A genetic‐based cognitive link decision algorithm for OFDM system

Abstract

SUMMARYTo solve the problem of cognitive link decision for OFDM system in a time-varying environment, an improved genetic algorithm-based decision-making module is proposed. At first, the decision-making module collects the environmental information and QoS requirements of the radio and then produces link configuration for the radio on the basis of the improved genetic algorithm. New crossover and mutation strategies are introduced to accelerate convergence rate of the genetic algorithm. A novel population initialization method is also proposed to reduce the time of achieving the optimal decision by the decision-making module when the radio is operated in a slow changeable environment. Simulations and discussions demonstrate the effectiveness of the decision-making module, and this research work exhibits its importance of enhancing the decision speed that is of crucial demand in cognitive decision-making applications.

Distributed opportunistic spectrum sharing in cognitive radio networks

“…Another approach is game theory, where the system is modeled as a centralized game with cognitive BSs being the players . Other optimization methods that have been used to settle on a proper channel allocation include the use of fuzzy logic , Markov decision processes , linear programming , machine‐learning , and genetic algorithms .…”

Link decision algorithm in underlay cognitive radio networks over a Rayleigh fading channel

“…Link decision and reconfiguration are the basic abilities of the CR, thus, they have drawn great attention, and many literatures about this topic can be found. A genetic-based link decision method is proposed for the orthogonal frequency division multiplexing (OFDM) system in [6], in this work, the decision is made according to the current channel path loss and user's QoS preference by using a genetic algorithm. In [7], the authors propose a link decision method and apply it to a system which can adapt its use of multiple antennas in addition to modulation and coding by employing the 'trial-and-error' mechanism.…”

“…Currently, although some literatures about how to properly determine the link parameters of the secondary user (SU) have been reported, for example, [6][7][8][9][10][11][12], there are still some problems. First, in some literatures, the SU makes link decision solely according to its quality of service (QoS) requirements and the channel characteristics without considering the influence of its behaviours on the primary users (PUs) [6][7][8]. Secondly, some of the existing work is based on the assumption that the activities of the PU have no influence on the SU's transmission [9][10][11][12][13], for example, they assume that the PU's transmission causes no interference to the SU.…”

An hybrid evolutionary multiobjective algorithm for multiuser margin maximization in DSL

“…AI techniques are being widely used in different areas of telecommunication [20][21][22][23][24][25][26]. AI techniques are being widely used in different areas of telecommunication [20][21][22][23][24][25][26].…”

“…Genetic algorithms are part of so called evolutionary algorithms (EA), which composes a greater field of science called artificial intelligence (AI). AI techniques are being widely used in different areas of telecommunication [20][21][22][23][24][25][26].…”