The exponentially increasing traffic demand for mobile services requires innovative solutions in both access and backhaul segments of 5th generation (5G) mobile networks. Whilst substantial research efforts address the access segment, the backhaul part has received less attention and still falls short in meeting the stringent requirements of 5G in terms of capacity and availability.

Ease of deployment and cost efficiency motivate the use of microwave backhauling that supports fiber-like capacity with millimeter-wave communications. However, these carrier frequencies are subject to weather disturbances like rain that may substantially degrade the network throughput and availability performance. To meet the stringent 5G requirements, in this thesis we develop a complete framework for network design and online adaptation in the presence of weather-based disruptions.

For topology design, we investigate the trade-off between the path diversity and link budget to meet the high availability requirements. We propose several efficient algorithms for joint optimization of cost and power to satisfy the availability, differential delay and data rate requirements. The results show that joint optimization of link budget and cost leads to more power-efficient solutions. Moreover, we characterize the correlation among failure events and incorporate its impact in the topology design problem. Performance evaluation results verify that considering correlation increases the network robustness under weather-based failures.

For network adaption, we develop a fast and accurate rain detection algorithm that triggers a network-layer strategy, e.g., rerouting. The rain impact can be alleviated by regular rerouting using a centralized approach realized by software defined networking (SDN) paradigm. However, careless reconfiguration may impose inconsistency due to asynchrony between different switches, which leads to a significant temporary congestion and limits the gain of rerouting. To address this, we propose a consistency-aware rerouting framework that considers the cost of reconfiguration. At each time slot, the centralized controller may either take a rerouting decision to increase the network throughput while accepting the switching cost, or choose not to reroute at the expense of a decreased throughput due to route sub-optimality. We use a model predictive control algorithm to provide an online sequence of decision policies to minimize the total data loss. Compared to regular rerouting, our proposed approach reduces the throughput loss and substantially decreases the number of reconfigurations.

In the thesis, we also study which backhaul options are the best from a techno-economic perspective. Fiber-based solutions provide high data rates with robust connectivity under different weather conditions, whereas wireless solutions offer high mobility at low installation costs with lower data rate and availability. We develop a comprehensive framework to calculate the total cost of ownership of the backhaul segment and analyze the profitability in terms of cash flow and net present value. The evaluation results highlight the importance of selecting proper backhaul solution to increase profitability.