1.1 Background
Mobile communication networks are currently faced to new challenges since the demand for mobile broadband access and data traffic exploded over the last few years. This situation is mainly due to data consumer devices, such as smartphones, that were first released in 2007.
Voice traffic used to dominate mobile network traffic volume until data traffic overtook voice in around 2010. The consumer habits in terms of mobile services use changed drastically as smartphones became more and more popular. The capabilities of those devices in addition to mobile applications and laptops connected to mobile networks create huge amounts of transmitted data, and traffic forecasts predict that this traffic explosion will keep on increasing. Also, the way users consume mobile services has also been considerably modified, as the majority of smartphones are used while people are at the office or at home [1]. Therefore, more than 60% of the mobile data traffic is now generated within “private” areas [2]. Mobile operators have noticed that in dense urban areas, the existing outdoor networks might not be sufficient to deal with all the traffic demand. So, they are now faced to new challenges for achieving sufficient coverage, capacity and quality targets.
The current mobile networks that we are used to rely on do not suit with the change of habits in mobile data consumption. Networks need nowadays to be more accurate on where are located the data transmission needs, which keep on increasing. Large network coverage might not meet requirements in terms of quality in the future, as people consume more and more mobile data in restricted areas. Network coverage might have to focus on providing high data transmission rates in specific places where people tend to gather and demand mobile access.
In response to the growing demand for affordable mobile broadband connectivity, a very interesting solution for the evolution of radio access networks is heterogeneous networks (HetNets). As a deployment strategy, especially in dense urban areas, they might be a
clever choice for mobile operators. It consists in using all at once different Radio Access Technologies (RATs) and Wi-Fi in the same network structure.
Customer satisfaction depends on good coverage and capacity in a lower churn rate [3]. If mobile operators reduce the load of data in indoors areas, they will be able to keep on offer quality services, no matter the ever-increasing traffic. Moreover, operators could provide value-added services and applications using this solution. Indeed, they can improve user’s utility and thus the average revenue per user (ARPU). The mobile operator benefits also rely on capital expenditure (CAPEX) and operational expenditures (OPEX) reduction, which may have impact on pricing. So, HetNets appear to be a win-win solution if used in a deployment strategy.
Many technologies are available for HetNets deployment. Operators already provide wide-area GSM coverage and HSPA in densely populated urban wide-areas. They now want to deploy small cells to support the macro layer and increase network capacity when required. Many networks will include an overlay of cells of different sizes. For instance, outdoor terminals may be served by a combination of macro, micro and pico cells. All together, they will complement the fixed wide area networks and support traffic.
1.2 Problem statement
One critical issue that has to be dealt with in HetNets is handover. Indeed, HetNets consist of multiple cells which might have different characteristics. As a result, they generate at the same time numerous radio transmission signals relying on different transmission protocols, transmitted power, and cell coverage, both for uplink and down link. As a result, they create a radio mist where different radio signals representing different. The handover of a wireless mobile device among this radio mist need to be sensitive to all types of connection powers and be able to choose in real time the most efficient one. Numerous handover protocols have been implemented and tested for allowing the device to be always connected to the cell currently providing the best QoS.
However, even if handovers protocols are nowadays quite effective, there is still some liabilities regarding the fact of being able to know whereas or not some handovers are really needed or should be avoided regarding various network parameters. There is
wide-measuring the need for a handover by evaluating network parameters in real time. Indeed, unnecessary handovers or handovers that are bound to fail when performed are the cause of a waste of time, energy, money, and has an impact on the provided QoS. Thus, reducing the amount of failed and unnecessary handovers may lead to a better throughput and improve the user experience by estimating of the need of a handover between cellular networks and WLANs.
1.3 Aim of the thesis
The aim of this thesis is to carry out a qualitative analysis of heterogeneous networks in order to have a better understanding of this innovative and on the rise network solution.
Firstly we will see the evolution of the mobile broadband market and highlight some trends in the market thanks to technology comparisons and real case examples. Then we will have a detailed presentation of heterogeneous networks, their structure, their technologies, strengths and weaknesses. Following that, we will focus the handover issue and how performing handover in heterogeneous networks might be improved using handover necessity estimation patterns.
To sum up in one question, the aim of the thesis may be explained as follows: Considering the even increasing heterogeneous networks deployments to make up for the ongoing growth of mobile data traffic, how the handover operation of a mobile device connected to such networks might be improved using handover necessity estimation protocols that rely on a real time network parameters analysis?
1.4 Structure of the thesis
The chapter 2 of the thesis is an analysis of mobile broadband traffic evolution over the last decade. It aims to understand how mobile traffic has so far changed, and also to try to make some forecasts about mobile traffic evolution within the next few years. Following that, chapter 3 introduces us with the notion of heterogeneous networks and how it represents an interesting alternative to make up for current macro cells networks liabilities.
Chapter 3 also analyses in more detail heterogonous networks architecture and some of its most popular used technologies. Then it tackles the handover issue created by the co-existence of current networks with heterogeneous ones. To deal with this issue, chapter 4 explains the importance of avoiding unnecessary or failed handovers that would result in a waste of time, energy, money and damage the consumer perception about the quality of service. Following that, chapter 4 presents some handover failure or necessity estimation
protocols. The new feature of this thesis is to use the estimated travelling time of a mobile device through a given cell and use it in such handover necessity estimation protocols. It aims to give these protocols more flexibility, time-responsiveness, and reduce latency.
Then chapter 4 goes through some simulations of these handover necessity estimation protocols using estimated travelling time to determine how these methods might improve the network quality of service and performance by eliminate unnecessary handovers. The results of those simulations are then analyzed and interpreted to compare the handover efficiency with and without such protocols. It leads to a clear assessment of the handover necessity estimation protocols overall performances and helps to figure out their deployment potential.