An Efficient Jaya Algorithm for Resource Allocation in the Cognitive-Radio-Networks-Aided Internet of Things

Abstract

More recently, there has been an ever-increasing demand for communication network bandwidth in Internet of Things (IoT), while requiring more and more powerful technologies in using scarce spectrum resources. Given the success of big data and artificial intelligence methods in a variety of industrial applications, it is expected that they can be also employed successfully to address the above issue. Cognitive radio networks (CRNs) identified as one of the potential solutions to improve the utilization of scarce radio spectrum resources, enable IoT to be achieved with high-performance. While in CRNs aided IoT systems, dynamic resource allocation is the main task. Then, orthogonal frequency division multiplexing (OFDM) as a multi-carrier parallel radio transmission technology, has been identified as one of the main approaches well-matched for CRNs aided IoT systems. In this paper, motivated by swarm intelligence paradigm, a solution method is proposed by applying an enhanced Jaya algorithm, named S-Jaya, to address the power allocation problem in cognitive OFDM radio networks for IoT. Due to the algorithm-specific parameter-free feature of the proposed Jaya algorithm with fast convergence speed, a satisfactory computational performance would be achieved in handling this problem. The simulation results show that, for the optimization problem with some constraints, the efficiency of spectrum utilization could be further improved through the use of S-Jaya algorithm, while maximizing the total transmission rate with faster convergence speed, compared with some popular algorithms.