spectrum sensing using probabilistic neural … sensing using probabilistic neural networks in...

Spectrum Sensing using Probabilistic Neural Networks in Cognitive Radio

Network

Pavithra Roy P1 , Dr. Muralidhar M2

1 Assistant Professor, Dept. of ECE, Vemana Institute of Technology, Bangalore, India.

E-mail: [email protected]

2 Principal, Sri Venkateswara College of Engg. & Technology (Autonomous), Chittoor, India.

Abstract

Prediction of potential idle times of different channels based on the past information allows a cognitive radio to select

the best channels for control and data transmission. For a successful cognitive radio, learning is one of the most

important factors. Cognitive radio with enhanced Artificial Intelligence (AI) techniques leads to design and development

of algorithm that enable computer to learn. The learning mechanisms are capable of utilizing measurements sensed

from the environment, gathered experience and stored knowledge to improve the radio operation and to guide decisions

and actions. This paper presents the learning schemes that are based on artificial neural networks; in particular, Neural

Network (NN) and probabilistic neural network (PNN) . Based on the intelligent radio learning schemes, an effective

training approach is proposed. Simulations demonstrate that the novel technique beats existing hierarchical

approaches, and present better performance.

1. Introduction

Intelligence is needed to keep up with the rapid

evolution of wireless communications, especially in

terms of managing and allocating the scarce, radio

spectrum in the highly varying and different modern

environments. Cognitive radio systems promise to

handle this situation. Cognitive radio [1] is an

intelligent wireless communication system that is aware

of its environment, can also learn from experience and

can make changes to certain operating parameters (e.g.

transmit-power, carrier-frequency, and modulation

strategy) to adapt to the incoming RF stimuli in real-

time. The main objective of a cognitive radio is to have

a highly reliable communications with efficient

utilization radio spectrum in order to satisfy the user

needs [2].

A wireless network designed based on the cognitive

radio technology is referred to as a Cognitive Radio

Network (CRN). A CRN is composed of two types of

users, namely, the primary users (licensed users) and

the secondary users (unlicensed users). The primary

users have a higher priority than the secondary users in

accessing the channels in the licensed spectrum. In

most cases, the secondary users in a CRN logically

divide the channels allocated to the primary users into

slots [3]. Within each slot the secondary user has to

sense the primary user activity for a short duration and

accordingly accesses the slot when it is sensed idle. The

idle slots are also called spectrum holes or white

spaces. Thus the secondary users can access the

licensed spectrum without causing any harmful

interference to the primary users. Once the neural

networks are trained, the computational complexity is

significantly reduced.

Cognitive radio is a merge of AI techniques and

wireless communication [4]. Cognitive radio with

enhanced AI techniques is used in a rapidly changing

environment. AI field that concerned with the design

Pavithra Roy et al , International Journal of Computer Science & Communication Networks,Vol 5(2),120-127

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mailto:[email protected]

and development of algorithm enable the computer to

learn. It suits for the situation that based on the past

experience, learn by example and learn by analogy [5].

Reasoning and learning techniques is a fundamental

constraint on cognitive radio field. The capability to

improve is provided by an intelligent software package

called a cognitive engine [6]. The cognitive engine

derives and enforces decisions to the software-based

radio by continuously adjusting its parameters,

Observing and measuring the outcomes and taking

actions to move the radio toward some desired

operational state [7].

Cognitive radios are capable of learning lessons and

storing them into a knowledge base, from where they

may be retrieved, when needed, to assist future

decisions and actions. The integration of a learning

engine can be important especially for the channel

estimation, for improving the stability and reliability of

channel and evaluation of the configuration

capabilities, without relying solely on the recent

measurements. There are many different learning

techniques available that can be used by a cognitive

radio ranging from pure lookup tables to arbitrary

combinations of machine learning techniques that

include artificial neural networks, evolutionary/genetic

algorithms, reinforcement learning, hidden Markov

models, etc. In this paper we proposed two learning

schemes NN and PNN, different issues such as learning

algorithm, performance, efficiency and learning rate are

discussed. The paper is aimed at overcoming the

existing drawbacks faced by conventional neural

networks and also aimed to having improved sensing of

the channel.

Section 2 briefly presents the basic NN algorithm. The

main purpose of this section is to introduce notation

and concepts which are needed to describe the NN

algorithm. The PNN algorithm is then presented in

Section 3. In Section 4 the PNN algorithm is compared

with the NN algorithm and with a variable learning rate

variation of back propagation. Section 5 contains a

summary and conclusions.

2. NEURAL NETWORK (NN) ALGORTHIM

Neural networks are computational models of the

biological brain. Like the brain, a neural network

comprises a large number of interconnected neurons.

Each neuron is capable of performing a simple

computation [8]. Artificial Neural Networks (ANN or

simply NN) on the other hand, are made up of artificial

neurons interconnected to each other to form a

programming structure that mimics the behaviour and

neural processing (organization and learning) of

biological neurons. Neural networks are basically

composed of input, hidden layers, output parameters

and components which have ability to learn eventually

by tuning the weight functions of the parametric model.

The neurons are connected by weighted links passing

signal from one neuron to another. An intermediate

layer couples the input and output signals together,

propagating the input signals along a set of connections

from the input to the output [9]. The output signal is

transmitted through the neurons outgoing connection.

The outgoing connection will splits in a number of

branches that transmit the same signal. The outgoing

branches terminate at the incoming connections of

other neurons in the network.

Neural network is renowned for the ability of non-

linear reflection, and could simulate the input and

output of a complex system [10]. The advantage of

neural networks over statistical models is that it does

not require a priori knowledge of the underlying

distributions of the observed process. Therefore, the

neural networks offer an attractive choice for modelling

the predictor. Neural network has the ability to classify

information and for pattern recognition and has been

used to classify modulation types in signals with great

accuracy. It can understand how many other radios are

nearby, types of communication which is of great use

in determining the optimization [9].

A typical artificial neuron and the modelling of a

multilayered neural network are illustrated in Fig. 1.

Referring to Fig 1, x1... xn is considered to be

unidirectional inputs, which are indicated by arrows,

and the signal flows towards neuron‟s output (O). The

neuron output signal O is given by the following

relationship:


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Where wj is the weight vector, and the function f (net)

is referred to as an activation (transfer) function. The

variable „net‟ is defined as a scalar product of the

weight and input vectors,

Where „T‟ is the transpose of a matrix, and the output

value O is computed as

Where θ is called the threshold level; and this type of

node is called a linear threshold unit.

Fig. 1 Architecture of a Neural Network (NN)

Learning in NNs is achieved through particular training

algorithms which are expanded in accordance with the

learning laws, assumed to simulate the learning

mechanisms of biological system [11]. Neural network

has to be configured such that the application of a set of

inputs produces the desired set of outputs. This can be

achieved by properly adjusting the weights. The neuron

impulse is then computed as the weighted sum of the

input signals, transformed by the transfer function. This

process is called learning or training. The learning

capability of an artificial neuron is achieved by

adjusting the weights in accordance to the chosen

learning algorithm. Learning can be generally

distinguished between supervised and unsupervised

learning. In supervised learning, the neural network is

fed with teaching patterns and trained by letting it

change its weights according to some learning rule,

called back-propagation rule. It is a massively parallel,

interconnected network of neurons. The neurons are

organized into layers with no feedback or lateral

connections.

The training algorithm used in this work is the

Levenberg-Marquardt (LM) algorithm, which is a

variation of Newton‟s method that was designed for

minimizing functions that are sums of squares of

nonlinear functions.

3. PROBABILISTIC NEURAL NETWORK (PNN)

ARCHITECTURE

Probabilistic Neural Network (PNN), first proposed by

Donald F. Specht [12] is the nearest neighbour

classifiers to the complex domain neural network. This

network provides a general solution to pattern

classification problems, called Bayesian classifiers [13,

14]. PNN is a feed-forward neural network with a

complex structure and predominantly a classifier since

it can map any input pattern to a number of

classifications. PNN has become an effective tool for

solving many classification problems [15, 1, 16, 17,

and 18].

Network structure determination is an important issue

in pattern classification which is based on PNN. In this

study, a supervised network structure determination

algorithm is discussed. Despite its complexity, PNN

merely has a single training parameter. This is a

smoothing parameter of the Probability Density

Functions (PDFs) which are utilized for the activation

of the neurons in the pattern layer. Thereby, the training

process of PNN solely requires a single input-output

signal pass in order to compute network response..

The PNN [19] architecture is composed of many

interconnected processing units or neurons organized in

successive layers often used in classification problems.

When an input is present, the pattern layer computes

the distance from the input vector to the training input

vectors which produces a vector where its elements

indicate how close the input is to the training input. The

summation layer sums the contribution for each class of


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inputs and produces its net output as a vector of

probabilities. At last, compete transfer function on the

output of the third layer picks the maximum of these

probabilities, and produces a 1 (positive identification)

for that class and a 0 (negative identification) for non-

targeted classes in the output layer.

The layered structure of a PNN is as shown in the

Figure 2.

Fig.2 Architecture of PNN

In input layer, each neuron represents a predictor

variable. The input layer does not perform any

computation and simply distributes the input to the

neurons in the pattern layer. It standardizes the range of

the values by subtracting the median and dividing by

the inter quartile range and the input neurons feed the

values to each of the neurons in the pattern layer. The

pattern layer consists of K pools of pattern nodes. Each

node in the pattern layer is connected with every node

in the input layer. The kth pool in the pattern layer

contains Nk number of pattern nodes. On receiving a

pattern z from the input layer, the neuron of the pattern

layer computes its output given by

)4(2

)()(exp

)2(

1)(

22

ij

T

ij

ij

zzzzz

Where, ε denotes the dimension of the pattern vector Z,

σ is the smoothing parameter and Zij is the neuron

vector. Pattern layer contains one neuron for each case

in the training data set. It stores the values of the

predictor variables for the case along with the target

value. A hidden neuron computes the Euclidean

distance of the test case from the neuron‟s center point

and then applies the RBF kernel function using the

sigma values and produces a vector whose elements

indicate how close the input is to a training input.

Gaussian function is often chosen as the activation

function, combined with a radial basis function in the

pattern layer.

For PNN networks [19] there is one pattern neuron for

each category of the target variable. The actual target

category of each training case is stored with each

hidden neuron; the weighted value coming out of a

hidden neuron is fed only to the pattern neuron that

corresponds to the hidden neuron‟s category. The

pattern neurons add the values for the class they

represent. This is carried out in summation layer. The

output layer compares the weighted votes for each

target category accumulated in the pattern layer and

uses the largest vote to predict the target category.

The summation layer consists of K nodes; each

summation node receives the outputs from pattern

nodes associated with a given class .The summation

layer neurons compute the maximum likelihood of

pattern being classified into by summarizing and

averaging the output of all neurons that belong to the

same class given by

)5(2

)()(exp

1

)2(

1)(

22

ij

T

ij

i

i

zzzz

Nzp

Where, Ni denotes the total number of samples in class

Cli. If the priori probabilities for each class are the

same, and the losses associated with making an

incorrect decision for each class are the same, the

decision layer unit classifies the pattern in accordance

with the Bayes‟ decision rule based on the output of all

the summation layer neurons given by

C (x)

Pm (x)

Pi (x)

P1 (x)

Output

Input Layer

Pattern Layer

Summation

Layer

Decision

Layer

Xm1

Xin

Xi2

Xi1

X1n

X12

1 X11

Xm2

Xmn

NN

x


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)6(,...,2,1)},(max{arg)( xizpzlC ii

Where, )(zlC i denotes the estimated class of the

pattern and x is the total number of classes in the

training samples.

The chief advantages of PNN are high-speed training,

an inherently parallel structure, assured to converge to a

best possible classifier as the size of the representative

training set increases and training samples can be added

or removed without extensive retraining [20]. PNN

learns more quickly than many neural network models

and success on a variety of applications. On the other

hand, the main disadvantages are it is not as general as

back-propagation, requires large memory, slow in

execution of the network and requires a representative

training set compared to other types of NNs.

4. COMPARISION BETWEEN NN AND PNN

ARCHITECTURE RESULTS

The proposed work based on NNs, motivated by the

fact that NNs are widely different from conventional

information processing as they have the ability to learn

from the prior data, thus being also able to perform

better in cognitive tasks. Two NN based learning

schemes have been discussed here, the basic NN and

the PNN. While the former one aims at predicting

channel state by such learning schemes and apply them

into future cognitive radio based systems, the latter one

addresses that such a learning scheme should be

flexible in incorporating further information in the

learning process, which can be a objective merit to the

process. The proposed NN-based schemes concern the

channel state prediction of a specific radio

configuration through training process. In order to

proficient, the cognitive radio networks must be able to

select all the available channel states and to train

without exception. This could be implemented by

deploying multiple parallel training neural networks.

Furthermore, it must be noted that, the NN-based

channel prediction can be further fed with other

information that might crucially affect the throughput,

idle channel prediction for secondary user etc. This

could be subject of our research.

Learning is a continuous process during which, the

NN‟s parameters are adapted according to the

gravitational search algorithm .In order to increase the

validity and robustness, NNs should be deeply explored

especially taking into account more realistic input time-

series and environment situations. In conclusion,

cognitive radio systems have gained extremely high

attention from the wireless research world in the recent

years, as well as they will do in the upcoming years, we

advocate for implementing learning mechanisms like

the ones presented in this paper, to assist the cognitive

radios in deciding for their operating radio

configuration.

In this work, we presented an experimental evaluation

of the performance of PNN and NN. We performed a

comparative study of PNN and the NN learning

technique and, PNN outperformed from the evaluation

criteria adopted. This study presented a new method of

hybridizing of artificial neural networks with

gravitational search algorithm. Although the obtained

results have acceptable accuracy but increasing the

number of training data can improved the error

function. Future work can be focused on comparing the

effect of changing the type of neural networks or

optimization techniques, on minimizing the error

function.

Figures 3 to 5 gives the comparative plot for NN and

PNN. The comparison of our technique is made with

respect to HMM, LM based NN [21, 22, 23]. The

results show the throughput, SU and SUimp by varying

the number of channels from two to ten. Fig.3 shows

that the value of the throughput increases with the

number of channels. Three, five and nine channel states

have been considered, each with 100 time slots and the

figures are shown in 3a, 3b and 3c respectively. Fig. 4

shows that SU decrease with increase in number of

channels. We can observe that PNN technique has

achieved higher values for SU. Fig. 5 shows that the

SUimp increases with number of channels. Higher SU

and SUimp values show the effectiveness of the

technique. The highest SU and SUimp values achieved

by our technique are about 0.585 and 0.52 respectively.


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a) 3 Channels

b) 5 Channels

c) 9 Channels

Fig. 3 Throughput Performance with varying

channel states

Fig. 4 SU with varying channel states

Fig. 5 SUimp with varying channel states

5. SUMMARY AND CONCLUSIONS

Despite numerous research efforts in probabilistic

neural networks, there has been an improvement in

spectrum sensing in channel prediction is concerned. In

this study, a supervised PNN structure determination

algorithm has been proposed. We have chosen the PNN

because of its implementation simplicity and high

computational speed in the training stage, when

compared to others algorithms. A key feature of this

supervised learning algorithm is that the requirements

on the network size and training of the predictive

channels are directly incorporated in the methodology.

As a consequence, the proposed algorithm often leads

to a fairly effective network structure with satisfactory


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classification accuracy. This paper also introduces and

evaluates learning schemes that are based on NN and

can be used for discovering the performance (e.g.

throughput) that can be achieved in a cognitive radio

networks. So rate of channel prediction can be used to

determine the efficiency of the proposed training

algorithm. In order to design and use an appropriate

neural network structure, performance analysis has

been done in simulation environment using MATLAB.

The results have been compared and discussed in order

to show the benefits of artificial neural network based

learning schemes into cognitive radio systems.

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[15] You-En Lin, Kun-Hsing Liu, and Hung-Yun

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[21] Pavithra Roy P., and Muralidhar M., “Hidden

Markov Model based Channel state prediction in

Cognitive Radio Networks” International Journal of

Engineering Research & Technology, pp. 391-394,

Vol. 4, Issue 2, 2015.

[22] Pavithra Roy P., and Muralidhar M., “Throughput

Maximization in Cognitive Radio Networks using

Levenberg-Marquardt Algorithm", International

Journal of Advanced Research in Computer and

Communication Engineering, pp. 315-319, Vol. 4,

Issue 2, 2015.

[23] Pavithra Roy P and Muralidhar M., “Channel

State Prediction in a Cognitive Radio Network using

Neural Network Levenberg-Marquardt Algorithm”

International Journal of Wireless Communications and

Network Technologies, pp. 24-29, 4(2), 2015.

BIOGRAPHY

Pavithra Roy P. obtained her

Bachelor‟s degree in Electronics

and Communication Engineering

from Jawaharlal Nehru

Technological University,

Hyderabad, AP, India. Then she

obtained her Master‟s degree in

Electronics and Communication Engineering with

specialization in Digital Electronics and

Communication Systems from the Jawaharlal Nehru

Technological University, Anantapur, AP, India.

Currently, she is an Assistant Professor at the Faculty

of Electronics and Communication Engineering,

Vemana Institute of Technology, Bangalore, India. Her

specializations include Communication Systems, Logic

Design and Neural Networks. Her current research

interests are Wireless Communications, Cognitive

Radio Systems, and Spectrum Utilization.

Dr. Muralidhar M. (Muralidhar

Mahankali.) received B.E., M.E. and

Ph.D degrees from Sri Venkateswara

University, Tirupati, Andhra Pradesh,

India. He worked in different cadres in

the department of Electrical &

Electronics Engineering in Sri

Venkateswara University, Tirupati, Andhra Pradesh,

India from 1977 to 2011. Currently he is working as

Principal and Professor in EEE in Sri Venkateswara

College of Engineering & Technology (Autonomous),

Chittoor, Andhra Pradesh, India. His research interests

are Wireless sensor networks and heterogeneous

wireless networks. He is a senior Member of the

Institution of Engineers (India) and a Member of the

Chartered Engineers (India).


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spectrum sensing using probabilistic neural … sensing using probabilistic neural networks in...

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