Network Selection Decisions for Multiple Calls Based on Consensus Level.

Saved in:
Bibliographic Details
Title: Network Selection Decisions for Multiple Calls Based on Consensus Level.
Authors: Falowo, Olabisi E., Taiwo, Olugbenga A.
Source: Journal of Network & Systems Management; Jul2018, Vol. 26 Issue 3, p592-615, 24p
Subject Terms: FEATURE selection, DECISION making, RADIO access networks, WIRELESS sensor networks, ALGORITHMS
Abstract: Next generation multimode terminals have the capability to support different classes of calls simultaneously as well as the ability to connect to two or more radio access technologies (RATs), at the same time, in a heterogeneous wireless network. For a mobile terminal having multiple classes of simultaneous handoff calls (such as file download and video sessions), RAT selection decisions can be made independently for individual calls in the group or jointly for the entire group of calls. Both independent and group RAT selection decisions for multiple calls have advantages and disadvantages. Existing RAT selection algorithms have focused on RAT selection decisions for single calls. Therefore, this paper investigates independent call and group call RAT selection decisions for multiple calls in heterogeneous wireless networks, and proposes a scheme that makes RAT selection decisions for multiple calls based on a consensus level among the multiple calls to be admitted. When this consensus level is among multiple calls to be admitted into a particular RAT and is equal to or above a certain threshold value, a group decision is used. Otherwise, independent decisions are made. The performance of the proposed RAT selection scheme is evaluated in a three service three RAT heterogeneous network, supporting multihomed terminals. Simulation results are given to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Network & Systems Management is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Be the first to leave a comment!
You must be logged in first