ISSN: 2349-2163 Probability Density ... Journal of Innovative Research in Advanced Engineering...

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Issue 9, Volume 2 (September 2015) www.ijirae.com

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Probability Density Function based Adaptive Spectrum Sensing for Throughput Maximization in Cognitive Radio

Network S.Kanimozhi*, N.P.Gopalan

Research Scholar, PRIST University, Thanjavur, India Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India

Abstract— In a cognitive radio network, the spectrum sensing must be done quickly and accurately to avoid primary interference and to achieve high throughput. Sensing a channel for a long reduces the throughput and increases the energy wastage. A probability density function (pdf) based adaptive sensing method is proposed in which either a pdf based spectrum sensing or a detailed cyclostationary approach is adopted according to the Signal to Noise ratio (SNR). Simulated results confirmed that the method proposed attains considerable improvement in the throughput and reduction in mean sensing time.

Keywords-- Cognitive radio Network, probability density function, spectrum sensing, throughput maximization, mean detection time

I. INTRODUCTION The number of wireless Network service providers increase day by day and the demand for radio spectrum put a question

mark on the traditional static channel allocation. Hence is the development of Cognitive radio network. Cognitive radio is the savior for the spectrum scarcity which uses the idle slots of licensed spectrum for unlicensed secondary transmission. But, they must vacate the spectrum as soon as the licensed holder starts transmitting again[1,2]. This intelligent radio requires a fast and accurate spectrum sensing at minimum cost and minimum complexity. Energy efficiency is another requirement

In cognitive radio network, there is always a tradeoff between reliable spectrum sensing and energy efficiency. Similarly, there is a tradeoff between reliable spectrum sensing and throughput. Reliable spectrum sensing not only depends upon the spectrum sensing methods, it also depends upon the number of samples taken and how long the channel is sensed. There are different spectrums sensing methods that include energy detection [3], cyclostationary detection [4] and matched filter detection. Energy detection is quick and simple, but not reliable under noise uncertainty. Cyclostationary and matched filter methods are reliable, but takes longer sensing time.

II. RELATED WORK For reliable spectrum sensing, two stage spectrum sensing has been suggested by many authors. [5] Proposed a two stage

spectrum sensing method. In his approach, the first stage is a coarse sensing in which a wide frequency range is sensed for spectrum holes and the suspected bands are energy sensed for confirming the free channel. [6] Proposed a two stage spectrum sensing in which energy sensing is used for coarse sensing and cyclostationary detection is used for fine sensing. In [7] W.Yue et all proposed a two stage spectrum sensing in which the first stage is energy detection based coarse sensing. Then it sort listed the channels based on the signal power. Channel with least signal power is detected with one order cyclostationary technique. Moreover, in all the above methods, decision on channel state is made only at the second stage. Hence, these methods increases the overall sensing time and reduces the spectrum throughput. Thus, these methods are concentrating only on the reliability and not on the energy efficiency, through put or reducing the system complexity.

In the proposed method, the detection time depends on the estimated noise power. If the noise power is low, pdf based spectrum sensing will be done. When the noise power is high, cyclostationary based detection or matched filter detection method can be used. In this paper, we discuss how to sense the spectrum using random samples.

III. FRAME WORK Consider a system with ‘m’ primary user and one secondary user. The secondary user is searching for a free channel out

of ‘m’ primary channels.

Let us assume that SU is sensing the primary channel 1. The received signal is,

r(t)=푛 (푡)푖푛푐푎푠푒표푓퐻

푥 (푡) + 푛 (푡)푖푛푐푎푠푒표푓퐻



퐻 indicates the absence of primary signal 퐻 indicates the presence of primary signal. 푥 (푡) is the primary signal 푛 (푡) is the noise interference.

All the primary channels will not have the same noise power. Each channel may suffer from different noise power. The path loss and the log normal shadowing will be different for each primary user. Primary user is a licensed holder whose signal would be a deterministic signal only. Hence, the signal power can be estimated from observation well in advance. Hence, from noise power of any instant, either a pdf based random sample detection or detailed cyclostationary detection may be used for spectrum sensing.

Sensing method=푝푑푓푏푎푠푒푑푟푎푛푑표푚푠푎푚푝푙푒푑푒푡푒푐푡푖표푛휎 < 휆푐푦푐푙표푠푡푎푡푖표푛푎푟푦푑푒푡푒푐푡푖표푛휎 > 휆

IV. PROBLEM ANALYSIS AND FORMULATION

In the general two stage spectrum sensing approach, the overall sensing time Ts is,

Ts=T1+T2

Here, T1 is the coarse sensing time and T2 is the fine sensing time. Moreover, energy detection is commonly used in the first stage of spectrum sensing. It is said to be the simplest spectrum sensing method. In energy detection, energy of ‘N’ samples is calculated. Hence, the sensing efficiency depends upon the number of samples taken for observation.

E=∑ 푥 (푛)

As per [8] , the detection time T is,

T=

푀 represents the number of samples taken for observation and W is the channel bandwidth. Hence, the detection time is directly proportional to the number of samples taken for sensing. If we could detect the signal even with reduced number of random samples, detection time as well as throughput and energy efficiency will also improve.

Feature detection methods make use of periodic or other signal characteristics for spectrum sensing. Structure of the signal can be found in the probability density function of samples collected randomly even without time reference [9] [ssp07]. The pdf of a signal is,

푝푑푓 =√

푒( )

xЄ(-∞,∞)

Fig1. Shows the block diagram of pdf based spectrum sensing. From the small set of randomly collected samples, high resolution histograms are obtained and then it is smoothed using window techniques. The resultant probability density function of a signal has the peak at the signal amplitude in case of single signal. If the received signal has more than one signal component, it will have peaks at the sum or difference of the signal components.

Random

samples of a signal

Fig 1.PDF based sensing

Fig.2 shows the pdf of a signal for different values of SNR. It performs well at high SNR. Hence the proposed system uses pdf based sensing when the SNR is high (Low noise power) and uses cyclostationary detection when the SNR is low.

Fine Resolution

Histograms

Smoothing filter Pdf Estimate



Fig 2. SNR Vs Probability Density Function

A. Throughput and Energy Efficiency:

In each time frame ‘T’ of a channel, cognitive radio is sensing the channel for ‘τ’ seconds. After confirming that the channel is free, the secondary user starts its transmission. If the sensing time is ‘τ’, then the useful time frame for secondary transmission is T-τ. If the sensing duration or mean detection time is longer, SU misses the opportunity to utilize the slot effectively. Thus, the spectrum utilization is reduced. If N out of M channels is sensed, then the overall spectrum utilization is reduced enormously and so is the throughput.

The overall throughput of the Cognitive radio Network is ,

Throughput = τ (퐶 1− 푃 (휂, 휏) 푃(퐻 ) + 퐶 1 − 푃 (휂, 휏) 푃(퐻 )

Here, 휂 is the sensing threshold, 푃(퐻 )& 푃(퐻 ) ar the probabilities that the primary use is busy or idle. 푃 and 푃 are detection and false alarm probabilities.

From the above expression, it is evident that the throughput varies proportionally with푇 − τ. If the sensing time is more, lesser is the useful time slot and so is the throughput.

Energy Efficiency=

Thus, the energy efficiency is improved with throughput and hence, it also depends upon the sensing time. Less sensing time results in improved energy efficiency. The proposed system uses pdf based sensing with high SNR (Low noise power) and uses cyclostationary detection for low SNR.

V. RESULTS AND DISCUSSION We simulated our approach using MATLAB and the performance is compared with that of energy detection based adaptive spectrum sensing method. Fig. 3 shows that the mean detection time is considerably reduced as the pdf based detection requires less number of samples than energy detection method. Similarly, the throughput is increased as shown in Fig.4



Fig 3. Mean Detection Time

Fig 4. Throughput Comparison

VI. CONCLUSION We proposed a novel pdf based adaptive spectrum sensing method for Cognitive radio. According to the estimated SNR either pdf based detection or cyclostationary detection is performed. Since the pdf based detection requires less number of samples than energy detection method, the mean detection time is reduced and throughput is increased.

ACKNOWLEDGEMENT [1]. J Mitola, GQ Maguire, Cognitive radio: making software radios more personal. IEEE personal Commun J. 6(4), 13–18

(1999). doi:10.1109/98.788210 [2]. S Haykin, Cognitive radio: brain-empowered wireless communications. IEEEJ Sel Areas Commun. 23(2), 201–220

(2005) [3]. D. Cabric, “Addressing the feasibility of cognitive radios,” IEEE Signal Processing Mag., vol. 25, no. 6, pp. 85–93,

Nov. 2008. [4]. Y. Zhuan, J. Grosspietsch and G. Memik, “Spectrum Sensing using Cyclostationary Spectrum Density for Cognitive

Radios”, IEEE workshop on Signal Processing Systems, Oct 2007, pp 1-6.



[5]. L. Luo, N. M. Neihart, S. Roy and D. J. Allstot, "A two-stage sensing technique for dynamic spectrum access," in IEEE Transactions on Wireless Communications, 3028-3037, Jun. 2009.

[6]. Minny Bhola et al., “Two Stage Spectrum Sensing for CognitiveRadio using Cyclostationarity detection and Energy Detection” , International Journal of Latest Trends in Engineering and Technology (IJLTET), volume 1 issue 4, November 2012

[7]. W. Yue, B. Zheng, Q. Meng and W. Yue, “Combined energy detection and one-order cyclostationary feature detection techniques in cognitive radio systems”, The Journal of China Universities of Posts and Telecommunications, vol.17, no.4, pp.18-25, 2010.

[8]. W Ejaz, NU Hasan, HS Kim, SNR-based adaptive spectrum sensing for cognitive radio networks. Int. J. Innov. Comput. Inf. Control 8(9), 6095–6106 (2012)

[9]. F. Boyle, J. Haupt, G. Fudge, and C. Yeh, “Detecting signal structure from randomly-sampled data,” Statistical Signal processing, pp. 326–330, 2007.

ISSN: 2349-2163 Probability Density ... Journal of Innovative Research in Advanced Engineering...

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