware implementation results followd by conclusions in section 4.

978-1-4244-7770-8/10/$26.00 ©2010 IEEE 126

r(t) � HO

>A �Hl .. Band Pass � NO ... Filter

<A

Fig. l. Energy detector paradigm

II. ENERGY DETECTION ALGORITHM

The fundamental problem of spectrum sensing in CR is to discriminate between samples that contain only noise and the samples contain signal information embedded with high noise power. Energy detection can be performed in both time and frequency domain.

Fig.l shows the block diagram representation of energy detector based spectrum sensing. The received signal r(t) is filtered by a band-pass filter to select the channel to be scanned. Detector computes the energy of the received signal by taking discrete time samples from NO Converter and compares it with the predetermined threshold value (A) . The objective of spectrum sensing is to discriminate between the following two hypotheses HO (PU is absent) and HI (PU is in operation) [12].

UnderHO : r(n) = w(n), UnderHI : r(n) = hs(n) + w(n), n = 0, I, 2, ... , N - 1.

Where r[n] = [r(O), r(I), r(2), ... , r(N - 1)] is the signal received by CR user is an N dimensional vector space over R(r E RD) or C, N is the sample size, s(n) is licensed user transmitted signal, w( n) is the additive white Gaussian noise (AWGN) and h is the amplitude gain of the channel. The optimal Neyman-Pearson test is to compare the log-likelihood ratio with a predetermined threshold (A) given as,

A � IOg[P(rIH1)] � A P(rIHo) HO (1)

Where P(r I HI), P(rIHo) are the probability density func­tions of HI and HO respectively.

Noise is assumed to be independent and identicaly dis­tributed with zero-mean and circularly symmetric complex Gaussian with variance one such that it follows X�u Chi-square distribution and signal follows X�u(2,) non central Chi-square distribution with 2u degrees of freedom, under such conditions,

Eq.(I ) can be rewritten as [13],

N-I H1

ICCCCT-10

(3)

Where PI-des is the desired false alarm probability. u and v are the shaping and sizing parameters and depends on the number of samples to be detected and variance of the noise respectively. yl(Ala,b) denotes the inverse of gamma function.

As Chi-square is the special case of gamma distribution, the threshold value can be computed from gamma distribution.

The variance of noise w(n) computed as,

1 N-l

(j2 = E[w2]- (E[W])2 = N L [w(n) - J1f

n=O

where (J1) is the mean of the noise vector defined as

1 N-I

J1=E[w]= NLw(n) n=O

Probability of detection and probability of false alarm are computed for analysis purpose as [15],

Pd = p{y > V Hd = Quh/2-Y, V':\) (4)

P = { AIR} = r(u, A/2) (5) I P Y> 0

r(u)

Where , is the signal to noise ratio (SNR), u = T. W is the time bandwidth product, f(.) and f(.,.) are complete and incomplete gamma functions and Qu(.,.) is the generalized Marcum Q-function

A high value of probability of misdetection (Pm), Pm = 1 - Pd would result in missing the presence of PU with high probability, which in turn increases interference to the primary user. On the other hand, a high P f would result in low spectrum utilization since false alarm decreases the probability of successful detection of holes.

In the fading environment, the received signal energy and SNR are location dependent. If the received signal does not have any line of sight (LOS) component the amplitude of the signal follows a Rayleigh distribution. For this case, P f is same for all locations, since it is independent of the SNR. When the amplitude gain of the channel, h, varies due to the fading. The probability of detection Pd varies with SNR as [15]

Pd-fad = J Qu(�, V':\)fl' (x) dx (6)

Where f,(x) is the probability density function of SNR under fading.

III. SIMULATION RESULTS AND HARDWARE

A � IIrl12 = L Ir[nW � A (2) IMPLEMENTATION

n=O HO

where Il rll is the norm of the received signal vector. In practice the detector may not have the characteristics of the recieved signal apriori.

The threshold (A) for a desired value of probability of false alarm (Pf) is [14],

A. Simulation results The simulation and implementation of algorithm is carried

out for signals of lengths N=16, 32, 64,128 from transmitted signals of primary users of different modulations such as BPSK, QPSK and DVB-T(64QAM). The received signal is filtered by a band-pass filter of band width 200 kHz and the

127

time of observation is 160p,sec for BPSK and QPSK with bit rate of 84kbps. DVB-T specification are given in table. 1 , where the band width of the channel is 6MHz and the time of observation for detection is 5.4p,sec for sample size of 64. Detector computes the energy of the received signal in the observed time period and compares it with a precomputed threshold (oX) as Eq.(2). Threshold is determined by the desired value of probability of false alarm (P f) as Eqn.(3). The slope of the curve of the ROC at a particular point is equal to the value of the threshold required to achieve the Pd and Plat that point [10] [16]. Noise is assumed to be independent and identicaly distributed with zero-mean and circularly symmetric complex Gaussian with variance one. But in practice it is random. The bandwidth and time of observation of selected channel for signal detection is taken such that the sample size (N) is equal to 2u where u is time bandwidth product. The algorithm is simulated for SNR range between -10dB to lOdB in steps of 2dB. We computed the detection probability using test statistic by 10000 iterations. The detection probability is computed for different probability of false alarm (0.2025, 0.1089, 0.012). More approximation of the ROC curve is possible with increasing the number of iterations.

TABLE I DVB-T SPECIFICATIONS

Transmission mode 2k Number of carriers 1705 Carrier frequency 4.8MHz Modulation 64 QAM Channel Band width 6MHz Code rate 2/3 Guard interval ratio 114 Data rate 3.732 - 23.751Mbps Observed Time 5.4JLsec

The performance of detector is measured with probabil­ity of detection (Pd) and probabillity of false alarm (P f). Performance of energy detector for different values of SNR and different sample size (N) of the received signal can be characterized through Receiver operating characteristics (ROC) curves.

The probability of detection is computed for different values of SNR with constant probability of false alarm for BPSK, QPSK and DVB-T signal. Figures 2,3 and 4 presents the Pd vs SNR with different PI values for BPSK, QPSK and DVB-T signals of sample saize 64 respectively. Figures 2-4 reveals that as the SNR increases the probability of successful detection of spectrum holes increases. As the PI value increases Pd also increases for all the three types of signals. The energy detector able to detect BPSK signals upto -2dB with PI=O.l, although it detects with pd = 0.9 upto -5dB for PI=0.2. Fig.3 shows that for QPSK signal the detector can detect the presence of signal with probability of 0.9 upto -8dB with P 1=0.1. It also reveals that for P 1=0.2, detector performance increses upto -lOdB. Fig.4 shows the simulated results for DVB-T signal. It shows the same trend as for QPSK signal, fig-3. for DVB­T signal which is being popularly used in IEEE 802.22, the detector is able to detect the presence of PU signal upto -8 dB

ICCCCT-10

Graph blw snr vs Pd with different fixed Pta tor(BPSK) N=64

0.1 . 0�--1�0�----- -�5------�0------�5------�10

snr(Eb/NO)

Fig. 2. SNR Vs Pd of BPSK signal under AWGN Channel (Pf=0.2025,0.I089,0.012 and N=64)

SNR level with P 1=0.1. In this case also detector performance increases with increase of P 1=0.2. The complementary ROC curves which plots the probability of false alarm V s probability of misdetection for a fixed value of sample size. Figure.5, 6 and 7 shows that as the sample size increases the probability of misdetection decreases at SNR -8dB.From figure.7.we can observe that the desired value of Pm at pf=0.12 with sample size 64 but for same value of PI the Pm value is increases for 32 and further increases for 16 samples. figure 5 and 6 follows the same trend. Figure 8 and 9 reveals that Rayleigh fading degrades the performance of energy detector for the DVB-T of sample size 16. Figure 8 gives the ROC curves at SNR OdB with and without Rayleigh fading. The fading parameters in the simulation are Doppler shift 1000Hz and bit rate 84kbps. From the figure.9.due to Rayleigh fading the probability of detection degrades approximately by 1db compared to the signal with A WGN for different values of SNR with fixed PI at 0.1089.

Table.2. Gives the S N Rwall for different primary users with sample size of 16,32 and 64 by architecture. We observed that the least SNR required to achieve the desired Pd and PI values (Pf::;0.012 and Pd�0.9) is -3dB for QPSK, DVB­T(64QAM) of sample size (N=64). For the case (Pf::;O.l and Pd�0.9) the SN Rwall observed at -8db for QPSK of sample size 64 and for the case (Pf::;0.2 and Pd�0.9) the least SNR possible to detect primary user is -lOdB. Since the Q functions is monotonically decreasing, it is evident that both Pd and Pf decay to zero as threshold (oX) increases. There is also an explicit relation between these two [10].

(7)

In order to increase Pd, we must also increase P f. This can be viewed for the latter case (Pf::;0.2 and Pd�0.9), where the S N Rwall is -lOdB the best case achieved. In practice the desired values of probabilities for a detection technique were

128

0.9 .

O.S .

'0 0.5 . � fJ 0.4 . Ll e a. 0.3 .

0.1

Graph b/w snr vs Pd with different fixed Pfa for(QPSK)N=64

.. :

: . -

. "

O L-� ________ L-______ � ______ -L ______ � -10 -5 o

snr(Eb/NO)

5 10

Fig. 3. SNR Vs Pd of QPSK signal under AWGN Channel (Pf=O.2025,O.I089,O.OI2 and N=64)

0.9

'0 0.5 . � fJ 0.4 Ll e a. 0.3

0.2

0.1

Graph blw snr vs Pd with different fixed Pfa for(DVB-T) N=64

OL-� ______ �L-______ � ______ -L ______ � -10 -5 o

snr(Eb/NO)

5 10

Fig. 4. SNR Vs Pd for a DVB-T signal under AWGN Channel (Pf=O.2025,O.I089,O.OI2 and N=64)

(Pf:::;OJ and Pd�O.9).

B. Hardware implementation The hardware implementation of the algorithm is carried

out using Xilinx System Generator for DSP tools and Xil­inx Vertex-2pro XC2VP30 (FFG896-7C) FPGA [11]. The Spectrum sensing architecture for FPGA implementation by energy detection is shown in Fig 10. it has four major blocks i.e., Energy computing block, noise variance computing block,threshold and decision block. Received signal samples are directly imported from MATLAB workspace to the Xilinx System-Generator. The Energy unit computes the energy of the received signal with different sample size(N). Noise vari­ance block calculates the variance of the noise. Threshold is calculated using gamma function. Decision unit depends on

ICCCCT-10

Complementary ROC for BPSK at SNR=-SdB for different N

100 t:"�""":""""�"",,,,����;:�7:=C::;;::::��

t: 0 ti 2 ., '0 '" E '0 � :0 '" Ll e a.

10-' . ........ , ... . .. . ... . .

Probability of false alarm

Fig. 5. Complementary ROC curves for BPSK signal under AWGN Channel (N=16,32,64 and ,),=-8dB )

t: o ti 2 ., '0 '" E '0 � :0 '" Ll e a.

Complementary ROC for QPSK at snr--SdB

100 �����������:=�������

10'" 10-3 10-' Probability of false alamn

Fig. 6. Complementary ROC curves for QPSK signal under AWGN Channel (N=16,32,64 and ,),=-8dB)

threshold value and energy of the received signal. Decision unit gives the status of primary user activity in the channel.

FPGA implementation of energy detection technique is validated with PU's Transmitted signals under AWGN and Rayleigh fading channel. Hardware in loop (HIL) technique is used for verification of algorithm in FPGA. The HIL simulation result matches with the MATLAB results. The re­source estimation for energy detection architecture based on 64 sample data requires 1641 slices, 3233 flip-flops, 1681 LUTs, 817 lOBs and 64 embedded multipliers. The results indicates that resources utilization is minimal for this techniqie and increases as the sample size of the received signal increases.

The algorithm works in the FPGA with operating frequency range between 11 OMHz-138MHz (maximum) for all above mentioned licensed user signals of sample size 16, 32 and

129

ICCCCT-10

Ol&l Re$OlIce sysaem Estm;a1Ot Generator ,-----____ --, Energy

Energy unit

Threshold

L-___________ �.�_ H��,��-���-�==� GoitewayOul1 Decision

Fig. 10. Spectrum sensing architecture for FPGA implementation by Energy detection

c o n 2 '" "0 '" E '0 � :0 '"

.0 e 0..

Complementary ROC for DVB-T with different sample size at snr-SdB 100

���==�����;;�������:;���

Probability of false alarm

Fig. 7. Complementary ROC curves for DVB-T signal under A WGN Channel (N=16,32,64 and ,""(=-SdB)

c o � '" "0 '0 � :0 '" .0 e 0..

ROC for DVB-T with different sample size at snr OdB 100 r--c"��--�""�--�"�������

10-2 Probability of false alarm

Fig. S. ROC(Pd Vs PC) curves of DVB-T signal under AWGN(A) and AWGN+Rayleigh fading Channel(A+R) (N=16, ,""(=OdB)

SNR Vs Pd (DVB-T(16)), PfaO.10S9 for AWGN, AWGN+RAYLEIGH FADING 1 ��--�--�---,--�-=���� I =;= �

+R I 0.9 .

O.S .

c 0.7 . o � 06 '" "0 '0 0.5 . � :g 0.4 .0 e a. 0.3 .

0.2 .

0.1

OL-_-L __ �_� __ �_� __ -L __ L-_� -8 -6 -4 -2 o

snr(Eb/NO)

2 4 6 8

Fig. 9. SNR Vs Pd of DVB-T signal under AWGN(A) and AWGN+Rayleigh fading ChanneI(A+R) (Pf=O.lOS9, N=16)

TABLE II THE LEAST SNR REQUIRED FOR ARCHITECTURE TO ACHIEVE

DETECTION PROBABILITY (PD) Vs PROBABILITY OF FALSE ALARM (PF)

Modulation type,(N) Pf�O.OI2,SNR Pf�O.lOS9,SNR Pf�O.2,SNR BPSK 16 7dB 2dB -ldB QPSKI6 5dB OdB -3dB DVB-T(64QAM)16 6dB IdB -2dB BPSK 32 5dB OdB -2dB QPSK32 5dB IdB -2dB DVB-T(64QAM)32 IdB -3dB -6dB BPSK64 2dB -2dB -5dB QPSK64 -3dB -SdB -IOdB DVB-T(64QAM)64 -3dB -SdB -9db

64. The results show that the Energy Detector can achieve the desired Pd and Pf values (Pf::;O.1 and Pd2:0.9) at low SNR up to -I OdB for QPSK, DVB-T with sample size(N) of 64 and -8dB to achieve(Pf::;O.1 and Pd2:0.9). Table.3.explains the maximum frequency that can be achieved by the detector in the FPGA. As the sample size increases the frequency of operation (sensing time increases) decreases. The best case

130

TABLE III MAXIMUM FREQUENCY THAT WORKS IN THE FPGA USING ENERGY

DETECTOR ARCHITECTURE FOR BPSK, QPSK, DVB-T (64QAM) Modulation Sample size(N) Maximum frequency( MHz) BPSK,QPSK,DVB-T 16 138.4 BPSK,QPSK,DVB-T 32 115.5 BPSK,QPSK,DVB-T 64 110.8

achieved at 138.4MHz for the input sample size of 16.

IV. CONCLUSION

In this paper energy detetion technique is used for detecting BPSK, QPSK and DVB-T signals of different length and spec­ifications as specified by IEEE 802.22 standard. The results indicate that as the sample size increases the performance of the energy Detector also increases, but at the same time it requires more resources. Due to Rayleigh fading the Probabil­ity of detection further devolves. The results discloses that our detection algorithem can able to detect the DVB-T signal with required 90% Pd and 10% Pf upto -8dB with sample size of 64. FPGA implementation of proposed detection technique can reliably detect the PU's activity in the channel with operating frequency between 110-138 MHz. The implementation of Energy Detector architecture on Wrreless Access Research Platform (WARP) board and Cooperative spectrum sensing techniques are under development to increase the detection probability.

ACKNOWLEDGMENT

REFERENCES

[1] T. Yucek and H. Arslan, "A survey of Spectrum Sensing Algorithms for cr Applications first quarter 2009," University of South Florida, USA, Tech. Rep., 2009.

131

ICCCCT-10

[2] K. Larson(Chair), L. Cacciatore, T. Eng, J. Jackson, B. Luther, and T. Magnire, "Fcc: Spectrum policy task force: Report of the interference protection working group," Federal Communications Commission, Tech. Rep., 2002.

[3] S. Haykin, "Cognitive radio: Brain-empowered wireless communica­tions," IEEE Journal on Selected Areas in Communications" vol. 23, no. 2, pp. 201-219, Feb 2005.

[4] G. R. Faulhaber, "Deploying cognitive radio:economic, legal and policy issues," International Journal of Communication" vol. 2, pp. 1114-1124, Aug 2008.

[5] A. Huseyin, Cognitive Radio, Software defined radio, and Adaptive wireless systems. Springer publication, 2007.

[6] Y. Zeng and Y.-C. Liang., "A review on spectrum sensing for cognitive radio:cha1lenges and solutions," EURASIP Journal on Advances in Signal Processing" vol. 138632, pp. 1-15, Oct 2009.

[7] K. C. Carlos and D. Birru., "Ieee 802.22:an introduction to the first wireless standard based on cognitive radios;' IEEE journal of commu­nications" vol. I, no. I, pp. 38-47, April 2006.

[8] D. Oh and Y. Lee., "Energy detection based spectrum sensing for sensing error minimization in cognitive radio networks;' International Journal of Communication Networks and Information Security (IJCNIS)" vol. I, no. I, pp. 1-5, April 2009.

[9] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty., "Next generation/dynamic spectrum access/cognitive radio wireless networks:

[10]

[11]

[12]

[13]

[14]

[15]

[16]

A survey;' Computer Networks" vol. 50, no. 13, pp. 1114-1124, May 2006. S. Kay, Fundamentals of Statistical Signal Processing and Estimation theory. Prentice Hall, 1998. www.xilinx.com .• Xilinx University program Virtex-2pro Development system, hardware reference manual. xilinx, 2001. P. Kimtho and T. Jun-Ichi, "Signal detection for analog and digital tv signals for cognitive radio," IEICE Technical Report (Institute of Electronics, Information and Communication Engineers), Tech. Rep., 2006. H. Urkowitz, "Energy detection of unknown deterministic signals," in IEEE Proceedings, vol. 55, no. 4, April 1967, pp. 523-531. W. Xia and W. Cheng., "Correlation-based spectrum sensing in cognitive radio;' in IEEE Proceedings, University of Huazhong Science and Technology, Wuhan , China, Sep 2009, pp. 67-72. A. Ghasemi and E. S. Sousa., "Collaborative spectrum sensing for opportunistic access in fading environments;' in IEEE Proceedings, Universityof Toronto, Toronto, Canada, Oct 2005, pp. 131-136. M. Barkat, Signal Detection and Estimation. Artech house publishing, 2005.