Cognitive engine implementation for wireless multicarrier transceivers

Abstract

This paper presents a genetic‐algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems. Determining the appropriate radio parameters, given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems. Genetic algorithms (GAs) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal. Although there have been implementations of GA‐based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail. Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single‐carrier systems neglect the practical issues of multiple carriers. A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived. The performance analysis results illustrate the trade‐offs between the convergence time of the GA and the size of the GA search space. Copyright © 2007 John Wiley & Sons, Ltd.

Design of Reasoning Agent for Cognitive Radio Networks

“…The utility metric we use is a choice with acceptable performance. Other metrics in utility functions such as those discussed in [3] can be adopted easily in this process of designing the CBR. For example, it is easy to implement case selection functionality with a metric on transmission power, for example selecting a case with the minimum transmission power.…”

Tecnicas inteligentes en la asignacion de espectro dinamica para redes inalambricas cognitivas

“…Por esta razón, los algoritmos genéticos han sido ampliamente adoptados para resolver optimización multiobjetivo y problemas de configuración dinámica en CRN, en respuesta al cambiante entorno inalámbrico de las redes de comunicación (Rondeau, Le, Rieser y Bostian, 2004;Newman et al, 2007;Yong, Hong y Qing, 2009 Las investigaciones desarrolladas en han evidenciado la aplicación de algoritmos genéticos como medio de adaptación de parámetros en la radio definida por software, para el entorno de radio comúnmente cambiante. Así mismo, en Newman et al (2007) se plantea diseñar un motor cognitivo basado en algoritmos genéticos para controlar parámetros de radio, de una portadora y multiportadora.…”

A Transmission Parameter Optimization Scheme Based on Genetic Algorithm for Dynamic Spectrum Access

“…유전자 알고리즘 기반 인지 엔진은 다른 인공지능 기반 인지 엔진과는 달리 사람의 인지 과정처럼 스스로 진화가 가능하기에 많은 주목을 받 아왔다 [4,5] . 최근의 유전자 알고리즘 기반 인지 엔진에 관한 연구는 SU의 전송 매개변수 최적화에 초점을 맞 추어 진행되어 왔다 [5][6][7][8] . [5] 목적 적합도 함수를 정의한다.…”