Abstract
Cognitive radio (CR) is a promising technology for addressing resource scarcity in wireless networks. However, in the current CR-based framework, when resource availability changes, existing resource allocation algorithms restart the optimization process without using historical information. In addition, most previous studies have focused on one or two objectives and some have only addressed pure power control or spectrum allocation, limiting the potential of CR. Thus, this article proposes a dynamic multiobjective approach for power and spectrum allocation in a CR-based environment in which the available spectrum channels vary over time and multiple objectives are involved. This article presents a dynamic multiobjective optimization problem (MOP) with the objectives of energy efficiency, fairness, and spectrum utilization and an approach of Pareto optimality. A dynamic resource allocation algorithm comprising a hybrid initialization method and feasible point generation mechanisms is proposed to solve the dynamic MOP. To dynamically adjust resource allocation, historical approximate Pareto optimal solutions, represented by a center and a manifold, are used to predict the new optima. The proposed approach can yield a better convergence level and convergence rate than those attained by comparable multiobjective resource allocation algorithms. Compared with conventional single-objective approaches, it achieves an excellent balance between the objectives.
Highlights
W ITH the Internet of Things emerging in global networks of machines, devices, and everyday objects, more data must be transmitted wirelessly [1]–[3]; this, in turn, has significantly increased the demands on spectrum bands and power in wireless networks
Motivated by the concept of dynamically tracking Pareto optimal solutions, in this article, we address the problem of resource allocation in a Cognitive radio (CR) network by considering a dynamic multiobjective optimization problem (MOP) with the objectives of energy efficiency, fairness, and spectrum utilization
This article focused on power and spectrum allocation and considered the energy efficiency, fairness, and spectrum utilization of CR networks
Summary
W ITH the Internet of Things emerging in global networks of machines, devices, and everyday objects, more data must be transmitted wirelessly [1]–[3]; this, in turn, has significantly increased the demands on spectrum bands and power in wireless networks. Motivated by the concept of dynamically tracking Pareto optimal solutions, in this article, we address the problem of resource allocation in a CR network by considering a dynamic MOP with the objectives of energy efficiency, fairness, and spectrum utilization. Spectrum allocation and power control are considered the discrete and continuous decision variables to be determined, respectively, and the number of idle spectra is considered to vary dynamically with time To address this dynamic MOP, we propose a dynamic multiobjective resource allocation algorithm, which comprises a hybrid initialization method and feasible point generation mechanisms.
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