A Multiple Attribute User-Centric Backhaul Provisioning Scheme using Distributed SON

Jaber, M., Imran, M. , Tafazolli, R. and Tukmanov, A. (2017) A Multiple Attribute User-Centric Backhaul Provisioning Scheme using Distributed SON. In: IEEE Globecom 2016, Washington, DC, USA, 04-08 Dec 2016, ISBN 9781509013289 (doi: 10.1109/GLOCOM.2016.7841518)

Abstract

The backhaul network is a critical challenge towards the success of 5G and corresponding difficulties are many-fold, such as network coverage expansion, very high bandwidth, ultra- low latency and energy consumption, at a minimum cost. No single backhaul solution can address all these requirements but, on the other hand, not all of the backhaul links require the same set of stringent requirements. To this end, we propose a novel scheme that capitalises on the diversity in both performance requirements and backhaul capabilities to maximise the system-centric as well as user-centric performance indicators. The user-centric backhaul provisioning scheme uses multiple attribute decision making (MADM) for the user-cell-backhaul association criteria in a way that intelligently associates users with available cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator of the end-to-end network performance such as capacity, latency, resilience, energy consumption, etc. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul capabilities that conform to the user requirements. Reinforcement learning is used by the radio cell to optimise the bias factors for each performance indicator in a way that maximises the system performance and users' end-to-end quality of experience (QoE). Preliminary results based on a case study show considerable improvement in users QoE when compared to state-of-the-art user-cell association schemes.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad
• Authors: Jaber, M., Imran, M., Tafazolli, R., and Tukmanov, A.
• College/School: College of Science and Engineering > School of Engineering
• ISBN: 9781509013289
• Copyright Holders: Copyright © 2017 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher