Demand of larger bandwidth increases the spectrum scarcity problem. By using the concepts of Cognitive radio we can achieve an efficient spectrum utilization. The cognitive radio allows the unlicensed user to share the licensed user band. To sense the accessibility of vacant channel and allocation of licensed user band is provided by Machine learning techniques because this decision need to be very fast and accurate. It is based on certain factors (such as Power, Bandwidth, antenna parameters, etc.). In this paper, we used neural network to propose this decision of resource allocation more accurately by providing bandwidth, power, antenna gain, azimuth, angle of elevation and location as a supplements factors to increase the predicting accuracy of Available channel frequencies for secondary user in particular bands. The comparative analysis is done between artificial neural network techniques to determine the maximum decision accuracy in order to design a suitable neural network structure and the system to make fast prediction for available channels. The dataset is divided in to cellular 850 MHZ and Advanced wireless service 1900/2100 MHZ bands. In both bands, Feed Forward networks performs better as compared to Elman and Radial basis network for predicting the best available channel to accommodate the secondary user. It will considerably increase overall QoS and decrease interference, hence making Cognitive radio system reliable.

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Comparative Analysis of ANN Techniques for Predicting Channel Frequencies in Cognitive Radio

The demand for spectrum resources has increased dramatically with the advent of modern wireless applications. Spectrum sharing, considered as a critical mechanism for 5G networks, is envisioned to address spectrum scarcity issue and achieve high data rate access, and guaranteed the quality of service (QoS). From the licensed network’s perspective, the interference caused by all secondary users (SUs) should be minimized. From secondary networks point of view, there is a need to assign networks to SUs in such a way that overall interference is reduced, enabling the accommodation of a growing number of SUs. This paper presents a network selection and channel allocation mechanism in order to increase revenue by accommodating more SUs and catering to their preferences, while at the same time, respecting the primary network operator’s policies. An optimization problem is formulated in order to minimize accumulated interference incurred to licensed users and the amount that SUs have to pay for using the primary network. The aim is to provide SUs with a specific QoS at a lower price, subject to the interference constraints of each available network with idle channels. Particle swarm optimization and a modified version of the genetic algorithm are used to solve the optimization problem. Finally, this paper is supported by extensive simulation results that illustrate the effectiveness of the proposed methods in finding a near-optimal solution.

Network Selection and Channel Allocation for Spectrum Sharing in 5G Heterogeneous Networks

To overcome the drawbacks such as pre-maturity and the inclination to converge to partial optimum of the standard genetic algorithm, the adaptive genetic algorithm has been proposed in this paper. The adaptive genetic algorithm can change its cross-over probability and mutation probability adaptively according to the iterative times and the value of the cost function to avoid the shortcomings of the standard genetic algorithm. The paper also analyses the dynamic reconfiguration problem in the cognitive radio system which is a key aspect in realizing the optimization of wireless resources management. At last, the proposed algorithm is simulated under three different modes of the OFDM multi-carrier system. The simulation results indicate that the adaptive genetic algorithm can overcome the drawbacks of the standard genetic algorithm effectively, the parameters adjustment outcomes coincide with the expected results.

An Adaptive Genetic Based Cognitive Radio Parameter Adjustment Algorithm

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An Adaptive Genetic Based Cognitive Radio Parameter Adjustment Algorithm