Heterogeneous Deployment Analysis for Cost-Effective Mobile Network Evolution: - An LTE Operator Case Study

The plethora of connected devices, such as attractive smartphones, data dongles and 3G/4G built-in tablet computers, has brought mobile operators to face increasing demand in mobile broadband traffic and services. In addition to the roll-out of Long Term Evolution (LTE), the deployment of small low-powered base stations is a promising, cost-effective solution to considerably enhance user experience. In such a network topology, which is denoted as heterogeneous deployment, the macro layer is expected to provide wider coverage but lower average data speeds whereas small cells are targeted at extending network coverage and boosting network capacity in traffic hot-spot areas. The thesis deals with the deployment of both outdoor small cells and indoor femto cells. Amongst the outdoor solution, particular emphasis is put on relay base stations as backhaul costs can be reduced by utilizing LTE spectrum available at the macro layer for wireless backhaul. The main goal is to investigate the LTE downlink performance of different deployment configurations, focusing on spectrum allocation schemes and deployment strategies that are needed to maximize network coverage. The coverage performance is expressed as the percentage of satisfied users achieving a data rate above a required minimum, with targets in the range of 90-95% coverage. Differently from most studies using statistical models of deployment areas, the performance analysis is carried out in the form of operator case studies for large-scale deployment scenarios, including realistic macro network layouts and inhomogeneous spatial traffic distributions. Deployment of small cells is performed by means of proposed heuristic deployment algorithms, which combine network coverage and spatial user density information. As a secondary aspect, deployment solutions achieving the same coverage performance are compared in terms of Total Cost of Ownership (TCO), in order to investigate the viability of different deployment combinations from both a performance and economic perspective.

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