You can view the full text of this article for free using the link below.

Title: Optimal channel allocation for multi-PU and multi-SU pairs in underlay cognitive radio networks

Authors: Long Chen; Liusheng Huang; Hongli Xu; Hansong Guo

Addresses: School of Computer Science and Technoogy, University of Science and Technology of China, Hefei, Anhui 230027, China; Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu 215123, China ' School of Computer Science and Technoogy, University of Science and Technology of China, Hefei, Anhui 230027, China; Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu 215123, China ' School of Computer Science and Technoogy, University of Science and Technology of China, Hefei, Anhui 230027, China; Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu 215123, China ' School of Computer Science and Technoogy, University of Science and Technology of China, Hefei, Anhui 230027, China; Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu 215123, China

Abstract: In the underlay cognitive radio networks, this paper defines the joint channel and power allocation problem, which aims to optimise the max-total and max-min throughputs of secondary users (SUs), with the constraints of interference on primary receivers. For the max-total problem, we formulate the problem as a bipartite matching and derive a maximum weighted matching-based sum throughput maximisation algorithm (STMA) to solve this problem. For the max-min problem, on the basis of the optimal relay assignment (ORA) algorithm, we derive a polynomial time optimal channel assignment algorithm (OCAA) to iteratively assign channels to each SU pair under the power constraint. Simulation results demonstrate the effectiveness of our algorithms when compared with random method.

Keywords: cognitive radio; underlay; channel assignment; multiple pairs; matching; bipartite graph; max-min fairness.

DOI: 10.1504/IJAHUC.2018.088351

International Journal of Ad Hoc and Ubiquitous Computing, 2018 Vol.27 No.1, pp.19 - 33

Available online: 22 Nov 2017 *

Full-text access for editors Access for subscribers Free access Comment on this article