Current technological advances have caused an exponential growth of the number of mobile Internet-connected devices, along with their respective traffic demands. To cope with this increase of traffic demands, fifth generation (5G) network architectures will need to provide multi-gigabit capacity at the access base stations (BSs), through the deployment of ultra-dense small cells (SCs) operating with millimeter-wave (mmWave) frequencies, e.g. 60 GHz. To connect the BSs to the core network, a robust and high capacity backhaul infrastructure is required. As it is unfeasible to connect all the SCs through optical fiber links, a solution for the future 5G backhaul relies on the usage of mmWave frequencies to interconnect the SCs, forming multi-hop wireless mesh topologies. In this thesis, we explore the application of the Software-defined Networking (SDN) paradigm for the management of a SC wireless backhaul. With SDN, the data and control planes are separated and the network management is done by a centralized controller entity that has a global network view. To that end, we provide multiple contributions. Firstly, we provide an SDN-based architecture to manage SC backhaul networks, which include an out-of-band Long Term Evolution (LTE) control channel and where we consider aspects such as energy efficiency, resiliency and flexible backhaul operation. Secondly, we demonstrate the benefit of the wireless backhaul configuration using the SDN controller, which can be used to improve the wireless resource allocation and provide resiliency mechanisms in the network. Finally, we investigate how a SC mesh backhaul can be optimally reconfigured between different topologies, focusing on minimizing the network disruption during the reconfiguration.

The growth of mobile devices, along with their traffic demands, is expected to saturate the current mobile networks soon. To cope with such demand increase, fifth generation (5G) network architectures will need to provide multi-gigabit capacity at the access level, through the deployment of a massive amount of ultra-dense small cells (SCs). To connect the access and core networks, a robust and high capacity backhaul is required. To that end, mmWave links that operate at e.g. 60 GHz, can be used to interconnect the SCs, forming multi-hop wireless mesh topologies.

In this thesis, we study the application of the Software-defined Networking (SDN) paradigm for the management of a SC wireless backhaul. Firstly, we provide an SDN-based architecture to manage SC backhaul networks, which includes an out-of-band control channel and where we consider aspects such as energy efficiency, resiliency and flexible backhaul operation. Secondly, we show the benefits of the wireless backhaul configuration using the SDN controller, which can be used to improve the wireless resource allocation and provide network resiliency. Finally, we investigate how a SC mesh backhaul can be optimally reconfigured between different topologies, while minimizing the network disruption during the reconfiguration.

The increase of mobile devices and services over the last decade has led to unprecedented mobile traffic growth. To cope with the increasing demands, fifth generation (5G) network architectures have been designed to provide the required capacity using a large number of small cells (SCs). However, a dense deployment of SCs requires a robust and scalable backhaul to transport the access traffic towards the Internet. In this thesis, we explore the application of the Software-defined Networking (SDN) paradigm for the management of a wireless backhaul. With SDN, the data and control planes are separated and the network is managed by a centralized entity. To that end, we provide multiple contributions that focus on achieving resilient and reconfigurable wireless backhaul networks. Firstly, we propose an SDN-based architecture to manage the wireless backhaul. Our architecture is integrated in practical testbed environments, where we use an SDN controller to configure the forwarding plane and wireless backhaul links. Secondly, we evaluate SDN-based resiliency in the wireless backhaul. We achieve that by implementing fast-failover resiliency with OpenFlow group tables and by using the bidirectional-forwarding detection protocol (BFD) to monitor the state of the backhaul links. Finally, we develop algorithms that calculate the necessary reconfiguration operations to transition between different wireless backhaul topologies, while minimizing the impact on existing user traffic. We consider that the backhaul nodes can be powered on/off and are equipped with steerable antennas that can be aligned to form links with different neighbors. Our optimization problems are modeled as mixed integer linear programs (MILP) that are optimally solved using exact mathematical programming methods. In addition, we develop greedy-based heuristic algorithms that solve the same problems and obtain good quality solutions in short time.

The increase of mobile devices and services over the last decade has led to unprecedented mobile traffic growth. To cope with the increasing demands, fifth generation (5G) network architectures have been designed to provide the required capacity using a large number of small cells (SCs). However, a dense deployment of SCs requires a robust and scalable backhaul to transport the access traffic towards the Internet.

In this thesis, we explore the application of the Software-defined Networking (SDN) paradigm for the management of a wireless backhaul. To that end, we provide multiple contributions that focus on achieving resilient and reconfigurable wireless backhaul networks. Firstly, we propose an SDN-based architecture to manage the wireless backhaul. Our architecture is integrated in practical testbed environments, where we use an SDN controller to configure the forwarding plane and wireless backhaul links. Secondly, we evaluate SDN-based fast-failover resiliency in the wireless backhaul. Finally, we develop several algorithms that orchestrate different backhaul reconfiguration operations with minimal impact on existing user traffic.