With the demand for wireless technology, Cognitive Radio (CR) technology is identified as a promising solution for effective spectrum utilization. Connectivity and robustness are the two main difficulties in cognitive radio networks due to their dynamic nature. These problems are solved by using clustering techniques which group the cognitive users into logical groups. The performance of clustering in cognitive network purely depends on cluster head selection and parameters considered for clustering. In this work, an adaptive neuro-fuzzy inference system (ANFIS) based clustering is proposed for the cognitive network. The performance of ANFIS improved using hybrid particle swarm and whale optimization algorithms for parameter tuning called PSWO. The consequent and antecedent parameters of ANFIS model are tuned by PSWO. The proper cluster heads from the network are identified using optimized ANFIS. The proposed optimized ANFIS based clustering model is analyzed in terms of number of clusters, number of common channels, reclustering rate and stability period. Simulation results indicate that proposed clustering effectively increase the stability of cluster with reduced communication overhead compared to other conventional clustering algorithms.

The fast development of wireless technology has progressively reduced the available spectrum. CR is introduced to offer higher spectral efficiency and improved user performance by integrating various spectrum sharing and allocation methodologies [

Presently, multimedia applications require different and stringent Quality of Service (QoS) requirements. The poor coordination between primary and secondary user’s leads to QoS satisfied service has to be a doubtful and challenging one. The topology of the cognitive radio is divided into two categories: centralized and distributed. The communication overhead of the network is heavy due to its flexible topology. Clustering is a concept of grouping the nodes which is used to improve the node’s cooperation and to minimize the communication overhead of the fusion centre [

In this work, a new clustering based on ANFIS approach is proposed. From a literatures, fewer parameters were considered for cluster head selection and ignoring quality features. The proposed algorithm considers multiple parameters for cluster head selection. The parameters of ANFIS are adjusted by a hybrid Metaheuristic algorithm for effective Cluster Head (CH) selection and formation.

The structure of this work is divided as follows. The work from the previous studies in CR clustering is explained in Section 2. In the Section 3, the proposed work Preliminaries are discussed. Section 4 described the proposed CH selection approach. The results of Simulation are explained in Section 5. Lastly, the conclusion is drawn in the final Section 6.

Currently, various clustering algorithms have been proposed for the cognitive network. Chen et al. [

Awin et al. [

Dai et al. [

Hossen et al. [

Mansoor et al. [

Osman et al. [

Saleem et al. [

An adaptive neuro-fuzzy inference system is a type of neural network which combines both fuzzy and neural network logic. It inherits the features of both and is considered to be a universal estimator. The inference system is constructed based on a set of fuzzy IF–THEN rules used for learning nonlinear functions

There are two kinds of fuzzy inference systems can be performed: Takagi_Sugeno and Mamdani. Normally, Takagi-Sugeno system is preferred due to its efficiency and flexibility. Adaptive learning techniques are used to adjust fuzzy membership functions to train the data. The minimum error occurred in training and data testing compared to ANN and Fuzzy system. The learning capacity of the ANFIS system can be improved by best parameter tuning and settings. Various premise and consequent parameter tuning algorithms have been proposed for tuning ANFIS parameters. But, concurrent parameter tuning and rule optimization is a complex tasks. ANFIS rules engine consist of both potential and nonpotential rules. Rules which powerfully used for decision making. Nonpotential rules are considered weak rules that should be removed to reduce an overall computational cost. To reduce computational complexity and to increase the accuracy of the ANFIS system, a proper optimization algorithm is needed for training and tuning.

In this work, the parameters of ANFIS system are optimized using hybrid PSO and WO algorithms. The adaptive parameters of premise and consequent parameters positioned at the layers of fuzzification layer and defuzzification layer respectively. In conventional PSO, the solution starts with a random solution. Conversely, the hybrid PSOWO start the initial solution with the help of WO. The outcome of PSO improved by the exploration capabilities of WO.

PSO is one of the Meta-heuristic optimization methods that are motivated by the bird flocking social behavior or fish schooling behavior of searching food [

where r1 and r2 are the random numbers varied among 0 to 1. i denotes the particle in a swarm. W is the inertia weighting parameter.

Whale optimization is also an optimization technique based on nature-motivated [

where

In the phase of exploration, the whale’s position is generated randomly without using the best solution. The expression for position updating in the exploration phase is formulated as:

where _{rand}

The PSO algorithm was successfully applied to all real-world problems due to its easy operation and fast searching speed. But, the PSO algorithm falls to the local optimum easily when solving the large computational problem. The drawback of PSO is solved by integrating with WO. In WO, the best solution is achieved by a hyper-cube mechanism with the highest exploration/exploitation ability. This ability is used to solve the falling local minima problem of PSO.

The guidance of WO is utilized to direct particle positions instead of random motion. The operation of PSO was improved by the WO algorithm. The proposed hybrid PSOWO algorithm is given in algorithm 1.

In this work, the consequent and antecedent (premise) parameters of ANFIS are tuned by PSWO. In conventional ANFIS, a least-square estimator (LSE) is used to change the parameters of the then-part in the forward transfer and gradient descent (GD) algorithm used to tune the membership settings as a means of backpropagation. In the proposed ANFIS, the PSOWO is used to tune both forward transfer and backpropagation parameters as shown in

The proposed clustering algorithm is classified into two stages: CH selection and cluster formation. The CH selection process selects CH using the following parameters: the number of common channels, location, distance and average channel capacity. By the process of one-hop and two-hop neighbor discovery, the node can calculate parameters for cluster head selection.

_{cap})

This work assumes all the nodes are equipped with GPS for location identification. The distance and link gain between nodes is calculated using GPS location. The capacity C_{cap} of one channel from _{a}_{b}

where BW is the bandwidth of the channel and _{ij} is the link gain between two nodes.

_{ab}

The duration of common channel usage between two nodes is used to measure quality. The communication duration is evaluated that depends on summing the values of all channels. The higher value of time indicates that the channel quality between two nodes is good.

_{ab}

The distance between nodes calculated by using GPS outputs

_{com}

The stability is mainly depending on the number of the common channel between two SU’s. The increased number of common channels can group a greater number of cluster members and avoid the re-clustering of a network.

This four-parameter is given as an input for the ANFIS model. The eligible cluster heads are identified with the proposed ANFIS model. The proposed CH selection process is explained in the following algorithm steps and

After CH selection, the CH node sends a message to neighbour nodes to become cluster member. The node joined to the CH by calculating a Euclidean distance between member to CH node in terms of distance and common channel.

The proposed optimized ANFIS clustering is simulated using MATLAB. The nodes are deployed randomly with an area of 10km^{2}. The parameter setting used for simulation is given in the

Number of SU | 100 to 500 |
---|---|

Number of channels | 10 |

Transmission range of PU and SU | 1000 meters & 100 to 500 meters |

Packet size & packet rates | 512 bytes & 100 packets |

Initial energy of SU | 10 Joules |

Tuning delay | 10 ms |

The performance of a number of common channels as a function of an increasing number of secondary users is given in

The packet transmission analysis of the proposed algorithm as a function of data flow rates is given in

This work presented an optimized ANFIS based clustering algorithm for the cognitive network. To solve the spectrum sharing difficulty of a network, a clustering algorithm with the consideration of quality, distance and common channels has been proposed. Lastly, to verify the proposed clustering efficiency, simulation is conducted and compared with the state-of-the-art clustering techniques. The results observed that the proposed optimized ANFIS clustering was better in terms of stability, cluster lifetime, delay and common channels.