Resource allocation for heterogeneous wireless networks

Demand for high volumes of mobile data traffic with better quality-of-service (QoS) support and seamless network coverage is ever increasing, due to growth of the number of smart mobile devices and the applications that run on these devices. Also, most of these high volumes of data traffic demanding areas are covered by heterogeneous wireless networks, such as cellular networks and wireless local area networks (WLANs). Therefore, interworking mechanisms can be used in these areas to enhance the network capacity, QoS support and coverage. Interworking enhances network capacity and QoS support by jointly allocating resources of multiple networks and enabling user multi-homing, where multi-homing allows users to simultaneously communicate over multiple networks. It widens network coverage by merging coverage of individual networks. However, there are areas where interworking cannot improve network capacity or QoS support, such as the areas with coverage of only one networks. Therefore, to achieve network-wide uniform capacity and QoS support enhancements, interworking can be integrated with device-to-device (D2D) communication and small cell deployment techniques. One of the challenging issues that need to be solved before these techniques can be applied in practical networks is the efficient resource allocation, as it has a direct impact on the network capacity and QoS support. Therefore, this thesis focuses on studying and developing efficient resource allocation schemes for interworking heterogeneous wireless networks which apply D2D communication and small cell deployment techniques. First, uplink resource allocation for cellular network and WLAN interworking to provide multi-homing voice and data services is investigated. The main technical challenge, which makes the resource allocation for this system complicated, is that resource allocation decisions need to be made capturing multiple physical layer (PHY) and medium access control layer (MAC) technologies of the two networks. This is essential to ensure that the decisions are feasible and can be executed at the lower layers. Thus, the resource allocation problem is formulated based on PHY and MAC technologies of the two networks. The optimal resource allocation problem is a multiple time-scale Markov decision process (MMDP) as the two networks operate at different time-scales, and due to voice and data service requirements. A resource allocation scheme consisting of decision policies for the upper and