Efficient access network selection and data demand prediction for 5G systems

The massive proliferation of sophisticated mobile terminals with advanced capabilities have led to an enormous surge in the demand for mobile broadband data. Also, the recent popularity of bandwidth intensive applications such as Netflix and YouTube has contributed to this demand for the wireless resources. In order to cope with this massive demand, fifth generation (5G) of wireless network is on the verge of deployment. This new generation of the wireless networks would pose different challenges for both subscribers and service providers, and the challenges need to be carefully addressed. Due to the diverse nature of the subscribers of mobile broadband, one network element is inadequate to meet the imposed requirements. Subscribers vary in terms of their usage of wireless resources as well as their preferred content. Deployment of the 5G systems promises the introduction of multiple tiers of heterogeneous networks within its architecture. This means radio access technologies (RATs) of various kinds (2G, 3G, 4G, 5G and Wi-Fi) would have to co-exist and aim to bridge the gap between the supply and demand for data. Subscribers, equipped with multi-mode or multi homing mobile terminals, can connect to one or more RATs to receive the required services. They also often run multiple applications simultaneously and as such, it must be ensured that the best access technology is assigned to a particular subscriber to maintain quality of experience and service. As such, an algorithm need to be devised that selects the best network to provide ubiquitous coverage to different types of users, running various kinds of applications, under dynamic network conditions. The network and infrastructure providers, on the other hand, face the need to meet up with the demand for data that the subscribers in different coverage regions require. In the 5G system, traditional proprietary hardware performing dedicated network functions such as packet gateway and service gateway would be replaced by softwarized virtual network functions (VNFs). These VNFs would need to be hosted in the data centres and would require computational power to process the subscribers’ traffic originating in an area. Therefore, data centres are set to play a key role in the provisioning of service in 5G systems. However, before establishing a data centre in a region, the traffic profile of that region need to be carefully studied to determine the optimal position and dimension of the facility. Furthermore, as cellular traffic differs depending on the time of the day, accurate prediction models are required to forecast future traffic demand to ensure dynamic and proper utilization of resources. This thesis aims to propose solutions to address these problems that subscribers and infrastructure providers face. Firstly, an algorithm is proposed to select the best access network for a subscriber running single or group of applications. Deviating from the existing access selection schemes in the literature, which consider the RAT-selection problem in an environment where accurate information is always available, the proposed algorithm models the problem in a completely fuzzy environment. As wireless networks are highly dynamic systems that are not only very unpredictable but also susceptible to sudden changes (for example malfunction of a particular RAT rendering it unusable), fuzzy systems are most adept in representing them. In the proposed algorithm, a new branch of fuzzy logic, Intuitionistic Fuzzy (IF) logic, is used with a popular multi-criteria decision making (MCDM) algorithm -Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to formulate a network selection problem. The IF-TOPSIS scheme is designed to accurately take in various parameters such as network conditions, different number of applications and user preferences to select the ideal network for different types of subscribers. The second part of this thesis aims to solve the problem associated with establishment of data centre and utilization of its resources. As the cellular traffic exhibit strong spatial and temporal dependencies, it becomes necessary to analyse the traffic before establishing an infrastructure like a data centre. Existing literature do not consider real world traffic while determining the best location and dimension of 5G data centres. In this thesis, a real world traffic data set is first analysed to understand the variations that are present in different regions within a city. Based on the traffic analysis, the ideal placement of the data centre is formulated as a facility location problem and solved using the Weiszfeld’s algorithm. Additionally, based on the traffic analysis, the optimal dimensions of the data centre in different regions are heuristically obtained. Finally, machine learning algorithms are employed to obtain future traffic demand values to aid dynamic allocation of data centre resources. Simulation results are presented to show the effectiveness of the proposed schemes