Population Adaptation for Genetic Algorithm-based Cognitive Radios

Abstract

Abstract-Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantionally increases as the system complexity grows.In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor (EVF), which represents the amount of change in the environment parameters since the previous cognition cycle.

CSPA: Channel Selection and Parameter Adaptation scheme based on genetic algorithm for cognitive radio Ad Hoc networks

“…The average power factor represents the power required to transmit the data in a given time slot and it is indicated by μ. The equation (19) describes the average power saving factor P sav achieved Figure 11 Sample result for different modes of operation.…”

“…Similar GA based schemes presented in [15][16][17][18][19] perform the channel selection by considering the power (PWR), modulation (MOD), bit error rate (BER), bandwidth (B) and frequency as the basic genes. In these schemes, the objective function converges to the optimal value and then termination condition is achieved based on the desired criteria.…”

Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

“…However, most of the existing algorithms rank channels based only on the availability, which can lead to the selection of a channel with low occupancy, but it can be of low quality. Some other algorithms such as the one given in [27][28][29][30][31] consider the quality of the channel, but this occurs only after the selection of the channel to adapt the parameters of transmission according to the condition of the selected channel. …”

Adaptive resource allocation for cognitive radio networks with multiple primary networks

“…A successful example of exploring the swarm intelligence algorithms in CRN is the testbed designed by Virginia Tech group [21,22], which shown the flexibility and stability of CRN with the genetic algorithm (GA)-embedded cognitive engine. Newman et al [23] proposed a GA-based cognitive radio suitable for Emergency (minimize BER) and Low Power (minimize power consumption, like MA in a traditional network scenarios). Since then, lots of alternative schemes such as the quantum genetic algorithm [24], cross entropy [25], and PSO [26] is exploited to address the problems of resource allocation.…”