Channel Availability Prediction in Cognitive Radio ... · PDF fileChannel availability...

Contemporary Engineering Sciences, Vol. 10, 2017, no. 12, 593 - 605

HIKARI Ltd, www.m-hikari.com

https://doi.org/10.12988/ces.2017.7758

Channel Availability Prediction in Cognitive Radio

Networks Using Naive Bayes

Hans Marquez

Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

Cesar Hernández


Diego Giral


Copyright © 2017 Hans Marquez, Cesar Hernández and Diego Giral. This article is distributed

under the Creative Commons Attribution License, which permits unrestricted use, distribution, and

reproduction in any medium, provided the original work is properly cited.

Abstract

This work develops a channel availability predictive model that allows taking

advantage of opportunities in the cognitive radio network’s spectrum in the most

efficient way. The developed scheme creates an availability prediction matrix for

each available channel in the GSM band, which is used to determine the potential

bandwidth and availability time slots to optimize the channel allocation policies.

The allocation model contains a training process in charge of preparing the

prediction algorithm so it can make predictions that are more reliable.

Additionally, the prediction procedure uses the Naïve Bayes algorithm to estimate

the availability in each available frequency channel. This facilitates broadcasting

for secondary users. Measurements were computed for the average bandwidth, the

average delay and the prediction error. The obtained results were assessed with

real spectral occupation data in the GSM frequency band. The developed model

shows a low prediction error, which leads to establishing optimal channel

allocation mechanisms hence minimizing the failed handoffs due to channel

occupation by primary users.

Keywords: availability, cognitive radio, naïve Bayes, prediction

594 Hans Marquez et al.

1. Introduction

Currently, one of the most difficult tasks in the communications sector in general

is the fixed allocation of the spectrum, which has caused problems in the optimal

use of the spectrum going through excessively used areas to areas without

extensive use [1]. This has given birth to the study of cognitive radio networks

(CRN) that would allow passing from a rigid model for fixed allocation to one

that performs a flexible use of the spectrum hence optimizing the scarce resources

within wireless networks [2]. This flexibility would improve the service quality of

wireless technologies since an opportunistic use of the spectrum would be more

efficient. [3].

When the spectrum is not under use by a primary user (PU), secondary users

could take these spectral opportunities. However, such opportunities must be

relinquished once the PU requires them [4]. This restriction forces the SU to move

on to another spectral opportunity that is free. The described process is named

spectral handoff. [2].

To minimize the interference for the PU, the handoffs carried out by the SU must

be performed before the PU arrives so that the PU’s transmissions are not greatly

affected by the SU’s spectral opportunities [2]. To solve this problem, a predictive

model is proposed to foresee spectral occupation, the probability of a PU’s arrival

and optimizing the channel allocation process according to the previous metrics.

[5].

This article is structured as follows: section 2 presents related work, section 3

describes the development of the model, section 4 includes all the results obtained

and section 5 finishes off with a set of conclusions.

2. Related Work

In [6] a two-stage cognitive process is proposed aiming to learn from the abilities

of the physical layer under different channel conditions optimizing the package

delivery in a multi-radio antenna. In the first stage, it learns the features of the

available techniques. In the second stage, it defines the settings that most satisfy

the radio’s goals according to the channel’s conditions. A design based on the

Bayes rule will be the baseline for future comparisons. This work also studies the

Naïve Bayes, Semi-Naïve Bayes and binary search models that offer proper

learning and optimization alternatives for the reference design, requiring the

estimation of less parameters. Nevertheless, performance optimization is

sacrificed to increase speed and save memory.

In [7], cognitive engines in wireless networks are studied for environments that

require that the system dynamically selects a channel to transmit. Although the

different selection strategies focused towards a wide range of networks may be

Channel availability prediction in cognitive radio networks 595

organized in a smaller group of simple strategies, a universal DSA network

simulator is used to model the actions of a DSA network with these simplified

strategies. The channel selection is also described with the purpose of establishing

the behavior of secondary users, specifically those that have started transmitting.

This project centers in the estimation of the low-cost channels’ selection since the

adopted methods only require characteristics and monitoring tools already present

in a DSA network.

In [8], the dependency existing in cognitive radio networks in comparison to the

capabilities of artificial intelligence to perform a variety of tasks. An example of

these tasks is the self-organization and the mitigation of interference. One of the

objectives of the cognitive radio applications is to trying to decentralize and

distribute network functions between all participating nodes. Consequently, any

network node must be capable of coordinating functions between its peers and

unknown transmitters. Therefore, it is suitable that the individual nodes are able to

predict the future states of the unknown transmitters with the purpose of

optimizing its operation. Methods are studied in this article to identify the

cognitive behavior in an unknown transmitter and the prediction of future states.

The problem of the Universal DSA Network Simulation (UDNS) context is

discussed and two classification algorithms are presented to achieve this.

In [9] it is indicated that the detection of the spectrum is an effective method to

improve the performance of the availability detection. In spite of this

improvement, the authors indicate that the cooperation between cognitive radio

users may introduce a variety of general expenses such as additional detection

time, delays, energy consumption that affect or limit the reachable cooperative

gain. As a solution, they propose a learning algorithm based on a fusion algorithm

that can offer real time training. It is rooted under a learning machine that uses

statistical tests for energy detection to determine to the presence or absence of the

PU transmission and predict new statistical frames with this information. A

comparative simulation of four supervised machine-learning classifiers is detailed

over 1000 test frames: K-nearest neighbor (KNN), support vector machine

(SVM), Naïve Bayes (NB) and Decision Tree (DT). They are previously trained

with a set of 1000 frames. It is shown that KNN and DT surpass the other two

classifiers.

In [10], the problem of accessing the channeled dynamic spectrum is considered.

There are M secondary users with N ≥ M orthogonal channels. Therefore, each

user requires a channel to transmit so to guarantee that it will not conflict with the

channels assigned to other users. Nevertheless, due to geographic dispersion, each

secondary user can observe multiple occupation behaviors by primary users in

each channel. A new algorithm is presented with polynomial storage and

computation for this problem and it is discussed how the results offer a non-trivial

generalization of theoretical knowledge.

In [11], a stable trajectory selection scheme for cognitive radio sensor networks is

described. The routing protocol uses a priori delivery information and gives a

novel routing metric is derived from the Naïve Bayes interference.


The metric is then used to establish the stability of the trajectory considering the

delivery probability. Extensive simulations show that the proposed routing

protocol is efficient to find the most likely delivery route and increase the

system’s performance.

In [12], a modulation classification algorithm is designed for MIMO systems

based on the pattern recognition focus. This is performed using higher-order

statistics (HOS) and a Bayesian network classifier. With the means of assessing

the efficacy of the Bayesian network methods, a comparative study is carried out

between these three classifiers: Naïve Bayes with discretization (NBD), the

augmented Naïve Bayes tree (TAN) and the decision tree (J48). The functional

characteristics of the receiver (ROC), the identification probability and the

training time show that the NBD and TAN classifiers reach a performance similar

to the J48 classifier. Hence, these classifiers can be used to distinguish between

different types of M-ary modulation allowing a better monitoring of the signals

intercepted in the wide band technologies with low complexity.

3. Proposed Prediction Model

The proposed prediction model can be seen in Figure 1. The first block called

“Occupation database of the spectrum” includes real occupation data of the

spectrum corresponding to the GSM band (824 MHz – 849 MHz). This block is

the entry point for spectrum data processing that is in charge of defining the

occupation or availability of each channel in the GSM band according to the false

alarm probability equation.

The rectangular area corresponds to the proposed model, which includes two

algorithms: (1) Naïve Bayes algorithm, (2) Channel allocation prediction. The

first one’s function is to train the algorithm for a 10-minute period by using

training variables or availability probability and the average time of availability.

The purpose is to compute the parameters cost and gradient, which are necessary

to adjust the predictor. The second algorithm assigns the channel occupation

through the assignment of “1” and “0” which outputs an availability prediction

matrix.

Figure 1. General structure of the proposed model


3.1 Defining the Simulation Time and the Training Occupation Data

The model assigns the resources according to the existing demand considering the

existing time intervals for simulation. Each time interval lasts 300 milliseconds

and the total simulation time is 10 minutes.

Additionally, the model has a spectral occupation data matrix with input data for

the GSM frequency bands that mark a significant difference in terms of the

measured traffic at different time intervals. Such differentiation refers to the

information and occupation volumes that are shown in each channel, i.e. low and

high traffic data. The assessment of the model is obtained through this

differentiation over two different scenarios with their own characteristics.

3.2 Naïve Bayes Algorithm

One of the main considerations for the selection of the prediction model is that it

has multiple characteristics or criteria that can improve prediction. This is

explained due to the training of the prediction model may take into account

information or criteria such as the availability probability (AP) and the average

availability time (AAT), among other metrics, that can enhance prediction.

According to the previous paragraph and considering the Naïve Bayes theorem, it

can be stated that the independent variables (or predictors in the specific case)

would be the availability probability and the average availability time whereas the

dependent variable will be the channel availability. According to this, the Naïve

Bayes prediction model works fine on predicting various classes and assumes the

independence between them.

In simple terms, a Naïve Bayes classifier assumes that the presence of one

characteristic in particular does not relate in any way with the presence of any

other characteristic. Even if one these characteristics depend from each other or

the existence or other characteristics, all these properties contribute independently.

One of the advantages is the utility to operate over large datasets and even surpass

highly sophisticated classification methods.

The Bayes theorem allows the calculation of the posterior probability P (c | x),

P(c), P(x) and P (x | c) in equation (1).

𝑃(𝑐|𝑥) =𝑃(𝑥|𝑐)𝑃(𝑐)

𝑃(𝑥) (1)

Here,

P (c|x) is the posterior probability of class c (c, target) given the predictor (x,

attributes)

P (c) is the previous probability of the class


P (x|c) is the predictor’s probability given the class

P (x) is the predictor’s probability

According to equation (1) and considering our independent variables AP and

AAT as it was described in previous paragraphs as well as the dependent variable

or class (in the specific case, it is the channel availability that will be denoted as

occupied or available), we have equations (2) and (3).

𝑝𝑜𝑠𝑡𝑒𝑟𝑖𝑜𝑟(𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑) =𝑃(𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑)𝑝(𝑇𝐸𝐷|𝑜𝑐𝑢𝑝𝑝𝑖𝑒𝑑)𝑝(𝑃𝐷|𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑)

𝑒𝑣𝑖𝑑𝑒𝑛𝑐𝑒

(2)

𝑝𝑜𝑠𝑡𝑒𝑟𝑖𝑜𝑟(𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒) =𝑃(𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒)𝑝(𝑇𝐸𝐷|𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒)𝑝(𝑃𝐷|𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒)

𝑒𝑣𝑖𝑑𝑒𝑛𝑐𝑒

(3)

The evidence would be given by equation (4).

𝑒𝑣𝑖𝑑𝑒𝑛𝑐𝑒 = 𝑃(𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑)𝑝(𝑇𝐸𝐷|𝑜𝑐𝑢𝑝𝑝𝑖𝑒𝑑)𝑝(𝑃𝐷|𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑)

+ 𝑃(𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒)𝑝(𝑇𝐸𝐷|𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒)𝑝(𝑃𝐷|𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒) (4)

3.3 Channel Allocation Prediction

In this block, the algorithm uses the probabilities generated through the Naïve

Bayes algorithm and the prediction function takes into account the probability

distributions shown in equation (1), and predicts the availability per channel

during simulation time. This process is iterative since 551 channels were

processed for availability prediction. Therefore, the algorithm has to predict the

availability for each one of them.

3.4 Design Model

The proposed model has for input a spectral occupation training matrix. Before it

is used in the predictor’s training process, the spectral information passes through

the spectral information-processing block, which converts the data into

dichotomous series where a “0” represents the occupation of the channel and a “1”

represents the availability of the channel to train the Naïve Bayes algorithm.

The variables AP and AAT that contain the necessary data to establish the

occupation/availability probabilities of the channel are now considered. During

the training process, two matrixes are built from the channel’s AP and AAT and

along with the predict function, each testing data row is classified into one of the

classes (occupied, available) according to the Naïve Bayes classifier and returns

the foreseen class level. These probabilities are assigned to a matrix called

Availability Prediction where the states of the channel are defined by “1”

(available) and “0” (occupied).


Once the prediction matrix is created, it is possible to compare the precision of the

channel allocation during the transmission time and assess whether the availability

predictions can lead to a beneficial use of the channels by establishing a more

effective allocation policy.

4. Results

To assess the performance of the proposed algorithm, a complete analysis was

carried out with two types of traffic: high and low traffic. For each type, eight

figures were designed that correspond to the metrics: anticipated handoffs, perfect

handoffs, failed handoffs, successful handoffs, average bandwidth, average delay,

interference handoff, average throughput.

Figures 2 through 9 show the obtained results for every metric previously

mentioned, for both high and low traffic.

4.1 Results Analysis

According to the results, the number of anticipated handoffs in high traffic is

slightly lower than in low traffic supposing a better performance in the spectrum’s

high occupation. This connects with the fact that the algorithm has a more

efficient use of the allocation time before it has to abandon the spectral

opportunity. This also validates the results for the number of perfect handoffs (see

Figures 2 and 3).

Figure 2. Number of anticipated handoffs


Figure 3. Number of perfect handoffs

Figure 4. Number of failed handoffs

Although the number of accumulated handoffs during simulation was relatively

high for both scenarios, this cost is compensated by the fact that the number of

handoffs with interference was very low in comparison (see Figures 5 and 6).


Figure 5. Number of successful handoffs

Figura 6. Number of handoffs with interference

In both scenarios, the behavior of the bandwidth and throughput was relatively

constant (see Figures 7 and 9) thanks to taking advantage of the maximum

channel transmission time according to the prediction performed by the algorithm.

In addition, the delay in both cases was not very significant (see Figure 8) due to

profiting more efficiently from the spectral opportunities.


Figure 7. Average bandwidth

Figure 8. Accumulated average delay

Figure 9. Average throughput


5. Conclusiones

It is concluded that the number of failed handoffs increased in high traffic

scenarios because the algorithm prioritizes the fact of maintaining a low number

of handoffs with interference to avoid the degradation of the transmission for the

network’s primary users. In spite of this increase, the algorithm tries to

compensate this cost with the number of perfect handoffs to guarantee a higher

transmission time for secondary users. The algorithm’s availability prediction is

validated with an increase in the number of perfect handoffs, a stable management

of the bandwidth allocation during transmission time for both traffic scenarios

and, consequently, a stable behavior in the throughput and the number of perfect

handoffs, which confirm that the allocation was effective as well as the

transmission time for each secondary user.

Acknowledgements. The authors would like to thank the Universidad Distrital

Francisco José de Caldas for the support given during the research process of this

project.

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Received: July 26, 2017; Published: August 21, 2017

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