Presentation Outline
 Introduction
 Spectrum sensing
 Schematic diagram of spectrum sensing.
 Development of the test statistics for the spectrum sensing in CR
 ONBC for spectrum sensing
 Optimizing the Bayesian parameters based on the Bat Bird Swarm Optimization
Algorithm (BBSA)
 Algorithms for BBSA
 Conclusion
 References
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ECE Dept. BIT Mesra, Ranchi
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Introduction
 Cognitive Radio(CR) technology is employed for ensuring access over the flexible
spectrum through the usage of the dynamic allocation schemes for spectrum.
 CR users are referred to as secondary users (SUs), and they engage in continuously
sensing the licensed users regarding the transmission.
 CR users can allocate only an unused portion of the spectrum. Therefore, these users
should monitor the available spectrum bands, capture their information and then
detect spectrum holes.
 Once the CRs detect few channels to be free, it is essential to estimate the channel
gain before the initiation of the data transmission.
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spectrum sensing
 spectrum sensing is done using the optimal naive Bayes classifier (ONBC) based on the
signal statistics, such as energy and likelihood ratio.
 The ONBC is developed by integrating the bat–bird swarm algorithm (BBSA) with the
naive Bayes classifier, which works based on the Bayesian concept.
 The BBSA is newly developed by integrating the bird swarm algorithm (BSA) and bat
algorithm. Finally, the channel estimation is done using the pilot-based sequential
procedure and least square estimation (LSE).
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Schematic diagram of spectrum sensing
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IFFT
CP Insertion
Parallel to series convertor
Assigning the sub-carrier
Serial parallel Convertor
Data
Channel
Pilot based sequential
procedure and least
square error (LSE)
Spectral sensing
Bird Swam
Algorithm (BSA)
BAT Algorithm
BAT Bird Swam
Algorithm (BBSA)
Optimal Naïve Bayes
Classifier (ONBC)
series to parallel convertor
FFT
CP Removal
Channel Estimation
Detection
Parallel to series convertor
Data

Development of the test statistics for the spectrum sensing in CR
 The desired data is represented as M. The data sample matrix is denoted as
𝐷 = 𝐽 ∗ 𝑀 + 𝑁
 where J and N are the channel gain and thermal noise matrix. The channel matrix is given as:
𝐽 = 𝐽 𝑢𝑣 𝑣×𝑢
where Jub is the channel gain between the uth sensor and bth transmitter.
 Notation for the thermal noise matrix is given as:
𝑁 = 𝑁 𝑢𝑣 𝑞×𝑦
 The ensemble covariance matrix corresponding to the received signal is given as :
𝑉 = 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝐷 𝐷+
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Continued..
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 matrix is modified using the maximum likelihood estimate (MLE) from which the sample is denoted as
𝑉 =
1
𝑦
𝐷 𝐷+
Once the covariance matrix C is computed, the energy of the signal and the eigen statistics are
determined, which are given as input to the ONGC model:
 The eigenvalue of V is computed and is represented as:
𝑒1 ≤ 𝑒2 ≤ 𝑒3 ≤ ⋯ 𝑒 𝑞
 Thus, the energy statistics is represented as:
𝑒 =
𝑒1
1
𝑞 𝑢=1
𝑞
𝑒 𝑢
 where eu is the eigenvalue of the uth sensor. The eigen statistics is found using the formula as:
𝜀 =
𝑒1
1
𝑝 × 𝑘2 𝑢=1
𝑞
𝑒 𝑢

ONBC for spectrum sensing
 The energy statistics and the maximum likelihood of the received signal are employed for
predicting the channel occupancy, which is progressed using the BBSA based NB
classifier.
 Let us represent the input of the optimal NB classifier as :
𝐎 = 𝑂𝑁𝐵 𝜖 , 𝑒
 The Bayesian rule is based on the posterior probability, which is computed based on the
likelihood, class prior probability and prior probability of the class. Accordingly, the
probability measure is given as:
𝑃𝑜𝑠𝑡 = arg max 𝑃(𝑐 𝑟)
𝑣=1
𝑦
𝑃(𝑓𝑣||𝑐 𝑟)
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Continued…
 The probability of the vth signal characteristics with respect to the class is computed as:
 where σ2 refers to the variance of y features from the signal and μr corresponds to the
mean of y
 The computation process of standard NB is time consuming and may cause computational
error.
 Thus, to provide the effective solution, introduces an optimization algorithm, BBSA, that
aims at tuning the NB parameters optimally
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Optimizing the Bayesian parameters based on the Bat
Bird Swarm Optimization Algorithm(BBSA)
 The ultimate aim of BBSA optimization is to tune the Bayesian parameters to derive the class
in order to decide the availability of the spectrum before the allocation of the band to any SU.
 It is based on the social behaviour of birds that follow some idealistic rules that follow: the
individual bird switches between the vigilance behaviour and foraging behaviour of birds.
 When foraging is in progress, the individual bird records and updates the previous experience
and their position, and in addition, the best experience of the swarms is updated, which is
regarding the location of food.
 In case of the vigilance behaviour, the individual birds move towards the centre of the swarm.
The vigilance behaviour is affected when there is a possibility of the interference. At the same
time, the birds switches between scrounging and producing when birds are trying to fly from
one site to another.
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Continued…
 So here
Secondary User is Bird
Spectrum is Food
1. Initialization In the first step, the parameters of the optimization including the
population are initialized, which includes Wg,h; (1 ≤ g ≤ y), where y is the population
size, ζ max as the maximal iteration, Pro as the probability of foraging food and fr is
the frequency of flight behaviour of birds.
2. Evaluate the fitness The fitness of the solutions is evaluated, and the solution
acquiring the better value of the fitness is declared as the effective solution. Thus,
initially, the solutions are randomly initialized and then updated at the end of
each iteration based on the probability.
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Continued…
3. Position update of the birds For updating their positions, the birds have three
stages, which is decided based on the probability Pro. Whenever the random
number R(0, 1) < Pro, then the update is based on the foraging behaviour or else the
vigilance behaviour commences. On the other hand, swarm is split as scroungers and
producers, which is modelled as flight behaviours.
4. Foraging behaviour of the birds: The individual bird searches for the food based on
their own experience and swarm behaviour, The standard equation modelling the
foraging behaviour of the birds is given by
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Continued…
5. Vigilance behaviour of birds: In the vigilance behaviour, the bird moves towards the
centre of the swarm and compete among each other, the vigilance behaviour is
modified using the bat optimization. The standard equation of BSA in the vigilance
behaviour of birds is given as
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Continued…
6. Flight behaviour: This behaviour of the bird progresses when the birds fly to another
site in case of any threatening events and foraging mechanisms. When the birds reach
a new site, they forage for food. Few birds in the group try acting as producers and few
as scroungers. The behaviour is modelled as
where Rr(0, 1) specifies the Gaussian distributed random number with zero-mean and one-
standard deviation.
7. Check the feasibility of the solution: The fitness of the best solution in the current
iteration is compared with that of the previous solution and retained in case of the best
fitness.
8. Termination: The steps are repeated for the maximal number of iterations. Thus, the
solution from BBSA is the optimal value of the Bayesian parameters such that ONBC
performs the optimal sensing in CR. 12/14/2020
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Parameters: y→population size; τ max→ maximal iteration,
Pr o→probability of foraging food, fr→frequency of flight
behaviour of birds
1. Initialization
2. Read the parameters
3. Determine the fitness of the solutions
4. While τ < τ max
5. For g = 1: y
6. If R (0, 1) < Pr o
7. Foraging behaviour using equation (22)
8. Else
9. Vigilance behaviour using equation (29)
10. EndIF
11. End for
12. Else
13. Split the swarm as scroungers and producers
14. For g = 1: y
15. If g is a producer
16. Update using Equation (30)
17. Else
18. Update using Equation (31)
19. EndIF
20. End for
21. Check the feasibility of the solutions
22. Return the best solution
23. τ = τ + 1
24. EndWhile
Algorithm:PseudocodeofBBSAoptimization
Proposed BBSA optimization
Input: bird swarm population Wg,h; (1 ≤ g ≤ y

Conclusion:
 The Bayesian-Based Spectrum Sensing for MAC-layer protocol in CR Networks is
done. The spectrum sensing is done based on the ONBC that uses the signal
statistics, like energy and likelihood ratio for which the proposed BBSA optimization
is used with the naive Bayes classifier. The proposed ONBC obtains the Bayesian
parameters of the naive Bayes classifier.
12/14/2020ECE Dept. BIT Mesra, Ranchi
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References
[1].Tsiropoulos, G.I., Dobre, O.A., Ahmed, M.H. and Baddour, K.E. (2016) Radio resource
allocation techniques for efficient spectrum access in cognitive radio networks. IEEE
Commun. Surv. Tut., 18, 824–847.
[2]. Zhao, Q. and Swami, A. (2007) A survey of dynamic spectrum access: Signal processing
and networking perspectives. IEEE international conference on Acoustics, speech and signal
processing, ICASSP, 4, IV–1349.
[3] Akyildiz, F., Lee, W.-Y., Vuran, M.C. and Mohanty, S. (2008) A survey onspectrum
management in cognitive radio networks. IEEE Commun. Mag., 46, 40–48.
[4]. Akyildiz, I.F., Lee, W.-Y., Vuran, M.C. and Mohanty, S. (2006) NeXt generation/dynamic
spectrum access/cognitive radio wireless networks: A survey. Comput. Netw., 50, 2127–2159.
[5] Maity, S.P., Chatterjee, S. and Acharya, T. (2016) On optimal fuzzy c-means clustering for
energy efficient cooperative spectrum sensing in cognitive radio networks. Digital Signal
Process., 49, 104–115.
[5]. Jemish V Maisuria,and Saurabh N Mehta, Bayesian-Based Spectrum Sensing and Optimal
Channel Estimation for MAC Layer Protocol in Cognitive Radio Sensor Networks
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Spectrum Sensing in Cognitive Radio Network