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Today the need for wireless communication is of utmost importance because the growth of wireless networks shows the trend that in future, it will be the collaboration of mobile systems and internet technologies that will offer several varieties of services to the consumer. So the spectrum becomes the important resource for these services but today’s spectrum policy is the biggest hurdle in the advancement of the technology.
The existing spectrum policy leads to inefficient usage of radio spectrum. To overcome this problem cognitive radio turns out to be one of the efficient technologies as a solution.
Spectrum sensing is the most important and the very first step of cognitive radio technology. This paper also compares the theoretical value and the simulated result and then describes the relationship between the signal to noise ratio and the detections. At last, energy detection, simulation and results are discussed for different SNR values.
In a recent study made by the Federal Communications Commission (FCC), it was found that most of the licensed radio frequency spectrum is not effectively utilized by the primary users [1].
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In order to improve efficient spectrum utilization, it has been suggested that opportunistic access to the spectrum should be given to secondary users [2].
Cognitive radio is a technique where secondary user trace for an unused band to use when primary user is not in use of its licensed band. A function of cognitive radio is named as Spectrum sensing which enables to search for the free bands and it helps to detect the spectrum hole (frequency band which is free enough to be used) which can be utilized by unlicensed user with high spectral resolution capability.
Spectrum sensing is considered as a powerful approach to improve the utilization of scarce spectrum resources.
In this paper, Section 1 gives introduction about the cognitive radio and Section 2 contains detailed definition of cognitive radio, spectrum sensing techniques have been explained in section 3 shows methodology for implementation of cognitive radio system, results are shown in section 4 and section 5 concludes the discussion.
Cognitive radio is a form of wireless communication where a transceiver can intelligently detect the channels for communication which are in use and which are free, and move into unused channels while avoiding occupied ones. This improves the use of available radio-frequency spectrum while interference is minimized to other users. This is a paradigm for wireless communication where transmission or reception parameters of network are changed for communication avoiding interference with licensed or unlicensed users. For secondary radio systems, the main challenge is to be able to sensing spectrum hole when they are within such frequency bands.
Advantages of CR:
Cognitive radios are expected to be powerful tools for solving general and selective spectrum access issues.
Improves current spectrum usage and wireless data network performance through increased user throughput and system reliability.
Requires more adaptability and less coordination between wireless networks.
Drawbacks of CR:
Security, Software, Reliability, Keeping up with higher data rates, Loss of control, Regulatory concerns, significant research remains to be done to realize commercially practical cognitive radio.
Types of CR:
There are two types of Cognitive Radios:
Fundamental requirement in cognitive radio network is spectrum sensing and is used to enhance the detection probability by finding the spectrum hole as shown figure 1.
The performances for cognitive radio system requires:
Characteristics of CR:
There are two main factors having the cognitive radio and can be defined as
Functions of cognitive radio:
Cognitive radio mainly performs four functions:
Spectrum sensing techniques:
There are different spectrum sensing techniques which are mostly employed for spectrum sensing such as:
All these methods are efficient and each has its own advantages and disadvantages. The method used in this paper is energy detection method because of its simplicity and ability with which it can be applied in almost all the cases.
The most important and crucial task to establish cognitive radio Networks includes spectrum sensing. The spectrum Sensing technique aims to define the availability of spectrum and the presence of the licensed users (PU). To allow effective operation of cognitive radios, we must be able to detect precisely the spectrum holes at the link level. In practice, the unlicensed users need to continuously monitor the activities of the licensed users to find the spectrum holes (SHs), which is defined as the spectrum bands that can be used by the unlicensed users without interfering with the licensed users. This procedure is called spectrum sensing [3].Table 1 shows comparison of different sensing techniques.
The sensing results basis helps unlicensed users to obtain information about the channels so that they have access. However, the channel conditions may change rapidly and the behavior of the licensed users might change as well. To use the spectrum bands effectively after they are found available, spectrum sharing and spectrum allocation techniques are important. As licensed users have priorities to use the spectrum when SUs co-exist with them, the interference generated by the SU transmission needs to be below a tolerable threshold of the PU system [5].
Energy detector is also known as radiometry and it is most common method of spectrum sensing because of its low computational and implementation complexities. Moreover, the cognitive user’s receivers do not need any knowledge of the primary user’s signal. The signal is detected by comparing the output of energy detector with threshold which depends on noise floor [6].
The block diagram for the energy detection technique shown in figure 3 is composed of four main blocks [7, 8]
In this method, signal is passed through BPF of the bandwidth W, then multiplied by itself and is integrated over time interval. The output from the integrator block is then compared with a predefined threshold. This comparison is used to detect the existence of absence of the primary user. The threshold value can set to be fixed or variable based on the channel conditions. The energy detection estimates the presence of the signal by comparing the energy received with a known threshold derived from the statistics of the noise.
The output of BPF is passed to a squaring device to measure the received energy. Then an integrator is placed to determine the observation interval, T. Finally, output of the integrator, is compared with a detection threshold, to decide whether the signal is present or not. We assume that each CR user employs same energy detector and use the same threshold.
Depending on the idle state or busy state of the primary user, with the presence of the noise, the signal detection at the secondary user can be modeled as a Binary Hypothesis Testing Problem, given as:
Hypothesis 0 (H0): signal is absent
Hypothesis 1 (H1): signal is present
If the received signal, y, is sampled, the nth (n= 1, 2, 3 _ _ _ _) sample, y(n) can be given as [9,10]
The aim of the spectrum sensing is to decide between two hypotheses which are
x (t) = w(t), H0 (Primary User absent)
x (t) = h * n(t) + w(t), H1 (Primary User present)
Where x (t) is the signal received by the CR user, n(t) is the transmitted signal of the primary user, w(t) is the AWGN band, h is the amplitude gain of the channel. H0 is a null hypothesis, which states that there is no licensed user signal.
Then a decision rule can be stated as:
H0 if α < vt
H1 if α > vt
where α is the test statistic. Energy detection differentiates between the two hypotheses H0 and H1 by comparing with threshold voltage vt. Setting the right threshold value is of critical importance [11]
Detection probability (Pd), false alarm probability (Pfa) and missed detection probability (Pmd) are the key measurement metrics that are used to analyze the performance of spectrum sensing techniques. The performance of energy detector spectrum sensing technique is illustrated by using ROC (Receiver operating characteristics) curves which is a plot of Pd versus Pfa or Pmd versus Pfa.
The probability of detection detects the exact status of the channel. As the channel is detected as occupied when the channel is vacant known as probability of false alarm. It reduces the chance to access the channel when it is vacant. If Pfa is high, results in lower spectrum utilization. The channel is detected as vacant when the channel is occupied known as probability of missed detection. It causes interference to licensed user, as the unlicensed user attempts to access the channel.
The simulation was done on MATLAB version R2013a through which the capability of an energy detector is evaluated. MATLAB is a fourth generation programming language tool for numerical computation, data visualization and serves as an easy laboratory.
Signal to noise ratio is a measure that compares the level of a desired signal to a level of background noise. As Pfa increases, Pmd values are going to decrease drastically which improves performance of energy detector at low SNR values
Similarly, as values of SNR are decreasing, detection probability decreases and increase in values of probability of missed detection.
The plot of Probability of false alarm versus probability of detection for different values of SNR is illustrated in figure 5 and detection probability (Pd), false alarm probability (Pfa) and missed detection probability, Pmd = (1 - Pd) are the key measurement factors that are used to analyze the performance of spectrum sensing techniques. It is clearly seen that, for less probability of false alarm values, probability of missed detection is high.
One feature of the rocsnr function in matlab is that we can specify a vector of SNR values and rocsnr calculates the receiver operating characteristic curve for each of these SNR values. Rocsnr returns the single pulse detection probabilities Pd, Pfa for the SNRs in the vector SNRdB. The ROC curve is constructed assuming a coherent receiver with a non-fluctuating target. Instead of individually calculating Pd and Pfa values for a given SNR, we can view the results in a plot of receiver operating characteristic curve. The rocsnr function plots the receiver operating characteristic curves by default if no output arguments are specified. Calling the rocsnr function with an input vector of five SNR values and no output arguments produces a plot of the ROC curves. From the rocsnr function curves for larger the value of SNR better the detection probability.
The cognitive radio system continuously searches the white space where primary user is not present and is determined by the method of energy detection. When it finds out the white space, immediately it allots to the secondary user (SU) and whenever primary user (PU) wants to use the slot, secondary user immediately leaves it.
Consider 10 sensing samples and channel is assumed as rayleigh for different average SNR values then calculate the Pf by using the gammainc function for different predefined threshold values. Calculating the Pd values then plotting Pd vs Pfa for different SNR values then compare these values with rocsnr function curves.
The plot of Probability of false alarm versus probability of detection for different values of signal to noise ratio is illustrated in figure 5.This shows that with increasing SNR values there will be an improvement in Pd values for both theoretical (thin line) and practical (thick line) cases that is with no output arguments are specified it is outperformed with built in function. Here by comparing with and without output arguments for simulation we find that rocsnr function outperforms.
It can also be analyzed from the plot that as the SNR is more positive the probability of detection increases whereas, if the SNR decreases it becomes very near to probability of false alarm and also affect the probability of detection .
Detection of spectrum holes depends greatly on setting the detection threshold and the SNR level. The plot of different threshold values versus probability of detection for different values of signal to noise ratio is illustrated in figure 6.This shows that with increasing SNR values there will be an improvement in Pd values for same threshold values.
The approach was to take the decisions in this paper on the basis of power spectral density of the channel which can be used cognitively to search the available spectral gaps those can be used to new incoming users (SU) thus improving the overall channel’s throughput.
The computation done keeping in mind Pd (Probability of detection). It is seen that there is greater chances of false detection at higher Pd (Probability of detection).The results show Probability of False Alarm vs Probability of detection for different SNR values. Figure 3 shows the ROC curves for different SNR values for a total of five channel model including primary and secondary users then compare it with the matlab function rocsnr. It has been observed that probability of detection is higher for higher SNR values. The probability of detection has been improved with the increasing SNR values. Therefore the results are satisfactory and the energy detector performs efficiently in the simulation.
Simulation of Roc With Snr Values Using Energy Detection in Cognitive Radio. (2024, Feb 14). Retrieved from https://studymoose.com/document/simulation-of-roc-with-snr-values-using-energy-detection-in-cognitive-radio
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