Open access peer-reviewed chapter

Machine Learning Approaches for Spectrum Management in Cognitive Radio Networks

By Ahmed Mohammed Mikaeil

Submitted: June 10th 2017Reviewed: January 29th 2018Published: March 28th 2018

DOI: 10.5772/intechopen.74599

Downloaded: 1207


Cognitive radio (CR) provides a better way for utilization of spectrum resource by introducing an opportunistic usage of the frequency bands that are not heavily occupied by a licensed spectrum user or a primary user (PU). In cognitive radio, the detection and estimation of PU channel availability (unoccupied spectrum) are the key challenges that need to be overcome in order to prevent the interference with licensed spectrum user and improve spectrum resource utilization efficiency. This chapter focuses on developing new ways for detecting and estimating primary user channel availability based on machine-learning (ML) techniques.


  • machine learning
  • spectrum sensing
  • spectrum management
  • channel state estimation
  • cognitive radio

1. Introduction

In this chapter, we study the problem of detection of unoccupied primary user spectrum (i.e., spectrum hole). We also introduce the methods for estimating the time when primary user channel state is available, so that the secondary spectrum user can adjust their transmission strategies accordingly.

The chapter is organized in two parts. The first part of the chapter focuses on the problem of detecting the unoccupied spectrum left by the primary user. In this part, we introduce the usage of machine-learning (ML) techniques as a fusion algorithm in cooperative spectrum sensing based on energy detector [1, 2]. In particular, we train a machine-learning classifier (i.e., K-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), and Decision tree (DT)) over a set containing energy test statistics of PU channel frames along with their corresponding decisions about the presence or absence of PU transmission in the channel. Then, we use the trained classifier to predict the decisions for newly unseen PU channel frames [3]. The second part focuses on estimating the near future of PU channel state. In the literature, there are many proposals that have studied the problem of estimating PU channel state in cognitive radio (CR) [4, 5, 6]. However, most of these studies focused on predicting PU channel state in frequency domain by converting the received digital signals into frequency domain using fast Fourier transform (FFT). This increases the system complexity due to the FFT computations process. In the second part of the chapter, we introduce a new time-domain approach for PU channel state prediction based on time series prediction with some machine-learning prediction model. In particular, a time series is used to capture PU channel state detection sequence (PU channel “idle” or “occupied”) in time domain. Then, prediction models such as the hidden Markov model (HMM) and Markov switching model (MSM) are used to predict the behavior of the time series that used capture PU channel state [7].


2. Machine-learning fusion-based cooperative spectrum sensing

In this part, we, first, define the system model for energy detection-based spectrum sensing; then, we present the method of calculating the thresholds for energy detector with different fusion rules. Second, we formulate a machine-learning classification problem and present four machine-learning classifiers to solve it. Then, we evaluate the performance of these classifiers with simulation experiments.

2.1. Energy detection-based cooperative spectrum sensing

Figure 1 shows a block diagram of the system model used for energy detection cooperative spectrum sensing based on machine-learning fusion rule. In this model, we consider a cooperative CR network with Kcooperative nodes. Each node uses Nsamples for energy detection, while Mframesare used for training the machine-learning (ML) classifier. The received signal of ithframe at the jthcooperative node yijn,1nN,1iM, 1jKis given by


where sijnis the PU signal which is assumed to follow Gaussian i.i.d random process (i.e., zero mean and σs2variance), wijnis the noise which is also assumed to follow Gaussian i.i.d random process (zero mean and σu2variance) because sijnand wijnare independent. Due to the fact that all K nodes are sensing the same frame at a given time, the global decision about PU channel availability will be made at the fusion center only. Thus, the energy statistic for the ithframe at the jthcooperative node Yijcan be represented by the energy test statistic of the ithframe at the fusion center which is given by


Figure 1.

Block diagram of machine-learning-based fusion rule spectrum sensing.

Yiis a random variable that has chi-square distribution probability density function (2Ndegrees of freedom for complex value and with Ndegrees of freedom for real value case). If we assume that the channel remains unchanged during the observation interval and there are enough number of samples observed N200[8], then we can approximate Yiusing Gaussian distribution as follows:


where σij2, is the standard deviation of noise samples wijn, and γijis the observed signal-to-noise ratio (SNR) of the ithframe sensed at the jth cooperative node. Assuming that the noise variance and the SNR at the node remain unchanged for all Mframes, then γij=γjand σij2=σj2. For a chosen threshold λjfor each frame in the probability of the false alarm, Pfas given in [9] can be written as


and the probability of detection Pdis given by


where the complementary distribution function of Gaussian distribution with zero mean and unit variance. To obtain the optimal threshold λfor Kcooperative sensing nodes, data fusion rules are used. The calculation of the thresholds for single user and other fusion rules is presented in subsections 2.1.1 and 2.1.2.

2.1.1. The detection threshold for single-user-based sensing

For single user, sensing the number of the cooperative nodes is one (i.e., K= 1, σj2=σu2,γj=γu. FromEq. (4) and for a given probability of false alarm Pf, the single-user threshold can be written as


where the inverse of the Q.function, and the probability of the detection Pdsinglecan be written as


2.1.2. The detection threshold for data fusion-based sensing

In a data fusion spectrum sensing scheme,Knodes cooperate in calculating the threshold that is used to make the global sensing decision. There are many fusion rules used to calculate the global sensing decision threshold, which are divided into: hard fusion rules including AND, OR, and majority rule and soft fusion rules including maximum ratio combining (MRC), equal gain combining (EGC), and square law selection (SLS). AND fusion rule

The AND rule decides that the signal is present if all users have detected the signal. For a system with K cooperative nodes with the same false alarm probability Pfcooperating using AND rule, the fusion center threshold can be expressed as


And the detection probability PdANDcan be written as

PdAND=QλANDσu21+γu1N2KE9 OR fusion rule

The OR rule decides that a signal is present if any of the users detect a signal. The fusion center threshold for K cooperative nodes cooperate using OR fusion rule which can be expressed as


And the detection probability PdORis

PdOR=1(1QλORσu21+γu1N2KE11 Maximum ratio combination (optimal MRC) fusion rule

In soft combination fusion K,cooperative nodes with noise variances σ112σ222σMK2and instantaneous SNRs {γ11,γ22,,γMK} send their ithframe energy test statistics Yij=1Nn=1Nyijn2,1jKto the fusion center. The fusion center, weighs and adds them together after receiving these energy statistics as follows:


An assumption is made that SNRs and noise variances at the sensing node will remain unchanged for all the frames during the training process (i.e., γij=γj, σij2=σj2). For soft optimal linear combination, we need to find the optimum weight vector wjthat maximizes the detection probability. For additive white Gaussian noise (AWGN) channels, the fusion threshold for MRC fusion rule is written as


And the detection probability PdMRCis given by


where the weighting coefficient vector wjw1w2wKcan be obtained by:





g=σ12γ1σ22γ2σ32γ3σ42γ4.σK2γKT Equal gain combination (EGC) fusion rule

Equal weight linear combining employs straightforward averaging of the received soft decision statistics. In the equal gain combination, the received energies are equally weighted and then added together. The calculation of the threshold λEGCand the detection probability PdEGCfollow Eqs. (13) and (14), respectively; the weighting vector is wj=w1wKwhere w1=w2=w3=wK=1/K[10]. Square law selection (SLS) fusion rule

Here, the fusion center selects the node with the highest SNR γSLS=MAXγ1γ2..γkand considers the noise variance σSLS2associated with that node. Then the fusion center threshold is calculated as follows:


And the detection probability PdSLSis


2.2. Machine-learning classification problem formulation

The ithframe energy test statistic (Yifor hard fusion or Ysifor soft fusion rule) given in Eq. (2) or (12) is compared to the sensing threshold to calculate the decision diassociated with ithframe in the training data set as follows:


where λλsingleλandλORλMRCλEGCλSLS,YFYiYsi,M is the number of frames in the training set and “1” represents the absence of primary user on the channel, and “1” represents the presence of the primary user transmission on the channel. The output of Eq. (17) gives a set of pairs Yidi,i=1,2M,di11that represent frame energy test statistics and their corresponding decisions. If we want to detect the decision (i.e., the class label) dxassociated with a new frame energy test statistic Yx, we can use one of the following machine-learning classifiers to solve this classification problem.

2.2.1. K-nearest neighbors (KNN) classifier

For K-nearest neighbors classifier,Knearest points to Yxare used to predict the class label dxwhich corresponds to Yx[11]. for K=1, the Euclidian distance dstbetween Yxand the training data points can be computed as


and, the new Yxis classified with the label dx= din, where dinis the point that achieves the minimum Euclidian distance between dstand Yx.

2.2.2. Naïve Bayes classifier

Under the assumption that di=1and di=1are independent, the prior probabilities for di=1and di=1given training example Yidi,i=1,2,,Mcan be calculated, and the class-conditional densities (likelihood probabilities) can also be estimated from the set Y1Y2Yk.Y1Y2Ykinwhich the new Yxis expected to fall in. And, the probability that the new Yxto be a member of either di=1or di=1class is calculated using Naïve Bayes assumption and Bayes rule [12] as follows:


where the prior probabilities are given to

Prdi=1=number ofYiwithclass label"1"total number of class labels
Prdi=1=number ofYiwithaclass label"0"total number of class labels

Whereas the class-conditional densities “likelihood probabilities” can be estimated using Gaussian density function by:


where μj,σjare mean and variance of the set Y1Y2Yk.Eq. (19) means that Naïve Bayes classifier will label the new Yxwiththe class label dithat achieves the highest posterior probability.

2.2.3. Support vector machine (SVM) classifier

For a given training set of pairs Yidi,i=1,2M, where YiR, and di+11, the minimum weight wand a constant bthat maximize the margin between the positive and negative class (i.e., wYi+b=±1) with respect to the hyper-plane equation wYi+b=0can be estimated using support vector machine classifier by performing the following optimization [13].


subject to diwYi+b1i=1,2,,M.

The solution of this quadratic optimization problem can be expressed using Lagrangian function as


where α=α1α2αMis the Lagrangian multipliers. IF we let Lwbα=0, we can get w=i=1MαidiYiand i=1Mαidi=0, and by substituting them into Eq. (21), the dual optimization problem that describes the hyper-plane can be written as


From expression (22), we can assess αand compute wusing w=i=1MαidiYi. Then by choosing αi>0,from the vector of α=α1α2αMand calculating bfrom b =dji=1MαidiYiYj, we classify the new instance Yxusing the following classification function


which means that the classification of new Yxcan be expressed as dot product of Yxand the support vectors.

2.2.4. Decision tree (DT) classifier

For the training of a set of pairs of sensing decision Yidi,i=1,2,,M,di11, the decision tree classifier creates a binary tree based on either impurity or node error splitting rule in order to split the training set into separate subset. Then, it repeats the splitting rule recursively for each subset until the leaf of the subset becomes pure. After that, it minimizes the error in each leaf by taking the majority vote of the training set in that leaf [14]. For classifying a new example Yx, DT classifier selects the leaf where the new Yxfalls in and classifies the new Yxwith the class label that occurs most frequently among that leaf.

2.3. Performance discussion

Figure 2 shows the receiver operating characteristic (ROC) curves for single-user soft and hard fusion rules under Additive White Gaussian Noise (AWGN) channel. In order to generate this figure, we assume a cognitive radio system with 7 cooperative nodes (i.e., K= 7) operate at SNR γu= −22 dB. The local node decisions are made after observing1000 samples (i.e., energy detection samples N= 100). For soft fusion rules, the SNRs γjfor the nodes are equal to {−24.3, −21.8, −20.6, −21.6, −20.4, −22.2, −21.3} and the noise variances σj2are 1111111.We use a false alarm probability Pfvaried from 0 to 1 increasing by 0.025. The simulation results show that soft EGC and optimal MRC fusion rules perform better than other soft and hard fusion rules even though that soft EGC fusion rule does not need any channel state information from the nodes.

Figure 2.

ROC curves for the soft and hard fusion rules under the case of AWGN receiver noise,σu2=1,γu= −22 dB,K= 7 users and energy detection overN=1000 samples.

Figure 3 shows the ROC curve depicting the performance of SVM classifier in classifying 1000new frames after training it over a set containing M = 1000 frames. The thresholds used for training SVM classifier (i.e., single-user threshold, AND, OR, MRC, SLS, and EGC fusion rule threshold) are obtained numerically by considering the cognitive system used to generate Figure 2; however, here, we set the false alarm probability to Pf=0.1.

Figure 3.

ROC curves shows the performance of SVM classifier in predicting the decisions for 1000 new frames after training it over a set containing1000 frames when single user, AND, OR, MRC, SLS, and EGC thresholds are used for training process.

From Figure 3 and Table 1, we can notice that when training SVM classifier with anyone of the following thresholds: single user, OR, MRC, SLS, or EGC, it can detect 100% positive classes. We can also notice that training with EGC threshold can provide 90% precession in classifying the positive classes with 10% harmful interference, whereas training SVM with AND threshold can precisely classify the positive classes by 97.8%. Table 1 shows the classification accuracy of SVM classifier (i.e., the proportion of all true classifications over all testing examples) and the precession of classification (i.e., proportion of true positive classes over all positive classes) as well as the recall of classification (i.e., the effectiveness of the classifier in identifying positive classes).

ThresholdSingle user (%)AND rule (%)OR rule (%)MRC rule (%)SLS rule (%)EGC rule (%)

Table 1.

The accuracy, precession and the recall of SVM classifier.

Figure 4 shows ROC curves showing the comparison of four machine-learning classifiers: K-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes and Decision tree when used to classify 1000 frames after training them over a set containing 1000 frames with single-user threshold (Note: the same system used to generate the simulation of Figure 3. is considered for computing the single-user threshold). We can notice from both Figure 4. and Table 2 that KNN and decision tree classifier perform better than Naïve Bayes and SVM classifier in terms of the accuracy of classifying the new frames.

Figure 4.

ROC curves shows a comparison of four machine learning classifiers: KNN, SVM, naive Bayes, and decision tree in classifying 1000 frames after training them over a set with 1000 frames using single user scheme threshold.

ClassifierAccuracy (%)Precession (%)Recall (%)
Decision Tree100100100
Naïve Bayes98.910091.2

Table 2.

The accuracy, precession and recall of KNN, SVM, NB, and DT classifiers used in classifying 1000 new frames after being trained with 1000 frames.

Table 3 shows the accuracy, precession, and the recall for decision tree classifier when used to classify 3000 frames after training it over a set containing 1000 frames for the same cognitive system used to generate Figure 3. The single-user threshold is used for training the classifier. The simulation was run with different number of samples for energy detection process. It is clear from the table that decision tree can classify all of the 3000 frames correctly or achieve 100% detection rate using only 200 samples for the energy detection process. And, due to the fact that the sensing time is proportional to the number of samples taken by energy detector, a less number of samples used for energy detection leads to less sensing time. Thus, when we use machine-learning-based fusion, such as decision tree or KNN, we can reduce the sensing time from 200 to 40 μs for 5 MHz bandwidth channel as an example, while we still achieve 100% detection rate of the spectrum hole.

Number of samplesAccuracy (%)Precession (%)Recall (%)

Table 3.

The accuracy, precession, and recall for decision tree classifier used in classifying 3000 frames for different number of samples.


3. Prediction of PU channel state based on hidden Markov and Markov switching model

In this part, the system model for forecasting the near future of PU channel state is divided into three models: (1) the model detecting the PU channel state (i.e., PU signal present or PU signal) which follows the conventional single-user energy detection (i.e., fusion techniques mentioned in Section 2.1 can also be considered here); (2) the model that generates a time series to capture PU channel state based on the detection sequence; and (3) the model for predicting the generated time series used to capture PU channel state based on hidden Markov model (HMM) and Markov switching model (MSM). The block diagram in Figure 5. illustrates these three models.

Figure 5.

Block diagram of PU channel state prediction model.

The PU channel state detection model can be written using Eq. (4); by giving probability of false alarm Pf, the detection threshold for single-user energy detector can be written as:


where the inverse of the Q.function.

And the decision of the sensing (i.e., PU detection sequence) over the time can be written as follows:


3.1. Time series generation model

Given PU channel state detection sequence over the time (i.e., PU absent, PU present), if we denote the period that the PU is inactive as “idle state,” and the period that PU is active as “occupied state,” our goal now is to predict when the detection sequence Dtwill change from one state to another (i.e., “idle” to “occupied “or vice versa) before that happens so that the secondary user can avoid interfering with primary user transmission. For this reason, we generate a time series zttomap each state of the detection sequence Dt(i.e., “PU present” and “PU absent”) into another observation space using two different random variable distributions for each state (i.e., ztv1v2vLrepresents PU absent or idle state and ztvL+1.vMrepresents PU occupied or present), the time series ztcan be written as


Now, supposing that we have given observations value O=O1O2OtOT,Otv1v2vMand a PU channel state at time step t,Xtsi,i=1,2.K, si01(i.e., 0 for PU idle and 1 PU occupied state), and we want to estimate the channel state at one time step ahead of the current state Xt+1. We can solve this problem using hidden Markov model Viterbi algorithm [15].

3.2. Primary users channel state estimation based on hidden Markov model

The generic HMM model can be illustrated by Figure 6.—in this figure,X=X1XtXTrepresents the hidden state sequence, where Xts1s2sK, Krepresents the number of hidden states or Markov chain and O=O1OtOTrepresents the observation sequence where Otv1v2vMand Mis the number of the observations in the observation space. Aand Brepresent the transition probabilities matrix and the emission probabilities matrix, respectively, while πdenotes the initial state probability vector. HMM can be defined by θ=πAB(i.e., the initial state probabilities, the transition probabilities, and emission probabilities) [15].

Figure 6.

Hidden Markov model.

Initial state probabilities for HMM can be written as


For a HMM model with two hidden states i=2,


And the transition probabilities can be written as,


where aij is the probability that next state equal sjwhen current state is equal to si. For HMM model with two states, the matrix Acan be written as


The emission probabilities matrix for HMM model is written as


Band bj represent the probability that current observation is vmwhen current state is sj. For example, in an HMM model with M=6and K=2, Bis written as


Now, for the problem we describe in subsection (3.1), if we assume that HMM parameters θ=πABand the observations value O=O1O2OtOTare given. If we assume that the maximum probability of state sequence tthat end at state ito be equal to δtiwhere


And we let ψtito be a vector that stores the arguments that maximize Eq. (30), we can write Viterbi algorithm to solve the problem mentioned in subsection (3.1) as follows:

1) step 1 initializes δtiand ψti.


2) step 2 iterates to update δtiand ψti.


3) step 3 terminates the update and calculates the likelihood probability Pand the estimated state qTat time Tas


In the above case, HMM parameters θ=πABare unknown and need to be estimated. We estimate these parameters statistically using Baum-Welch algorithm [16].

3.2.1. Hidden Markov model parameters estimation using Baum-Welch algorithm

If we assume that we have given some training observations with length L O1O2OtOLand want to approximate HMM parameters θ=πABfrom them, we can use maximum likelihood estimation. In order to do that, we define γtito be the probability of being in state siat time t,given t Ot,t=1,2L. γtiis written as


We also define ζtijto be the probability of being in state siat time tand transiting to state sjat time t+1,given Ot,t=1,2L. ζtijis written as


Given γtiand ζtij, the anticipated number of transitions from state siduring the path is written as


and the anticipated number of transitions from state sito state sjduring the path is written as


Given Eζtijand Eγti,we can extract the model parameters θ=πABfrom the training sequence as given in [16] using the step listed below

1- for i=1,2,3K, let π̂i=expected frequency in statesiattimet=1


2- for i=1,2,3Kand j=1,2,3K, compute

âij=Expected number of transitions fromstatesito statesjExpected number of transitions from statesi

3- for i=1,2,3Kand j=1,2,3K, compute

b̂im=Expected number of times in statesjand observingvmExpected number of times in statesj

The estimation algorithm can be summarized in the following steps:

  1. Get your observations O1O2,OL,

  2. Set a guess of your first θestimate θ1,k=1

    Update k=k+1

  • Compute θkbased on O1O2,OLand


  • Compute Eγtiand Eζtijfrom Eqs. (38) and (39)

  • Compute according to 5 the new estimate of aij, bik, πi, and call them θ(k + 1)

  • Go to 3 if not converged.

  • The prediction for a one-step ahead PU channel state can be done based on the trained parameters {π,A,B} with the help of Eqs. (31), (34), and (35) by setting T=1.

    3.3. Primary users channel state estimation based on Markov switching model

    An alternative way to estimate PU channel state is to use Markov switching model (MSM). For the time series in Eq. (26), we assume that ztobeys two different Gaussian distributions Nμz0σz02or Nμz1σz12based on the sensed PU channel state “PU channel idle” or “PU occupied.” We can rewrite Eq. (26) as follows:


    It is obvious that Eq. (43) represents a two-state Gaussian regime switching time series which can be modeled using MSM [17]. In order to estimate the switching time of one state ahead of the current state for this time series, we need to derive MSM regression model for the time series and estimate its parameters.

    3.3.1. Derivation of Markov switching model for Gaussian regime switching time series

    A simple Markov switching regression model to describe the two-state Gaussian regime switching time series is given in Eq. (43). This model can be written by following Ref [17] as


    where μstis an array of predetermined variables measured at time t, which may include the lagged values of zt, ϵtis the white noise process,st=01is a hidden Markov chain which has a mean and standard deviation over the time equal to μst=μ01st+μ1stand σst=σ01st+σ1st, respectively (the state variable stfollows first order Markov chain (i.e., two-state Markov chain as in [18])). Given the past history of st, the probability of sttaking a certain value depends only on st1, which takes the following Markov property:


    where Pijij=01denotes the transition probabilities of st=j, given that st1=i. Clearly, the transition probabilities satisfy Pi0+Pi1=1. We can gather the transition probabilities Pijinto a transition matrix as follows:


    The transition matrix Pis used to govern the behavior of the state variable st, and it holds only two parameters (P00and P11). Assuming that we do not observe stdirectly, we only deduce its operation from the observed behavior of zt. The parameters that need to be estimated to fully describe the probability law governing ztare the variance of the Gaussian innovation σ0,σ1, the expectation of the dependent variable μ0, μ1and the two-state transition probabilities P00and P11.

    3.3.2. Markov switching model parameters estimation via maximum likelihood estimation

    There are many ways to estimate the parameters for the Markov switching model. Among these ways are Quasi-maximum likelihood estimation (QMLE) and Gibbs sampling. In this section, we focus on maximum likelihood estimation (MLE).

    If we denote ψt1= {zt1,zt,zt+1z1} to be a vector of the training data until time t1and denote θ={σ0,σ1, μ0, μ1P00,P11} to be the vector of MSM parameters, then ψL= {zt1, zt,…, zL} to a vector of the available information with the length Lsample (see Figure 7.). In order to evaluate the likelihood of the state variable stbased on the current trend of zt,we need to assess its conditional expectations st=i, i=01based on ψand θ. These conditional expectations include prediction probabilities P(st=iψt1θ, which are based on the information prior to time t, the filtering probabilities P(st=iψt;θ) which are based on the past and current information, and finally the smoothing probabilities P(st=iψLθwhich are based on the full-sample information L. After getting these probabilities, we can obtain the log-likelihood function as a byproduct, and then we can compute the maximum likelihood estimates.

    Figure 7.

    Markov switching model.

    Normally, the density of ztconditional on ψt1and st=i, i=01is written as


    where Frepresent the probability density function. Given the prediction probabilities P(st=iψt1θ,the density of ztconditional on ψt1can be obtained from


    For i=0;1, the filtering probabilities of stare given by:


    The prediction probabilities are:


    where P0i=P(st+1=ist=0and P1i=P(st+1=ist=1are the transition probabilities. By setting the initial values as given in [19] assuming the Markov chain is presumed to be ergodic:


    we can iterate the Eqs. (49) and (50) to obtain the filtering probabilities P(st=iψtθand the conditional densities Fztst=0ψt1θfor t=1,2,..T. Then we can compute the logarithmic likelihood function using


    where Lθ̂is the maximized value of the likelihood function. The model estimation can finally be obtained by finding the set of parameters θ̂that maximize the Eq. (51) using numerical-search algorithm. The estimated filtering and prediction probabilities can then be easily calculated by plugging θ̂into the equation formulae of these probabilities. We adopt the approximation in Ref [20] for computing the smoothing probabilities P(st=iψLθ


    And, for i;j=0;1,smoothing probabilities is expressed as:


    Using PSL=iψLθas the initial value, we can iterate the equations regressively for filtering and prediction probabilities along with the equation above to get the smoothing probabilities for t=L1,··,k+1.

    3.4. Results and discussions

    Figure 8 shows the training detection sequence which we generate as a training observation using randomly distributed PU channel state “idle and occupied” over T = 250 ms simulation time. We use this training observations to train Baum-Welch algorithmin order to estimate HMM model parameters θ=πAB, assuming that the first estimate of θ1is:


    Figure 8.

    The training detection sequence for HMM and MSM.

    Figure 9a shows the performance of HMM algorithm in estimating the PU channel states (i.e., PU idle or PU occupied) of the time series that capture the detection sequence for a single-user cognitive radio network. Figure 9a contains three plots; the top plot shows the randomly distributed PU channel states over time T=500ms. The middle plot shows the generated time series following the distribution zt123for idle states and zt456for occupied states (note: we can construct the observation space from these two distributions as Ot123456,t=1,2500ms). The bottom plot shows performance of HMM algorithm in forecasting the time series generated to capture PU detection sequence.

    Figure 9.

    (a) Shows the performance of HMM algorithm in predicting the generated time series to capture PU channel state detection sequence. (b) Shows the performance of MSM algorithm in predicting the generated time series to capture the same PU detection sequence inFigure 8.

    Figure 9b shows the performance of MSM algorithm in predicting the switching process between the two PU channel states for the same PU detection sequence given in Figure 9a T=500ms. The top graph in Figure 9b shows the generated time series with the following distribution zt0.1,0.5for idle states and zt0.01,0.2for occupied states and the bottom graph shows the prediction performance using MSM. As it is clear from the figure, the prediction performance is smoother than HMM approach.


    4. Conclusions

    In this chapter, we have presented a per-frame decision-based cooperative spectrum sensing based on machine-learning classifier-based fusion approach. The simulation and numerical results have shown that the machine-learning classifier-based fusion algorithm performs same as conventional fusion rules in terms of sensing accuracy with less sensing time, overheads, and extra operations that limit achievable cooperative gain among cognitive radio users. In addition, we have also studied the problem of primary user channel state prediction in cognitive radio network and introduced Markov model and Markov Switching Model to solve this problem. We finally showed by the means of simulation that both hidden Markov model and Markov switching model perform very well in predicting the time series that capture the actual primary user channel state.



    I gratefully acknowledge the funding received from Shanghai Jiao Tong University to undertake my PhD. I also thank Prof. Bin Guo for his encouragement and help on the topic.

    © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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    Ahmed Mohammed Mikaeil (March 28th 2018). Machine Learning Approaches for Spectrum Management in Cognitive Radio Networks, Machine Learning - Advanced Techniques and Emerging Applications, Hamed Farhadi, IntechOpen, DOI: 10.5772/intechopen.74599. Available from:

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