Throughput optimization in cognitive wireless network based on clone selection algorithm. (May 2016)

• Record Type: Journal Article
• Authors: Zheng-Yi, Chai; Xue-yang, Yan; Ya-lun, Li; Si-Feng, Zhu
• Is Part Of: Computers & electrical engineering. Volume 52(2016)
• Page Start: 328
• Page End: 336
• Publication Date: 2016-05
• Subjects: Cognitive wireless network -- Throughput optimization -- Immune clone algorithm -- Constraint optimization; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.12.012
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A clone selection algorithm was proposed for the throughput problem in cognitive wireless network. The proposed algorithm presents a very reasonable tradeoff between computational complexity and performance. The proposed solution is more suitable for slowly varying spectral environments such as IEEE 802 . 22 networks in realistic network settings. Abstract: In cognitive wireless network, throughput scheduling optimization under interference temperature constraints has attracted more attentions in recent years. A lot of works have been investigated on it with different scenarios. However, these solutions have either high computational complexity or relatively poor performance. Throughput scheduling is a constraint optimization problem with NP(Non-deterministic Polynomial) hard features. In this paper, we proposed an immune-clone based suboptimal algorithm to solve the problem. Suitable immune clone operators are designed such as encoding, clone, mutation and selection. The simulation results show that our proposed algorithm obtains near-optimal performance and operates with much lower computational complexity. It is suitable for slowly varying spectral environments. Graphical abstracts: