Joint Coverage and Backhaul Self-Optimization in Emerging Relay Enhanced Heterogeneous Networks

Imran, A., Giupponi, L., Imran, M. A. and Abu-Dayya, A. (2014) Joint Coverage and Backhaul Self-Optimization in Emerging Relay Enhanced Heterogeneous Networks. In: 2014 IEEE International Conference on Communications (ICC), Sydney, Australia, 10-14 Jun 2014, pp. 2671-2677. ISBN 9781479920037 (doi: 10.1109/ICC.2014.6883727)

Abstract

This paper presents a novel framework for joint self-optimization of backhaul as well as coverage links spectral efficiency in relay enhanced heterogeneous networks. Considering a realistic heterogeneous network deployment, where some cells contain Relay Station (RS), while others do not, we develop an analytical framework for self-optimisation of macrocell Base Station (BS) antenna tilts. Our framework exploits a unique system level perspective to enable dynamic maximization of system-wide spectral efficiency of the BS-RS backhaul links as well as that of the BS-user coverage links. A distributed and practical self-organising solution is obtained by decomposing the large scale system-wide optimization problem into local small scale optimization problems, by mimicking the operational principles of self-organisation in biological systems. The local problems are non-convex but have very small scale and can be solved via appropriate numerical methods, such as sequential quadratic programming. The performance of developed solution is evaluated through extensive system level simulations for LTE-A type networks and compared against conventional tilting benchmarks. Numerical results show that up to 50 gain in average spectral efficiency is achievable through the proposed solution depending on users geographical distributions.

• Item Type: Conference Proceedings
• Additional Information: This work was made possible by NPRP grant No. 5-1047-2437 from the Qatar National Research Fund (a member of The Qatar Foundation).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad
• Authors: Imran, A., Giupponi, L., Imran, M. A., and Abu-Dayya, A.
• College/School: College of Science and Engineering > School of Engineering
• ISBN: 9781479920037
• Published Online: 28 August 2014