Performance Analysis of Network Assisted Neighbor Discovery Algorithms

Recently there has been an increasing interest in applications that enable users in the proximity of one another to share experiences, discover surrounding events, play online games and in general develop proximity based social networks. Most of the existing applications are based on cellular network communications, combined with over-the-top (OTT) solutions involving either registration at an application server and/or obtaining location information from a positioning system such as Global Positioning System (GPS). However, registration at a server often requires continuous registration updates due to, for example, mobility and changes in user population, which is a tedious and resource consuming process. In addition, using GPS drains the battery of devices. Since the spectrum used for cellular network is limited, it can become a scarce resource with increasing quantity of the devices. In order to deal with these problems, the concept of direct Device-to-Device (D2D) communication has been proposed as a solution. Using D2D technology, devices can discover nearby devices without extra positioning information. It can not only increase the spectrum efficiency, but also improve the coverage of cellular network. The discovery of devices can be prepared before the actual communication phase or proceed simultaneously. In this work, we mainly investigate the former one, which is called a-priori discovery. In fact, a-priory device discovery provides a value on its own right, independently of a subsequent communication phase using D2D or traditional cellular communication. Previous studies indicate that ad hoc D2D discovery (i.e. without cellular network assistance) is feasible but time, resource and energy consuming. Recognizing this problem, both academia and industry pay more attention to the D2D discovery in cellular spectrum, where D2D discovery can be assisted by a cellular radio access network. Despite this interest, to the best of our knowledge, there is essentially no work on identifying different degrees of network assistance (that we call the “network assistance levels”) and evaluating the potential gains of specific netw ork assistance algorithms. Therefore, in this thesis work we develop algorithms that take advantage of network assistance to improve the performance of the ad hoc neighbor discovery algorithms in terms of energy efficiency, resource utilization, discovery time and discovery rate. To address the equirements of different applications and types of devices, two design objectives are studied in this work. The first one is discovery time prioritized without energy limitation, while the other is constrained to using a certain amount of energy. We distinguish five levels of network involvement from allowing for synchronization to explicitly providing information on the used peer discovery resources. The analysis in this work indicates that the setting of transmission probability for devices, which depends on system load, plays a critical role in the process of D2D discovery. Furthermore, stopping the devices which have already been discovered by enough candidates can improve the performance, in terms of reducing the interference to other devices and saving energy consumption. It is also shown in the simulation results that, to reach a given quantity of D2D communication candidates for all the devices in the area of study, the discovery time as well as the energy consumption can be reduced up to 87-91% from the lowest level of the network assistance to the highest level