Journal
PHYSICAL COMMUNICATION
Volume 34, Issue -, Pages 301-309Publisher
ELSEVIER
DOI: 10.1016/j.phycom.2018.06.003
Keywords
Cuckoo search algorithm; Cognitive vehicular networks; Spectrum allocation; Multi-objective optimization
Funding
- National Natural Science Foundation of China [61402346, 51475347]
- International ScienceAMP
- Technology Cooperation Program of China [2015DFA70340]
- Fundamental Research Funds for the Central Universities [WUT: 2017III015]
Ask authors/readers for more resources
The allocation of spectrum resources efficiently and equitably in dynamic cognitive vehicular networks is more challenging than static cognitive networks. Currently, most spectrum allocation algorithms are on the basis of a fixed network topology, thereby ignoring the mobility of cognitive vehicular users (CVUs), timeliness of licensed channels, and uncertainty of spectrum sensing in complex environments. In this paper, a cognitive vehicular network spectrum allocation model for maximizing the network throughput and fairness is established considering these factors. A rapid convergence, improved performance algorithm for solving this multi-objective problem is necessary to adapt to a dynamic network environment. Therefore, an improved decomposition-based multi-objective cuckoo search (MOICS/D) algorithm is proposed. This algorithm integrates a decomposition-based multi-objective optimization framework and an improved CS algorithm. The multi-objective problem is decomposed into multiple scalar sub-problems with different weight coefficients, and the cuckoo algorithm with adaptive steps is used to optimize these sub-problems simultaneously. Simulation results show that the MOICS/D algorithm has faster and more stable convergence than the MOEA/D and NSGA-II algorithms and can improve the throughput and fairness of the network. (c) 2018 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available