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Cognitive radio networks have huge advantages especially by enabling dynamic access to the spectrum. Management of the radio spectrum is done using a cognitive radio network.

Spectrum sensing in cognitive radio MATLAB is one of the favourite topics for researchers doing projects in cognitive radio networks. Our experts too have guided a lot of research projects in the field. Here is a complete overview on the spectrum sensing process in cognitive radio networks especially using MATLAB. Let us start by understanding the process of spectrum sensing.


Spectrum sensing is the process in which the presence of a primary user is identified by monitoring the frequency band regularly.

  • Until a frequency band is vacant by the primary user it can be utilized by the secondary user. This is the main concept of CRN or Cognitive radio networks
  • But at once as the primary user comes to occupy the band, the secondary user should search for another band so as to nullify the effect of interference
  • Fair usage of spectrum concept is applied when two secondary users are projected to use the same band

These are the main ideas and objectives of spectrum sensing in cognitive radio networks. Our subject experts gained huge knowledge by rendering support to a variety of research topics related to spectrum sensing. The in-depth knowledge that we gained over time is acting as a great boost to researchers from around the world who are searching for the best online research guidance. They approach us with many issues and challenges which are solved by our experts in no time. Now let us see in some more detail about the steps in spectrum sensing.


As you might know, there are different types or methods involved in spectrum sensing in CRN. It includes the following

  • Bayesian method
  • Energy detection method
  • Cyclostationary detection
  • Robust hypothesis test
  • Matched filtering detection

All these methods function based on consideration of certain parameters of the spectrum. In order to have a complete analysis and a comparative study on these methods we should first understand the basis on which these methods are classified. The following points will give you an idea on how spectrum sensing methods for classified on various fundamentals.

  • On the basis of the techniques involved in signal processing
    • Matched filter detection
    • Sensing based on autocorrelation
    • Sensing on the basis of eigenvalue
    • Energy detection sensing
    • Detection based on waveform and features
    • Sensing on the basis of covariance
  • On the account of COVID-19 between secondary users spectrum sensing is classified into two heads
    • Non cooperative or local sensing
    • Cooperative sensing
      • Relay assisted
      • Distributed
      • Centralized
  • On the basis of signal bandwidth spectrum sensing has two methods
    • Wideband sensing
      • Nyquist wideband sensing
        • Filter Bank based detection
        • Multi band joint detection
      • Sub Nyquist wideband sensing
        • Compressive sensing
        • Mighty channel sub nyquist sensing
    • Narrowband sensing
  • On the basis of number of RF chains, the following for the two methods of spectrum sensing
    • Multi antenna sensing
    • Single mode sensing

These are the different techniques involved in spectrum sensing. For the detailed working of these methods you can reach out to us at any time.

Our research experts are here to help you understand any advanced processes involved in these techniques. There are multiple ways in which spectrum sensing methods are being upgraded these days.

You can easily understand the working of the advanced techniques involved. For the technical details, implementation issues and performance analysis of those methods you can approach us readily. Our technical experts will guide you in this regard. Now let us see some details about the types of spectrum sensing.


As we saw in the previous section spectrum sensing has different methods and techniques. Now let us see about the channel correlation and noise correlation in different cases of spectrum sensing.

  • Cooperative spectrum sensing

Channel correlation

  • Type – spatial correlation
  • Cause – collocated nodes in CRN

Noise correlation

  • Type – filtering
  • Cause – autocorrelation function of the filter
  • Oversampled spectrum sensing

Channel correlation

  • Type –
    • Filtering
    • Over sampling
  • Cause –
    • Autocorrelation function of filter
    • Over sampling operation

Noise correlation

  • Type –
    • Oversampling
    • Filtering
  • Cause –
    • Over sampling operation
    • Autocorrelation function of the filter
  • Multi-antenna spectrum sensing

Channel correlation

  • Type –
    • antenna mutual coupling
    • Spatial correlation
  • Cause –
    • Separation of transmission and receiver antenna
    • Multipath propagation

Noise correlation

  • Type – filtering
  • Cause – autocorrelation function of the filter

Still there are some technical glitches in spectrum sensing which need expert advice to be solved. Your research can also take up this as the primary objective.

For these purposes machine learning methods are used in spectrum sensing techniques. Our technical team is well experienced in advanced machine learning algorithms. In the next section let us see about the machine learning concepts for spectrum sensing in CRN.

Machine Learning for Spectrum Sensing in Cognitive Radio Networks

The environment’s proximity is learnt heuristically using machine learning techniques. But it is important that the classifier is trained using a large amount of data (historically labelled). As a result of this, the observations during test can be classified on the basis of the parameters that are specified in the training phase. The following is a brief note on different machine learning algorithms and the evaluation metrics for specific features of spectrum sensing.

  • Energy statistics
    • Gaussian mixture model
    • K nearest neighbour
    • K means
    • Support vector machine
    • Naive Bayes
    • Decision tree
    • Random forest

Metrics used for evaluation

  • False alarm probability
    • Detection probability
    • Discrimination probability
    • Time of training
    • Accuracy
    • Delay
    • Sensing time
    • Rate of arrival
    • Throughput
    • Total error rate
  • Occupancy overtime
    • Support vector regression
    • Linear regression

Metrics used for evaluation

  • Time index
  • False probability
  • Probability of false alarm
  • Regression
  • Probability vector
    • K means cluster
    • SVM

Evaluation metrics

  • False alarm probability
  • Probability of detection
  • Delay in classification

As an example, now let us see the logical steps involved in sensing spectrum using fuzzy logic and SVM

  • First the input signal of the primary user is obtained
  • Computation of energy spectrum from the received primary user signal is the second step
  • The sensed energy of each secondary user is then sent to fuzzy fusion centre
  • Multilevel threshold values are used here to detect the primary user occupancy. Three cases arise as a result of this.
    • Primary user is strongly present
    • Moderate presence of primary user
    • The primary user is weakly present

After sensing the signal the training set has to be trained with the large amount of data which is the most essential factor for any module of machine learning to work efficiently. The following are the steps involved in training the SVM module.

  • Sensing signal is the first step
  • From the sensed data features are extracted
  • Feature values obtained from which using the residual energy class label is determined
  • The data is then fed to training set
  • Training SVMis the final step

Our research experts will guide you in each and every step involved in the above processes. We will share with you the practical difficulties that we faced while carrying out these processes along with our successful solutions so that it becomes an unmatchable source of reference for you.

Our engineers have been working on MATLAB for different projects. Especially we gained popularity for spectrum sensing in cognitive radio MATLAB. So we have given some details on using MATLAB for spectrum sensing below


  • MATLAB simulink is used to design a spectrum sensing detector on the basis of energy
  • The conditions of signal to noise ratio and optimal threshold are considered for this design
  • MATLAB editor window is used to write the MATLAB algorithm for designing the own block of the users for a particular threshold calculation which is optimal
  • The result of simulation is used to tabulate the real and estimated primary user activities

For any further advanced details about the MATLAB applications for spectrum sensing feel free to contact us. Our technical team is ready at any time to guide you. Now let us see about the MATLAB simulation scenario involved in spectrum sensing.


For the primary users present in each and every node the following processes are involved in simulation techniques using MATLAB.

  • Local sensing at each node is the first step
  • Coding and qualifying the sensed result is the next step
  • The results then forwarded to fusion centre and big data fusion by reinforcement learning at selected nodes is carried out
  • Consumption of energy and performance is balanced
  • From there certain control and feedback mechanism are involved to increase the efficiency
  • Performance of the system is then evaluated

Finally, the same process is carried out till the last node. As we have been in the field of spectrum sensing in cognitive radio using MATLAB we are very much experienced to solve all your queries arising out of MATLAB for simulation.

So, you can reach out to us regarding any kind of doubts related to MATLAB and the simulation techniques involved in cognitive radio network systems using MATLAB. Now let SSC about the different methods of spectrum sensing in MATLAB. Many Methods of Spectrum Sensing in Matlab have been investigated as follows,

  • Statistical tests such as kolmogorov Smirnov etc
  • Matched filter detector
  • Cyclostationary detector
  • Energy detector
  • Eigenvalue detector
  • Cyclic prefix correlation for OFDM

Now let us see in some detail about the working of MATLAB simulation for spectrum sensing.


The toolbox in MATLAB and Simulink are essential for spectrum sensing simulation purposes are designed and released by many research expert and commercial developers.

  • RFeye toolbox is one such toolbox for monitoring of spectrum designed by a global leader in devising solutions for monitoring RF spectrum, CRFS
  • You can import this tool box into MATLAB and use it with your algorithms for signal processing
  • Spectrum information and IQ can also be imported from the receivers of RFeye

Here you should know that CRFS is one among the top leaders who are finding solutions to real time monitoring of RF spectrum for the applications like defense, regulation, to aid security agencies and to perform spectrum operation. The methods devised by them have been applied extensively in the following fields

  • Management and spectrum enforcement
  • Real time situational awareness
  • Signal intelligence
  • Monitoring at the remote site
  • Detection of threat

A perfect environment for simulation based on MATLAB has to be created for evaluating the performance of cooperative spectrum sensing under fading channels.

We will share with you the details of projects that be guided under spectrum sensing using MATLAB. For now we will give you the input parameters for MATLAB simulation of spectrum sensing in cognitive radio networks below.


The following are the major parameters that can be used as input for spectrum sensing in cognitive radio MATLAB.

  • Signal transmitted
  • Channel models
  • Sensitivity of detection
  • Time consumed for sensing
  • Sensing samples
  • Testing the observations
  • Classifying thresholds (residual energy)
  • Difference in energy
  • K fold cross validation
  • Bandwidth
  • Total number of receivers
  • Modes involved in spectrum sensing
  • Training using observations
  • Probability of detection (reliability threshold)
  • Reference energy
  • Kernel function

Until now we have seen a lot about simulation parameters and metrics involved in simulation of cognitive radio network using MATLAB. These details are more than enough for you to choose your research topic under this heading. For further assistance you can connect with us at any time of your convenience as we have got 24 hours customer service system.

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