English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Ultra dense network (UDN) is the extreme densification of heterogeneous radio access technologies (RATs) that are deployed closely in a coordinated or uncoordinated manner. The densification of RATs forms an overlapping zone of signal coverage, leading user equipment (UE) to frequent signal handovers among the available RATs. Consequently, this degrades the overall system performance. The traditional approach of RAT selection is network-centric and the decision is primarily focused on the signal aspect. However, the next generation of digital wave is a paradigm shift to being user-centric. In this paper, a context-aware multi-attribute RAT (CMRAT) selection approach is proposed to eliminate unnecessary handover of UE among RATs and determine the best RAT as the next point of attachment among the available ones in the UDN. CMRAT integrates the context-aware concept with multi-attribute decision making (MADM) theory in RAT selection. CMRAT is formed with two mechanisms, including, first, a context-aware analytical hierarchy process mechanism to prioritize the criteria for obtaining the weight. Then, a context-aware technique for order preference by similarity to an ideal solution mechanism is employed to choose the best RAT amongst the available RATs. The proposed CMRAT mechanism was implemented and validated using MATLAB. The obtained simulation findings demonstrate that the proposed CMRAT approach outperforms classic MADM methods, namely TOPSIS, SAW, and GRA with respect to the number of handovers and ranking abnormality metrics. Hence, this paper paves the way to choose RAT based on context information comprising network and user preference criteria information.

Context-Aware Radio Access Technology Selection in 5G Ultra Dense Networks