期刊
PHYSICAL COMMUNICATION
卷 34, 期 -, 页码 301-309出版社
ELSEVIER
DOI: 10.1016/j.phycom.2018.06.003
关键词
Cuckoo search algorithm; Cognitive vehicular networks; Spectrum allocation; Multi-objective optimization
资金
- 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]
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.
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