The hype surrounding the 5G mobile networks is well justified in view of the explosive increase in mobile traffic and the inclusion of massive “non-human” users that form the internet of things. Advanced radio features such as network densification, cloud radio access networks (C-RAN), and untapped frequency bands jointly succeed in increasing the radio capacity to accommodate the increasing traffic demand. However, a new challenge has arisen: the backhaul (BH), the transport network that connects radio cells to the core network. The BH needs to expand in a timely fashion to reach the fast spreading small cells. Moreover, the realistic BH solutions are unable to provide the unprecedented 5G performance requirements to every cell. To this end, this research addresses the gap between the 5G stipulated BH characteristics and the available BH capabilities. On the other hand, heterogeneity is a leading trait in 5G networks. First, the RAN is heterogeneous since it comprises different cell types, radio access technologies, and architectures. Second, the BH is composed of a mix of different wired and wireless technologies with different limitations. In addition, 5G users have a broader range of capabilities and requirements than any incumbent mobile network. We exploit this trait and develop a novel scheme, termed User-Centric-BH (UCB). The UCB targets the user association mechanism which is traditionally blind to users’ needs and BH conditions. The UCB builds on the existing concept of cell range extension (CRE) and proposes multiple-offset factors (CREO) whereby each reflects the cell's joint RAN and BH capability with respect to a defined attribute (e.g., throughput, latency, resilience, etc.). In parallel, users associate different weights to different attributes, hence, they can make a user-centric decision. The proposed scheme significantly outperforms the state-of-the-art and unlocks the BH bottleneck by availing existing but misused resources to users in need.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:725195 |
| Date | January 2017 |
| Creators | Jaber, Mona |
| Contributors | Imran, Muhammad |
| Publisher | University of Surrey |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://epubs.surrey.ac.uk/842117/ |
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