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Cognitive radio systems are helpful to access the unused spectrum using the popular technique, referred to as spectrum sensing. Spectrum sensing involves the detection of primary user (PU) signal using dynamic spectrum access. Cooperative spectrum sensing takes advantage of the spatial diversity in multiple cognitive radio user networks to improve the sensing accuracy. Though the cooperative spectrum sensing schemes significantly improve the sensing accuracy, it requires the noise variance and channel state information which may lead to transmission overhead. To overcome the drawbacks in conventional cooperative spectrum sensing, this paper proposes a fuzzy system based cooperative spectrum sensing. Selection combining (SC) and maximum ratio combining (MRC) are used at fuzzy based fusion center to obtain the value of the sensing energy. These energy values are utilized in finding the presence of PU, results in improved sensing accuracy. In addition, an intelligent fuzzy fusion algorithm determines the PU presence without the channel state information based on multiple threshold values. Simulation results show that the proposed scheme outperforms the existing schemes in terms of sensing accuracy.

Cognitive Radio (CR) technology has been introduced to alleviate the spectrum scarcity in wireless communication. Using this technology, secondary users (SUs) or CR users are allowed to opportunistically access the licensed spectrum band of primary users (PUs). In a CR network, SUs must accurately sense the vacant spectrum (spectrum holes) for their transmissions and vacate the frequency band as soon as the Primary Users (PUs) start their transmissions [

Various schemes have been proposed in the literature for spectrum sensing. Based on the signal detection, two broad categories of sensing techniques are: coherent and non-coherent detection. In coherent detection, the PU presence is determined by comparing the received signal with a priori knowledge of PUs. However, non-cohe- rent detection techniques do not require any prior knowledge.

Cyclostationary feature detection techniques detect the PUs using the periodicity in the received primary signal [

In most detection techniques, the noise power is supposed to be known for fixing a specific threshold value [

threshold but represents a significant transmission overhead. However, soft combining schemes simply perform like maximal ratio combining schemes in low SNR conditions. Some of the soft computing based algorithms require full idea about SNR and noise level of at all SUs [

Authors in [

The PSO and soft combining approaches require more information about the detected signals. To reduce the amount of transmission information overhead in energy detection schemes, a fuzzy logic can be utilized which can process noisy and incomplete input information [

The effective cooperative spectrum sensing can be achieved by the CR system which utilizes the fuzzy logic concept. In this section, a fuzzy inference system is introduced that can be useful for spectrum sensing systems and system model is explained based on fuzzy hypothesis test.

i) Fuzzy inference system for Spectrum Sensing

In cognitive radio networks, fuzzy logic based spectrum sensing is preferred over conventional mathematical methods. The fuzzy rule based fusion process provides better results in comparison with conventional AND/OR fusion rule is presented in [

Fuzzy based decision making is performed the fuzzy inference system, shown in

ii) System model and Fuzzy Hypothesis Test

Performance evaluation of Spectrum sensing techniques is carried out in [

In the hypothesis H_{0}, x_{i}(t) represents the signal received by ith SU and n_{i}(t) is additive white Gaussian noise. In the hypothesis H_{1}, s(t) is PU signal and h_{i}(t) is channel impulse response. Spectrum sensing scheme using energy detection is presented in [

The traditional energy detection problem decides between two crisp hypotheses H_{0} (null hypothesis) and H_{1} (alternative hypothesis). In real time situations, it is not a wise choice to go with crisp hypotheses due to the problems and practical difficulties associated with uncertainty. Fuzzy set theory concepts have been discussed in the literature to consider the uncertainty issues. The hypothesis test utilizes these fuzzy set theory is called as fuzzy hypothesis test (FHT).

Suppose µ is mean parameter of a normal probability density function, the crisp hypothesis can be defined as

However the fuzzy hypotheses are defined for realistic models as

The flowchart for conventional energy detection scheme is shown in

The main objective of this work is to propose an intelligent fuzzy logic based data fusion algorithm at the fusion centre (FC). The important contribution of the proposed work is to identify the PU signal without prior information. Some of the information are the noise variance, SNR of the PU signal at the SUs. In the proposed scheme, each SU measures the energy of its received signal in the frequency band of interest and then transmits its observation to the FC without any additional information. The FC performs data fusion with an adaptive fuzzy system using online learning to determine the fuzzification parameters from the data received. Consequently, the FC can make a global decision based on local observed energies without the knowledge of instantaneous SNR and noise variance at the SUs.

In the proposed spectrum sensing energy detection is performed at secondary users followed by the fuzzy based fusion for decision making. Based on the energy detection at SUs, local observations are transmitted to the fusion centre (FC). The global decision can be made at the FC using the local observations by means of adaptive Sugeno’s fuzzy system [

where x_{k}(j) is the jth sample of the received signal at the kth SU.

The Sugeno’s fuzzy system comprises of fuzzification unit, inference engine, rule base and defuzzification unit as shown in

Cognitive base station finds the strongest PU signal using the weighted combination of PU signal energy measured from the sensed energy of individual SUs. Fuzzy logic is utilized in energy detection using multilevel threshold values based on cooperative spectrum sensing. Membership functions are formed to fix the optimal set of thresholds. The received PU signal energy can be classified into different categories such as PU strongly present, PU moderately present, PU weakly present and PU not present using four different membership functions. The objective is to determine the optimal combination of these parameters that would maximize the fuzzy function which allow representation in flexible and adaptive form.

Based on the fuzzified energy, the inference rules are used to gain information on the present status of the PU

signal. Let pd_{i} denote the private decision reflecting the possibility that the PU signal is present based on the observation of the ith SU. Then, the fuzzy inference rule set can be proposed as follows [

Based on the private decisions obtained from the weighted averaging procedure, the global decision can be made based on the majority rule. The global decision is given by

In the simulation scenario, we assumed equal SNR for all the nodes in the cognitive radio network. Here each node will calculate the energy of the received signal and based on the threshold values it decides on the output as strongly present, moderately present, weakly present and not present This information is passed on to neighboring nodes as two bit information. Each node will take the result of two neighboring nodes, own decision and SNR value to make final decision. Performance of this decision fusion method is evaluated at various SNR values and it is compared with conventional AND/OR rule based single node decision. The presence and absence of the primary user is found using the time domain and frequency domain plots of the received signal. These plots are shown in

To validate the proposed cooperative spectrum sensing scheme, a Monte-Carlo simulation was carried out to analyze the ROC characteristics, probability of detection under the following conditions: the number of SUs is M = 7; the number of samples is N = 1000; the PU signal is a zero mean signal with 6 MHz bandwidth; the

noises at SUs have SNRs of −20, −19, −18, −17, −16, −15, −14 respectively. All the simulations are performed using Matlab version 7.12 with communication toolbox. To identify the best PU signal energy values and weighted energy values for MRC, the variability in sensing channels are considered. It is assumed that the distances from the PU to the SUs are significantly large compared to the distances between the SUs. Let p(H_{1}) and p(H_{0}) represent the probabilities that PU is present and absent, respectively. It is given by

and

p(H_{1}) and p(H_{0})are taken as 0.5 and 0.5 initially. Energy values are first averaged over 1000 Monte Carlo simulations and then is normalized dividing by the maximum average energy value. The performance of the proposed scheme was first evaluated in terms of the receiver operating characteristic (ROC) under both non-fading and Rayleigh fading channels with the learning rate a fixed at 0.05.The probability of detection was computed the same way as that of probability of false detection and the same thresholds are been used. Performance comparison of the proposed scheme and conventional method is shown in

The proposed scheme provides better probability of detection, reduced probability of false alarm and probability of miss detection and thus outperforms the existing hard decision based technique for varied SNR conditions. The ROC plot between Probability of false alarm and probability of detection (Pd) for SNR=-10 dB is shown in

If the spectrum is free or not utilized by the licensed PU, then these situations are indicated by false detection. The probability of false detection was computed by running the program 100 times and counting the number of times the PU was falsely detected when it was not using the spectrum.

The ROC plot between Probability of false alarm (Pfa) and probability of miss detection for SNR = −10 dB is shown in

An intelligent and reliable cooperative spectrum sensing based on fuzzy system based data fusion has been proposed. The main contribution of the work is that an intelligent fuzzy fusion algorithm is developed based on multiple threshold values at the fusion centre (FC) without the channel state information of the PU signal. Simulation results have been provided for spectrum sensing, probability of detection in different SNR conditions. The proposed method outperforms the existing energy detection schemes in terms of probability of detection and probability of false alarm.

Sitadevi Bharatula,Meenakshi Murugappan, (2016) An Intelligent Fuzzy Based Energy Detection Approach for Cooperative Spectrum Sensing. Circuits and Systems,07,1042-1050. doi: 10.4236/cs.2016.76088