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Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning...
In wireless ad hoc networks, multiple descriptions (MD) coding of video can effectively improve the quality of transmission, but there is a problem that how to choose the optimal path. In order to solve the problem, particle swarm optimization algorithm with adaptive mutation (AMPSO) is presented in this paper, which is used to choose transport path of MD video. This method can find the optimal solution...
The problem of end effects in Hilbert-Huang transform is produced in the Empirical Mode Decomposition (EMD), which has a badly effect on Hilbert-Huang transform. In order to overcome this problem, multi-objective Genetic Algorithm (GA) for solving the parameters selection of RBF Neural Network (RBF_NN) (GRHHT) is presented in this paper. Then the RBF_NN is used to predict the signal before EMD. The...
Cognitive radio (CR) is an intelligent communication system which greatly improves spectrum efficiency through the effective use of idle spectrum. In this paper, spectrum decision and adaptive reconstruction technology are researched. Adaptive spectrum decision scheme is proposed based on characteristics of user services, wireless environment and CR node capacity. Since the weights of CR adaptive...
The radio frequency spectrum is a scarce natural resource, so it is very important to use the spectrum efficiently. Cognitive radio, which can greatly improve the spectrum utilization, is able to adjust the transmit parameters according to the environment. In this paper, an adaptive multi-objective genetic algorithm is proposed to design the cognitive radio engine. In order to improve the performance...
Cognitive radio (CR) wireless parameter optimization is a typical multi-objective optimization problem. In order to optimize wireless parameters, a non-dominated neighbor distribution multi-objective genetic algorithm (NNDA) is presented in this paper. Based on non-dominated sorting, NNDA increases the probability that superior individuals pass down to next generation by crowding distance and distribution...
Resource optimization is a very important aspect in cognitive radio network (CRN). It is a typical multi-objective optimization problem. This paper proposes a mixed multi-objective immune cloning genetic algorithm (MMGA) to solve the optimization of resource allocation in CRNs. Based on the genetic algorithm of non-domination sort, the MMGA adds external memory immune operator and cloning operator...
According to the design requirement of UAV (the unmanned aircraft vehicle), the models of threat, UAV's dynamics, guidance and trajectory planning are established. Trajectory planning adopts genetic algorithms (GAs) and genetic coding uses "distance, corner" program. Through simulating by Matlab/Simlink software, the result proves the trajectory planning according with dynamics, characteristic.
To solve the problem of monocular-vision-oriented landmark recognition for mobile intelligent robot marching with complex background, a kind of landmark extraction algorithm which fused with transcendental color information and based on tabu-genetic search algorithm is proposed, and the convergence analysis of this algorithm is presented. Firstly, a novel experiment threshold based on RGB vision model...
In this paper, an enhanced inversion propagation model for a single cell coverage prediction is presented. We have simplified a complicated propagation problem to a system of large scale ill-condition equations by inversion method, which can be solved by simple genetic algorithm (GA) appropriately. The effectiveness of the proposed method has been demonstrated using experiments under various radio...
In the first place, an improvement was made on crossover and mutation of adaptive genetic algorithm (AGA) to let the crossover probability and mutation probability adapt nonlinearly. Then a comparison was made between Improved adaptive genetic algorithm (IAGA) and adaptive genetic algorithm (AGA) in segmentation time and adaptive function curve. The results indicated that IAGA can give attention to...
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