Spectrum Sensing for Cognitive Radio Using Blind Source Separation

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unlicensed users to exploit the radio frequency spectrum by not ... Index Terms—Cognitive Radio (CR), Blind Spectrum Sensing. (BSS) .... Given CN i H, which is ..... 201 - 220. [3] Joseph Mitola III, “An Integrated Agent Architecture for Software.
2014 Fourth International Conference on Advanced Computing & Communication Technologies

Spectrum Sensing for Cognitive Radio Using Blind Source Separation and Hidden Markov Model Amrit Mukherjee1, Satyabrata Maiti2

Dr. Amlan Datta

Research Scholar1, M.Tech2 School of Electronics Engineering, KIIT University Bhubaneswar, India [email protected], [email protected]

Professor and Associate Dean School of Electronics Engineering, KIIT University Bhubaneswar, India

with primary users (PU), as they are having the right to use spectrum and thus must have a surety not to be interfered by secondary users. Fig.1 shows the basic cognition cycle model, which focuses on spectrum processing. The cognitive capability of the system to allows it to interact with the nearby surrounding environment, and results in selecting proper communication parameters for that specific environment.

Abstract— Most of the radio frequency spectrum is not being utilized efficiently. The utilization can be improved by including unlicensed users to exploit the radio frequency spectrum by not creating any interference to the primary users. For Cognitive Radio, the main issue is to sense and then identify all spectrum holes present in the environment. In this paper, we are proposing the Blind Source Separation (BSS) sensing which is applied through the Hidden Markov Model (HMM). It does not need any kind of synchronizing signals from the Primary user as well as with the secondary transmitter in a working condition. Simulation results by the proposed method for BSS by the activity of Primary User (PU) have been presented. Index Terms—Cognitive Radio (CR), Blind Spectrum Sensing (BSS), Primary User Activity Prediction, Hidden Markov Model (HMM), Channel state prediction

I.INTRODUCTION Wide applications of radio signals have resulted in obtaining a maximum usage of radio spectrum. The new factors coming into picture elaborate the need of spectrum management. Survey has shown about the actual utilization of spectrum, where it is clearly mentioned about the unused spectrum in the allocated range. These are reported by Federal Communications Commission (FCC)’s Spectrum Task Force. This introduced the need of Cognitive Radio (CR) with which we can improve the allocated spectrum efficiency. Here we can adequately use the unused spectrum in random and continuously changing environment. These involves in obtaining the different transmission attributes viz. power, latency, bandwidth of the signal, symbol rate of the different combination of the required potential users which depends upon the nature and behavior of the primary user. Spectrum sensing for cognitive radios is still an ongoing development and the techniques for the primary signal detection are limited in the present [1]. One of the most important and unique property of CR networks is the ability to shift and change between two different radio access technologies (ISM and Sensor networks) as idle and different frequency band slots arise [2]. This dynamic spectrum access which was proposed in [3] and is one of the most basic transmitter requirement to adapt to the criteria like varying quality of the channel, the available network congestion, channel to signal interference and service requirements of the channel. The secondary users of CR will also need to coexist 978-1-4799-4910-6/14 $31.00 © 2014 IEEE DOI 10.1109/ACCT.2014.63

Fig1. Cognitive Cycle

The involvement of Blind source separation (BSS) in cognitive radio systems is introduced. BSS spectrum sensing methods are proposed [5]. The advantage of using BSS is that these can work without any synchronization for the primary signal from the transmitter in presence of the secondary transmitter working in an active mode. The multi-frequency spectrum sensing is implemented to distinguish the combined and mixed signals which are present in different frequency band [7]. Another attempt is made to separate the mixed and combined observed signals with or without the presence of PU which are based the auto and cross correlation between that different separated signals. The Energy Detection Techniques are mainly divided into two categories: “Transmitter Detection” and “Receiver Detection”. In the first category, the Primary User (PU) is assumed to be transmitting and in the latter PU is receiving. In this paper we have emphasized on transmitter detection.

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Fig2. Spectrum Sensing

The main work for the CR users is to detect if any spectrum hole exists so that it can be used by any other secondary user which is shown in fig. 2 above and referring to spectrum sensing. The main challenge is all about the spectrum sensing in the channel(s) with the detection of primary users(PU’s) activity that whether these are present or not.

Fig3. Cognitive radio (CR) network architecture. The figure shows the CR network and the primary transmitter are using the same bandwidth available

As a binary hypothesis testing, the sensing is performed to ensure if any presence of the primary transmitter for a particular band of frequency and denoting them by:

II. CHARACTERISTICS OF SPECTRUM SENSING AND SPECTRUM SENSING MODEL

H0: The idle primary user H1: The working primary user

In [10], the spectrum sensing process and dynamic PU activity are well designed by a finite Markov chain. To predict the nature of the primary user, and Auto regressive( AR) algorithm is been implemented. Least square (LS) and minimum mean square error(MMSE) are the attributes of an auto regressive which are being predicted[12]. Based on binary time series , the prediction method predicts the primary user activity[13]. The HMM method is been implemented for the prediction of primary user activity. In the proposed designing, BSS is used to sense the spectrum holes and the primary user activity and parallel the secondary users are performing their actions (usage of the allocated spectrum band) . We are using the Hidden Markov Model for the primary user prediction by sensing and predicting the primary user’s next sensing frame. These sensing frames are different from those of primary user’s data frame. The proposed model works in the way that if the result of the prediction is showing the absence of primary user for the upcoming next sensing frame, then any one of the secondary user is allowed to occupy the channel and thus can send the data frame during the sensing which is coming next. With the process taking place, the other secondary user’s continuously senses the spectrum and with this the throughput of the cognitive radio network is becoming efficient since these includes the send only part of the data frame send initially. For an optimum and fast working of spectrum sensing which helps to avoid the interferences with the primary users and properly and fast detection of the white spaces which are present in the spectrum available for the proper and intelligent access by the secondary users.

The working primary user must be different with that of the idle primary user and thus from CR decision performed by spectrum sensing part. Here in this case, the presence of primary signal has been given by HPNi where i=0 is the absence and i=1 shows the presence. Given CN i H, which is sensing the presence or absence of the primary signal and making the decision on the basis of that hypothesis [14]. The following model structures are used [14] which defines the below conditional probabilities: And

Pm=P(HCN0|HPN1)

(1)

Pf =P(HCN1|HPN0)

(2)

As shown above in fig.3, the architecture implemented for the cognitive network and primary network. The base station of the cognitive radio network is acting as the main source which implements the cooperative sensing of the spectrum. Taking any one secondary user is then selected and allowed to send its data frame it is having when the primary user is not present and parallel the other secondary users acts as distributed sensors. The signal which is then received at a particular instance say jth is given by: yj = hj;1aPN +hj;2aCN +nj

(3)

where (4) aPN= [aPN1 aPN2 aPN3 ….. aPNL ], (5) cAN= [cPN1 cPN2 cPN3 ….. cPNL ], (6) nj= [nj,1 nj,2 nj,3 ……. nj,L] and where the channel between the primary user is denoted by hj;1 and the j-th secondary user and hj;2 is the channel between j-th secondary user and any other secondary user with setting

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channel property as Rayleigh distribution. In this case we are considering a particular quasi-static Rayleigh distribution in the channel model where the coefficient of the channel remains constant for the duration of time interval taking to sense one single frame. Simultaneously it changes from the previous sensed frame to another next frame. Here the time taken for the primary user data frame is much longer than the sensing frame, where nj is given as the zero mean circularly symmetric complex Gaussian (ZMCSCG) noise and having the distribution n j ~ CN (0;σ2nj IL) where IL is the L X L is the identity matrix. The vector aPN which is a primary network signal includes the primary user symbol transmission and aCN includes the transmitted symbols from any active secondary user. When the cognitive system is performing spectrum sensing ( HCN0 ), then we get the cognitive users sensed signals as follow:

Several cognitive radio sensing frames are being sensed by spectrum sensing units and the output of the sequence contains the observation which is essential by the algorithm which is predicting the next primary user state. In the Fig.4 below we can see the structure of the frames which are inside the cognitive radio sensing where n-1 are the total sensing frames which can be observed and form to predict the primary user state in the n-th sensing frame there. If the primary user is present that is indicated by the predictor and thus the active secondary user becomes idle and inactive in the n-th frame of sensing. It also indicates if the PU is absent, then the active secondary user becomes in active condition in the channel in n-th sensing frame in the structure.

(7) And with the absence of any operation of spectrum sensing from cognitive systems ( HCN1 ), the initial model leads to:

Fig.4. Sensing structure frame for cognitive radio

As we can see that the predictor unit first observes n-1 previous sensing frames so as to predict the next n-th senseing frames. In [9], as mentioned the CR network is sensing all the users so as to collect as the base station of CR network which is shown by matrix Y. Again, on the observations based which is Y, the process of spectrum sensing takes place in the sensing period of the n-1 frames.

(8)

The total received signal can be represented as matrix: Y = HA+N Where

III. SPECTRUM SENSING BASED ON BLIND SOURCE SEPARATION

The signaling scheme which is used by the PU, the channel path coefficients and the noise power, spectrum sensing techniques are divided into the following techniques: 1. Feature detection techniques: This algorithm applies the attributes of the primary user’s signal, noise and other channel parameters (for ex: channel fading coefficients etc). With more observation in PU’s, the channel and the noise are assumed so that an optimum performance can be achieved without making much complexity and using simple manner. 2. Blind detection algorithms: This algorithm needs a previous knowledge of the primary signal properties (e.g., noise power) or the channel information (e.g., channel fading coefficients). Spectrum sensing technique based on the BSS method is implemented. With this, we can differentiate between two or more dependent observation and two independent sources in a vector format and implementing the independent component analysis (ICA) algorithm [15]. Our main aim is to take out the original source of signal vector A, by selecting the best possible value for matrix W :

(10)

where y j;l is given by l-th observed symbol present in the j-th secondary user ( j = 1,2, ….,q) and q is given by the number of sencondary user that exist in the spectrum sensing process. . The matrix are given by : (11)

(12) where the channel coefficient between primary user and secondary users are given by hi j and the matrix N is given by :

(13)

(Received signal matrix) Y = WX Where Y is given by :

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(14)

(15)

Y is representing the best approximation with matrix A and (16) Fig.5. Illustration of channel state prediction.

As we can see in the figure below where a subfrequency band an observation value is obtained at the end of phase of spectrum sensing in each slot which is ot as per our proposed model. Certain channel is associated in selected and each slot for time for a sub frequency band along with channel is denoted active or passive. Practically it is very hard to find the state of the channel. Pin the proposed model, the state of the channel are assumed to be known and fixed by such verifications. The maximal mean of verification delay is given by Y in time slots. As shown in Fig. 5, the actual states, unknown states and the known value from observations are marked. Channel state are predicted by using known values after observations and the states of original channel for finding and predicting the future states of channel. Here in this case, HMM is improved from its standard given form.

Different metrics are used to make a decision like correlation metric, negentropy metric. These are used to measure the separated signal’s non gaussainity. These measured nongaussianity is then made comparison with the assumed threshold so that it can make the present decision about the primary signal in the frame within the sensing period. The decision made is then implemented to rest of frames that are sensed. IV. PREDICTION USING HIDDEN MARKOV MODEL The Hidden Markov model (HMM) (Simmons) is one of the Markov model which is having a hidden i.e the state which is unobservable. This model is a five tuple < S,A,O,T,Z,p >, in which S, A, and T with the Markov model are same and p is given as the distribution for initial state Also O which is a finite set and Z denotes the observation function given by: O×S×A→R, and Z(o, s, a) =p( o| s, a), that is given as the probability of O (receiving observation) when the system ends in S state which will give action a after execution In many applications, Z remains the same for all the values of a ( Z(o, si, aj)=Z(o, si, ak)). Implementing Z(o, s) as the substitute for the Z(o, s, a), given that Z remains constant for all a. In Markov model, when predicting the state of actions, the system state remains uncertain. In Markov model where as mentioned the state may also remain uncertain although the actions are executed. Thus with this drawback, we have selected Hidden Markov Model (HMM) where the observations and the transitions can be used to infer the distribution of the next state. In the Markov models, equations given are to predict the state distribution when the time t+1 occurs and providing the state distribution when time is t. Here the action at+1 .Also, ot+1 which shows the observation when time is t+1 are applied for further state distribution. P (s) t+1

p (s)Z(o , s, a )/ p(o ) t+1

t+1

t+1

Where p(o t+1) is given as p(ot+1) = Σ pt+1 (si)z(ot+1,si,at+1)

t+1

V. PROPOSED SCHEME: IMPLEMENTING HIDDEN MARKOV MODEL AND BSS SPECTRUM SENSING Implementation of HMM is specifically used for the prediction in the channel usage by the primary users and their corresponding behavior in frequency band [16]. These will let into conclusions that whether to shift to another band or remain same. Using HMM based pattern recognition, a channel state predictor is used [16]. The proposed paper collectively infers to an approach for channel Blind Source separation and based on modified new HMM. The modified HMM is defined the following two equations: (19)

(20)

Here X is given as the span of prediction which handles the maximal response delay possible in time slots. The different parameters of HMM (modified) {π,A,B}, are calculated statistically The following equations are shown for extracting the parameters of HMM (modified) by implementation of training sequence given as:

(17) (18)

which is the normalizing factor. The quantization can be of any type which includes vector and scalar quantization. Here we can process many number of sub-frequency bands simultaneously using HMM approach. Proposed model can be implemented to almost all type of wideband applications.

(21)

412

(22)

(23)

Where, L is the length of the above training sequence presented in the time slots, and (24)

Now with this the channel state X-Slot can be predicted with one step ahead as per the trained parameters { π,A,B} which uses two methods which are “π B” which uses π and B and other method is “AB” which uses A and B correspondingly for prediction. These are defined by following equations with T=1 1. Initialization : (25) 2.

Fig 6 .Performance of single secondary user

Taking into account the two above methods discussed “πB” and “AB” as the proposed prediction model. Now, when Y=0, in the second method “AB” which can have a higher performance in prediction in the measured multi band frequency applications. In some of the special cases Y>0, the method “AB”‘s performance may be degrade. The first method “πB” becomes a little less which is independent of “Y” and so it is better to use “πB” method as per as the HMM based prediction model is concerned for Y>0.

Iteration :

VI. SIMULATION AND DISCUSSION (26)

3.

The simulated results as performed is shown to differentiate the achieved with BSS(modified) and with standard classical methods of BSS sensing. As per the comparison one more secondary users predictor which is 1nearest neighbor i.e 1-NN which is selected as the reference and this is used for detection of channel state as sensed for the future prediction as shown by : (29) qt+X = qt here, as shown qt which is the channel state which helps in estimating in prediction in current time slot “t”. The term qt+1 is of X-slot for future channel state. qt is calculated as per following :

Termination : (27)

The other method, named “AB”, uses A and B for the prediction, which is defined by (26), (25) with T = 1. (28)

Using above either equation, the channel state which is predicted q*T and their respectively likelihood probabilities which is indicated by P* is calculated in (27). Now, using above the prediction of the performance is calculated by using two different metrics which are PD i.e. probability of detection and PFA i.e. probability of false alarm. In case of prediction, the probability of detection indicates the rate with which the prediction model predicts the state of the channel preciously when the channel state is “busy” and PFA denotes the rate of failing to predict the state of the channel correctly when the actual state of channel is “idle”. For a better prediction, the combination of lower PFA and higher PD is suggested.

(30)

where ot is the term showing non-quantified value from observation in the present time slot t and the threshold is th. Thus, with increase in little complex in nature of the proposed method for predicting the single secondary user which is practically optimum comparing channel conditions with 1-NN predictor.

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REFERENCES [1]

[2]

[3]

[4]

[5]

[6] Fig 7. Probability of false-alarm versus SNR .Here the constant of probability miss-detection is taken as 0:01 which are compared to with proposed model and classic BSS model

[7]

In Fig. 7, as we can see the fixed probability of miss detection i.e. Pm which is taken as 0.01 and determined PF ( Probability of false alarm) versus Signal to Noise ratio (SNR). The optimum throughput is achieved as shown by PF for the given cognitive radio networks with the constant value of Pm. Here, it is the imposed and tolerated interference to the primary user. As with the figure of comparison above, the proposed method is providing a lower chance of PF and thus indicates higher and optimum working for CR networks.

[8]

[9]

[10]

VII.CONCLUSION In the proposed method the demerit of the classical spectrum sensing (BSS) which includes non proper uses of the spectrum efficiently and not able to know the state of primary user. Thus both of this disadvantage is taken care of in the modified HMM and based on the primary user activity is sensed with BSS. In the paper, we are considering the PU which is signaling in a particular and predictable way and by our proposed method the prediction with BSS to which enhance the sensing accuracy and thus reducing the chance of interference for the primary network in cognitive radio system. Also the improve in a combing spectrum sensing method with BSS and detection modified HMM for PU.As shown in the graph for proposed model with classical BSS method and increasing sensing accurately. The overall model is modified with sensing of spectrum when the primary network and CR are working simultaneously.

[11]

[12]

[13]

[14]

[15]

[16]

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Tevfik Y ¨ucek, H ¨useyin Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications”, IEEE communications surveys & tutorials, vol. 11, no. 1, first quarter 2009,p. 116-130. Haykin S. “Cognitive radio: brain-empowered wireless communication”, IEEE Journal on Selected Areas in Communications, 2005, vol. 23, no. 2, p. 201 - 220. Joseph Mitola III, “An Integrated Agent Architecture for Software Defined Radio”, Dissertation, ISSN 1403-5286 ISRN KTH/IT/AVH— 00/01--SE Tafaghodi KhajavI, N., Sadeghi, S., Sadough, S. M. S, “An improved blind spectrum sensing technique for cognitive radio systems”, 5th International Symposium on Telecommunications (IST). Tehran (Iran), 2010, p. 13 - 17. Sadeghi Ivrigh, S., Sadough, S. M. S., Ghorashi, S. “A blind source separation technique for spectrum sensing in cognitive radio networks based on kurtosis metric”, International eConference on Computer and Knowledge Engineering (ICCKE).mashhad (Iran), 2011, p. 327 - 331. Navid Tafaghodi Khajavi, Siavash Sadeghi and Seyed MohammadSajad Sadough, “An improved blind spectrum sensing technique for cognitive radio systems”, 5th International Symposium on Telecommunications (IST'2010), p. 13-17. Liu, X., Tan, X., Anghuwo, A. A, “Spectrum detection of cognitive radio based on blind signal separation”, IEEE Youth conference on Information, Computing and Telecommunication. Beijing (China), p. 166 – 169. Xu, G., Lu, Y., He, J., Hu, N., “Primary users detect for multiple antenna cognitive radio based on blind source separation”, 8th International Symposium on Intelligent Information Technology workshops (IITAW). Shanghai (China), 2008, p. 777 - 780. Li, Y., Dong, Y. N., Zhang, H. Zhao, H. T. ; Shi, H. X. Zhao, X. X, “Spectrum usage prediction based on high-order markov model for cognitive radio networks”, IEEE 10th International Conference on Computer and Information Technology (CIT). Bradford (UK), 2010, p. 2784 - 2788. Tafaghodi Khajavi, N., Sadeghi Ivrigh, S., Sadough, S.M.S.,“ A novel framework for spectrum sensing in cognitive radio networks”, IEICE Transactions on Communications, 2011, vol. E9-B, no. 9, p. 2600 – 2609. C. Ghosh, C. Cordeiro, D. P. Agrawal, and M. B. Rao, “Markov chain existence and hidden Markov models in spectrum sensing,” Proceedings of IEEE International Conference on Pervasive Computing and Communications, 2009. Chunli, D., Yuning, D., Li, W, “Autoregressive channel prediction model for cognitive radio”, 5th International Conference on Wireless Communications, Networking and Mobile Computing, WiCom ’09. Beijing (China), 2009, p. 1 – 4. Yarkan, S., Arslan, H, “Binary time series approach to spectrum prediction for cognitive radio”, IEEE 66th Vehicular Technology Conference. Baltimore (MD, USA), 2007, p. 1563 - 1567. Chen, Z., Qiu, R. C., “Prediction of channel state for cognitive radio using higher-order hidden Markov model”, Proceedings of the IEEE SoutheastCon (SoutheastCon). Concord (NC, USA), 2010, p. 276 - 282. A. Hyvarinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. on Neural Networks, vol. 10, no. 3, 1999 p. 626–634. I. Akbar and W. Tranter, “Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case,” Proceedings of IEEE SoutheastCon, 2007, pp. 196–201.