7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
https://doi.org/10.1016/j.comnet.2018.06.005
Copy DOIJournal: Computer Networks | Publication Date: Jun 5, 2018 |
Connection between two secondary users (SUs) in cognitive radio networks (CRNs) is not only determined by their transmission power and distance, it also depends on the availability of a common channel for both SUs to open it for communication. In CRN, each SU is equipped with a number of antennas, denoted as β, which is the maximum number of channels that an SU can open simultaneously, known as antenna budget constraint. As each SU has a limit on the maximum number of channels it can open simultaneously, so network may not be connectable. But, it is desirable to connect the largest subset of SUs while minimizing the interference introduced due the nearby transmissions among SUs on the same channel, this problem is called the largest-connected minimum-interference topology control (LMTC) problem in CRNs. In this paper, we model the network of SUs as a potential graph PG=(V(PG),E(PG)), where V(PG) is set of SUs and E(PG) is set of potential edges. First, we show that the LMTC problem is NP-hard then we propose an approximation algorithm to address LMTC problem with min (m/log n, n.β/2log n) ratio, where n and m are the number of nodes and edges in potential graph respectively. We also propose a distributed algorithm called distributed-LMTC with message complexity O(n2), to address the LMTC problem. To address this NP-hard problem, we combine both topology control and channel assignment phase. In topology control phase, a network subgraph is derived with satisfying antenna budget constraints. In channel assignment phase, we assign channel to link to minimize interference. Simulation results show that the constructed topology can achieve higher connectivity and throughput than other competitive topology control algorithms.
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.