Wireless Communications
Mathematics and Computers in Simulation
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
Auction-based spectrum sharing
Mobile Networks and Applications
Dynamic Spectrum Access with QoS and Interference Temperature Constraints
IEEE Transactions on Mobile Computing
Allocating dynamic time-spectrum blocks in cognitive radio networks
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Formalizing the interference temperature model
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
Cognitive radio adaptation using particle swarm optimization
Wireless Communications & Mobile Computing
Expert Systems with Applications: An International Journal
Resource allocation for spectrum underlay in cognitive radio networks
IEEE Transactions on Wireless Communications - Part 2
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Spectrum Sharing for Multi-Hop Networking with Cognitive Radios
IEEE Journal on Selected Areas in Communications
Hi-index | 0.00 |
Dynamic spectrum access is a promising technique designed to meet the challenge of rapidly growing demands for broadband access in cognitive radio networks. By utilizing the allocated spectrum, cognitive radio devices can provide high throughput and low latency communications. This paper introduces an efficient dynamic spectrum allocation algorithm in cognitive radio networks based on the network utility maximization framework. The objective function in this optimization problem is always nonconvex, which makes the problem difficult to solve. Prior works on network resource optimization always transformed the nonconvex optimization problem into a convex one under some strict assumptions, which do not meet the actual networks. We solve the nonconvex optimization problem directly using an improved particle swarm optimization (PSO) method. Simulated annealing (SA), combined with PSO to form the PSOSA algorithm, overcomes the inherent defects and disadvantages of these two individual components. Simulations show that the proposed solution achieves significant throughput compared with existing approaches, and it is efficient in solving the nonconvex optimization problem.