Placement of Relay Stations in WiMAX Network Using Glowworm Swarm Optimization

Sangeetha J, Keerthiraj Nagaraj, Ram Prakash Rustagi and Balasubramanya Murthy K N
Additional contact information
Sangeetha J: M S Ramaiah Institute of Technology, Bengaluru, India
Keerthiraj Nagaraj: University of Florida, Gainesville, USA
Ram Prakash Rustagi: Kammavari Sangha Institute of Technology, Bengaluru, India
Balasubramanya Murthy K N: PES University, Bengaluru, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2019, vol. 10, issue 3, 39-67

Abstract: The Relay Station (RS) deployment problem for WiMAX networks is studied. Unlike Base Station (BS), RS does not need a wire-line backhaul and has much lower hardware complexity. Hence, usage of RSs can significantly minimize the deployment cost and maximize the network coverage of the system. To solve the RS deployment problem, the authors have used a nature inspired technique known as Glowworm Swarm Optimization (GSO). Different cases have been considered for a single fixed BS, to find the feasible number of RSs and its optimal placement in WiMAX networks. Computational experiments are conducted to show the effect of RS deployments in different distribution scenarios. This article also shows the impact of placing RSs at optimal locations to serve given Mobile Stations (MSs) that are distributed arbitrarily in a given geographic region such that the cost is minimized, and the network coverage is maximized. The results obtained from the GSO algorithm are compared with k-means algorithm and it is observed that GSO performs better than k-means algorithm.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2019070103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:10:y:2019:i:3:p:39-67

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().