implementation of energy detection based spectrum sensing in ni

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ISBN: 978-81-932712-5-4

IMPLEMENTATION OF ENERGY DETECTION BASED SPECTRUM SENSING IN NI USRP2920 S.Dhivya

A.Rajeswari

R.Aswatha

Research scholar, Dept of ECE, Coimbatore institute of technology, Coimbatore, India.

Professor and Head, Dept of ECE, Coimbatore institute of technology, Coimbatore, India.

PG student, Dept of ECE Coimbatore institute of technology, Coimbatore, India.

ABSTRACT Cognitive Radio is an emerging technology in wireless communications. Spectrum sensing helps the secondary users to detect the presence of primary users. Among various transform domain based spectrum sensing techniques, Energy detection is simple and less complex. The objective of this paper is to implement the energy detection based spectrum sensing in USRP hardware platform and obtain its performance. The implementation is done using LABVIEW and the detection performance is analysed. Keywords Cognitive radio, spectrum sensing, primary users, secondary users, energy detection.

1. INTRODUCTION The tremendous growth in the wireless communications has led to a huge demand on the implementation of new wireless services in both licensed and unlicensed frequency spectrum. Recent studies show that the fixed spectrum assignment policy results in poor spectrum utilization [1]. Cognitive Radio (CR) is a promising technology to enable access to unoccupied spectrum bands, called white spaces or spectrum holes, thereby achieving efficient spectrum utilization. The main objective of a CR user is to monitor the spectrum continuously and identify the presence or absence of primary user. This is done by sensing the RF environment by a process called spectrum sensing [2]. Spectrum Sensing must avoid any harmful interference to the primary users and

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.Dhivya, A.Rajeswari, R.Aswatha

should efficiently detect and utilize the spectrum.Sensing performance can be primarily determined on the basis of two parameters: Probability of False Alarm (Pfa) and Probability of Detection (Pd) [3]. The remaining paper is organized as follows. In section 2, a brief literature survey on various sensing methods is presented. In Section 3, a detailed study on energy detection and its hardware implementation is discussed. In section 4, results of energy detection technique are discussed. Finally the paper is concluded in section 5. 2. LITERATURE SURVEY various energy detection techniques in both time domain and frequency domain representations are discussed and a detailed survey on various spectrum sensing methods is presented [5]. Several challenges and solutions for various spectrum sensing techniques for cognitive radio are presented [6]. Methodology for cognitive radio system implementation is simulated and dynamic spectrum access technique is presented [7].

3.

IMPLEMENTATION OF ENERGY DETECTION BASED SPECTRUM SENSING IN USRP TEST BED 3.1 Energy detection Energy detection is the most common method of spectrum sensing due to easy computation and it does not require prior knowledge of the Primary User (PU) transmitted signal [4]. The energy detector estimates the presence or absence of

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primary user transmission which is done by comparing the received signal energy with a fixed threshold value. The transmitted signal at the primary user is denoted as s(t). The received signal at the Cognitive Radio (CR) user is given by [5], 𝑋 𝑡 =

𝑛 𝑡 ℎ 𝑡 ∗𝑠 𝑡 +𝑛 𝑡

; 𝐻0 ; 𝐻1

(1)

ISBN: 978-81-932712-5-4

signal to noise ratio. As the threshold increases, the probability of false alarm decreases. 3.2 USRP TESTBED IMPLEMENTATION The NI USRP 2920 is used for hardware implementation [17] which is programed with NI Lab view software.

Where, h(t) is the channel gain of the sensing channel , n(t) is the zero-mean additive white Gaussian noise(AWGN) The signal detection at the secondary user can be modelled as a binary hypothesis testing given as,  

Hypothesis 0 (H0): signal is absent Hypothesis 1 (H1): signal is present

The following are the steps involved in Energy detection based spectrum sensing 

The received signal by the CR user is sampled at definite time intervals. The samples are squared to measure the received energy. The average energy of all the samples is measured and compared with a decision threshold.

 

Figure 3.1: Block diagram of baseband transmitter Fig 3.1.shows the block diagram of baseband transmitter which acts as the primary user transmitter. The continuous sine waveform of 300Hz frequency is converted to array to sample data which forms the real part of the data being transmitted. The same waveform is phase shifted and it is converted to an array of sample values to form the imaginary part of the data being transmitted. AWGN noise is added to the data and transmitted.

The threshold formula for energy detection [5] is given by, 𝜆 = 𝑄−1 𝑃𝐹 + 𝑁

𝑁 2 𝜎2𝑤

(2)

Where, N= number of samples, 𝜎𝑤 = variance of the noise signal 𝑃𝐹 = Probability of false alarm If the energy of the received signal is greater than the threshold λ, the energy detector makes a decision as presence of primary user signal and if the energy of received signal is less than the threshold the decision is taken as absence of primary user signal. The threshold value can be increased by increasing number of samples and

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.Dhivya, A.Rajeswari, R.Aswatha

Figure 3.2: Front panel of baseband transmitter

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Fig 3.2.shows the front panel of baseband transmitter. The spectrum of the transmitted signal is displayed which is obtained by applying FFT to the data values. From the array of clusters the data values are unbundled to separate real and imaginary part and IQ plot is plotted. The 90 degree phase shift between the in phase and quadrature component is displayed in IQ plot.

ISBN: 978-81-932712-5-4

Fig 3.4.shows the front panel of baseband receiver. The energy of the received signal and the threshold is displayed in the front panel. The decision of presence or absence of primary user signal is given by the LED display. If the obtained energy is greater than the threshold the LED blinks and if the energy is less than the threshold the LED does not blink. Green colour in LED icon shows the presence of presence of primary user. The spectrum and IQ plot of the received signal are displayed. The following are the steps involved in the test bed implementation 

The USRP transmitter(Tx1)is configured to transmit the signal at 2GHz. The receiver (Rx2) is set to receive the signal from the transmitter at frequency of 2GHz. The USRP IP address is specified to configure the USRP transmitter and receiver. The signal power is obtained for 1000 samples of the received signal.

 Figure 3.3: Block diagram of baseband receiver Fig 3.3.shows the block diagram of baseband receiver. The data values of the received signal are fetched from the USRP fetch block. The energy detection is applied to the data values. The threshold calculation is done using MATLAB script by fixing the Signal to Noise Ratio as 10dB and Pfa as 0.01.The obtained energy is compared with the threshold and the decision is displayed using LED icon.

 

4. RESULTS AND DISCUSSION The models shown in Figures 3.1 and 3.3 are implemented in the testbed and the values of energy are acquired. Energy detection is performed by considering probability of false alarm, probability of detection, signal to noise ratio and number of samples taken into consideration. The following table gives the comparison about the decision taken by the energy detector based upon the threshold values. Table 4.1 Summary of test results

Figure 3.4: Front panel of baseband receiver.

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.Dhivya, A.Rajeswari, R.Aswatha

Trail

Energy

Decision

10.225

Threshold for Pfa=0.1 4.0526

1 2

3.263

4.0526

Signal exist

Signal Exist doesn’t

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5.CONCLUSION Energy detection based spectrum sensing is implemented on NI-USRP hardware platform. A basic transmitter and receiver have been implemented under AWGN environment. The results are obtained and analysed.The same energy detection algorithm can be extended to modulated stream of data and the results can be compared with other existing methods. ACKNOWLEDGEMENT The authors would like to thank the UGC- Major Research Project scheme through which the implementation in hardware platform became a possible task. REFERENCES [1] NishaYadav and SumanRathi (2011), “A Comprehensive Study Of Spectrum Sensing Techniques in cognitive Radio”, in International Journal of Advances in Engineering and Technology (IJAET), Vol. 1,Issue 3,pp.85-97. [2] Amir Ghasemi, Elvino S. Sousa (2008), “Spectrum Sensing In Cognitive Radio Networks: Requirements, Challenges and Design Trade-off” in Cognitive Radio Communications And Networks. [3] DongyueXue, EylemEkici, and Mehmet C. Vuran (2014), “Co-operative Spectrum Sensing in Cognitive Radio Networks using Multidimensional Correlations”, in IEEE Transactions on Wireless Communications, vol.13, No.4.,pp. 1832-1843. [4] Chongjoon You, Hongkyu Kwon, and Jun Heo (2011), “Cooperative TV Spectrum Sensing in Cognitive Radio for Wi-Fi Networks”, in IEEE Transactions on Consumer Electronics, Vol.57, No.1, pp. 62-67. [5] Miguel L’opez-Ben’itez and Fernando casadevall (2013), “Signal Uncertainity in Spectrum sensing for Cognitive Radio” in IEEE Transactions on Communications, Vol.61, No.4.,pp. 1231-1241. [6] Li, Z, Yu, FR & Huang (2010), 'A Distributed Consensus-Based Cooperative Spectrum Sensing in Cognitive Radios', IEEE Transactions on Vehicular Technology, vol. 59, no. 1, pp. 383– 393.

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ISBN: 978-81-932712-5-4

[7]

Chen, Z, Guo, N, Qiu (2010), 'Demonstration of Real-Time Spectrum Sensing for Cognitive Radio', IEEE Communications Letters, vol. 14, no. 10, pp. 915–917. [8] Haiqwan Wang, En-hui Yang, Zhijin Zhao, and Wei Zhang (2009), “Spectrum Sensing in Cognitive Radio Using Goodness of Fit Testing” in IEEE Transactions on Wireless Communications, Vol.8, No.11, pp. 5427-5430. [9] Chongjoon You, Hongkyu Kwon, and Jun Heo (2011), “Cooperative TV Spectrum Sensing in Cognitive Radio for Wi-Fi Networks”, IEEE Transactions on Consumer Electronics, Vol.57, No. 1, pp. 62-67. [10] Zhu Han, Rongfei Fan, and Hai Jiang (2008), “Replacement of Spectrum Sensing In Cognitive Radio” in IEEE Transactions on Wireless Communications, Vol. 8, No. 6, pp. 2819-2826. [11] ShengliXie, Yi Liu, Yan Zhang, and Rong Yu (2010), “A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks”, in IEEE Transactions on vehicular technology, Vol.59, No.8, pp. 4079-4092. [12] ZhiQuan, Shuguang Cui, and Ali H.Sayed (2008), “Optimal Linear Cooperation for Spectrum Sensing In Cognitive Radio Networks”, in IEEE Transactions on Selected Topics in Signal Processing, Vol. 2, No. 1, pp. 28-40. [13] Ian F. Akyildiz, Brandon F. Lo ,RavikumarBalakrishnan (2011), “Cooperative spectrum sensing in cognitive radio networks: A survey”, Elsevier journal of Physical Communication, Vol.4,pp.40-62. [14] Wei Zhang, Ranjan K. Mallik, Khaled Ben Letaief (2009), “Optimization of Cooperative Spectrum Sensing with Energy Detection in Cognitive Radio Networks”, IEEE Transactions on Wireless Communications, Vol. 8, No. 12, pp.5761-5766. [15] M.Tahir1, H. Mohamad1, N. Ramli 1,Y. Lee2 (2012), “Cooperative Spectrum Sensing using Energy Detection in Mobile and Static Environment”, IEEE International Conference on Computer Networks and Communication Systems, Vol.35,pp.1-5. [16] PankajVerma, Brahmjit Singh (2016), “On the decision fusion for cooperative spectrum sensing in cognitive radio networks”, Springer journal of wireless networks, Vol.22, pp1-10.

International Conference on Innovative Research in Engineering, Science, Management and Humanities (ICIRESMH-2017) at (IETE) Institution of Electronics and Telecommunication Engineers, Lodhi Road, Delhi, India

on 19th February 2017

[17]http://www.ni.com/pdf/manuals/376358a.pdF

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.Dhivya, A.Rajeswari, R.Aswatha

ISBN: 978-81-932712-5-4