An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Introduction to Linear Optimization
Introduction to Linear Optimization
Convex Optimization
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Adaptive channel allocation spectrum etiquette for cognitive radio networks
Mobile Networks and Applications
Adaptive Resource Allocation in Multiuser OFDM System Based on Genetic Algorithm
CMC '09 Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 01
Adaptive radio resource allocation in OFDMA systems: a survey of the state-of-the-art approaches
Wireless Communications & Mobile Computing - Next Generation Wireless Communications and Mobile Computing-Networking Technologies
Resource allocation in an OFDM-based cognitive radio system
IEEE Transactions on Communications
OFDM for cognitive radio: merits and challenges
IEEE Wireless Communications
Opportunity detection for OFDMA-based cognitive radio systems with timing misalignment
IEEE Transactions on Wireless Communications
Cognitive radio spectrum allocation using evolutionary algorithms
IEEE Transactions on Wireless Communications
IEEE Computational Intelligence Magazine
Resource allocation for spectrum underlay in cognitive radio networks
IEEE Transactions on Wireless Communications - Part 2
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiuser OFDM with adaptive subcarrier, bit, and power allocation
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
This paper proposes two evolutionary algorithms (EAs) to perform dynamic spectrum assignment in distributed OFDM-based cognitive radio access networks. To achieve better radio resource utilization, the central spectrum manager (CSM) jointly considers the type of modulation level which can be used by each radio pair when deciding the number of subcarriers to be assigned. Using the piecewise convex transformations, we reformulate the nonlinear integer programming problem to an integer linear programming so that it is possible to obtain the optimal solution. While the solution to the integer linear programming problem is NP-hard, the proposed EAs provide useful suboptimal solutions especially when the number of radios and subcarriers are large. Our first proposed EA adopts the genetic algorithm. Although the reproduction process generates chromosomes which do not fulfill the constraints, our algorithm integrates the invisible walls technique used in particle swam optimization to retain the diversity while constructing chromosomes for the next generation. The second EA adopts the ant colony optimization approach using a directed multigraph. The vertices are used to represent the subcarriers and each edge corresponds to a possible chosen modulation index of a specific radio. We further obtain the performance of the two EAs through simulations and benchmark them against the optimal solution. Our studies show that ant colony algorithm gives better solutions most of the time, however, its computation time increases much faster compared to generic algorithm when the numbers of users and subcarriers increase.