Artificial intelligence based cognitive routing for cognitive radio networks

Abstract

Abstract-Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. While cognitive radio networks (CRN) are envisioned as intelligent networks, relatively little research has focused on the network level functionality of CRNs. Although various routing protocols, incorporating varying degrees of adaptiveness, have been proposed for CRNs, it is imperative for the long term success of CRNs that the design of cognitive routing protocols be pursued by the research community. Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design. In this paper, we provide a self-contained tutorial on various AI and machine-learning techniques that have been, or can be, used for developing cognitive routing protocols. We also survey the application of various classes of AI techniques to CRNs in general, and to the problem of routing in particular. We discuss various decision making techniques and learning techniques from AI and document their current and potential applications to the problem of routing in CRNs. We also highlight the various inference, reasoning, modeling, and learning sub tasks that a cognitive routing protocol must solve. Finally, open research issues and future directions of work are identified.

New Approaches in Cognitive Radios using Evolutionary Algorithms

“…EAs are a set of machine learning techniques that seek to imitate the robustness of the procedures and structures that biological organisms have used for adaptation and evolution learning [33]. We are going to describe in brief, the technical and fundamental operations by which the evolutionary algorithms have been motivated.…”

“…The GAs encode their potential solutions in a simple chain of chromosomes as a data structure. Then, they select the best options to apply recombination operators and preserving the desired information [33]. The implementation of genetic algorithms starts with a random population of chromosomes, which evaluate the problem and seek opportunities for reproduction.…”

“…Algorithms such as ant colony optimization (ACO) are based on stochastic processes, and social the behavior of the ants described above, introduce solutions to a complex optimization problem [33]. Characteristics such as parallel computing, self-organization, and positive feedback are inherent in ACO.…”

“…This EA intends to establish and improve communication channels based on the simulation of swarms [33]. Its main application is in the optimization of nonlinear functions [41].…”

Enerji Etkin Kognitif Radyo Ağları: Yeşil Ödeme

“…Ancak çalışılan ortamdaki kullanıcı ve kanal farklılıkları bu mesajların alınabilmesi için uygun bir kanal kalitesi ister. Özellikle de çoklu-atlama (multi-hop) gerektiren ağlarda uygun seçilmiş ölçütlere sahip yönlendirme protokolleri bu kalitenin gerçekleşmesine yardımcı olur [31], [34]. Cesana, Cuomo ve Ekici ise çalışmalarında BR ağlarda algılanan ve paylaşılan spektruma ilişkin bütün bilgileri kullanarak etkin yönlendirme protokolleri bulunmasını veya bunun için sadece girişim, minimum yönlendirme enerjisi, uygun bant genişliğinin kullanılması, gecikme ve etkin iletişim kullanımı gibi yerel spektruma ait parametre ve değerlerin kullanılmasını önermişlerdir [35].…”