A goodput distribution model for planning IEEE 802.11 WBNs in built environments. (1st December 2017)

• Record Type: Journal Article
• Authors: Qu, Ying; Ng, Bryan; Homer, Michael
• Is Part Of: Journal of network and computer applications. Volume 99(2017)
• Page Start: 28
• Page End: 46
• Publication Date: 2017-12-01
• Subjects: Wireless backhaul networks -- Built environments -- Goodput modelling -- Network planning; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2017.10.005
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: IEEE 802.11 wireless backhaul networks (WBNs) have emerged as a practical solution for bridging access points and backbone networks with low cost and ease of installation. However, the IEEE 802.11 protocol operates in the unlicensed spectrum and experiences unregulated interference, making it difficult to guarantee quality of service (QoS). With newer and farther reaching applications being densely deployed in built environments, such as small cell cellular networks and smart grids, users are increasingly expecting high QoS and fair access. This increased expectation for high QoS motivates the study of goodput models for planning IEEE 802.11 WBNs in built environments. In this paper, we design a goodput distribution model with consideration of structured placement of WBN nodes in built environments and validate our goodput model through simulations. The results show that our model provides an accurate prediction of goodput distribution in IEEE 802.11 WBNs under different traffic demands and radio propagation conditions. Such a goodput model is useful for node placement and optimising routing and channel assignment algorithms in IEEE 802.11 WBNs. Abstract : Graphical abstract: Abstract : Highlights: A new model for predicting goodput distribution is proposed. The model predicts goodput&starvation in both effective&in effective CSMA. The model is well suited for planning WBNs in built environments.