A Study on Device To Device Communication in Wireless Mobile Network

Volume 3 Issue 3 (March 2015

Towards D2D-Enhanced Heterogeneous Networks

In this paper, we examine upcoming 5G networks where the support of device-to-device (D2D) communication is expected to be a key asset for operators and users alike. Firstly, we argue the need to functionally integrate D2D and infrastructure-to-device (I2D) modes. Next, we address practical issues such as integrated resource scheduling of D2D communication within heterogeneous networks, proposing an extension of the proportional fairness algorithm, which we call multi-modal proportional fairness (MMPF). We evaluate the impact of D2D in a two-tier scenario combining macro- and micro- coverage, finding that, although I2D retains a clear edge for general-purpose downloading, D2D is an appealing solution for localized transfers as well as for viral content

Clustering algorithm for D2D communication in next generation cellular networks : thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand

Next generation cellular networks will support many complex services for smartphones, vehicles, and other devices. To accommodate such services, cellular networks need to go beyond the capabilities of their previous generations. Device-to-Device communication (D2D) is a key technology that can help fulfil some of the requirements of future networks. The telecommunication industry expects a significant increase in the density of mobile devices which puts more pressure on centralized schemes and poses risk in terms of outages, poor spectral efficiencies, and low data rates. Recent studies have shown that a large part of the cellular traffic pertains to sharing popular contents. This highlights the need for decentralized and distributive approaches to managing multimedia traffic. Content-sharing via D2D clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. Different studies have established that D2D communication in clusters can improve spectral and energy efficiency, achieve low latency while increasing the capacity of the network. To achieve effective content-sharing among users, appropriate clustering strategies are required. Therefore, the aim is to design and compare clustering approaches for D2D communication targeting content-sharing applications. Currently, most of researched and implemented clustering schemes are centralized or predominantly dependent on Evolved Node B (eNB). This thesis proposes a distributed architecture that supports clustering approaches to incorporate multimedia traffic. A content-sharing network is presented where some D2D User Equipment (DUE) function as content distributors for nearby devices. Two promising techniques are utilized, namely, Content-Centric Networking and Network Virtualization, to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multi-factor clustering algorithm is proposed for grouping the DUEs sharing a common interest. Various performance parameters such as energy consumption, area spectral efficiency, and throughput have been considered for evaluating the proposed algorithm. The effect of number of clusters on the performance parameters is also discussed. The proposed algorithm has been further modified to allow for a trade-off between fairness and other performance parameters. A comprehensive simulation study is presented that demonstrates that the proposed clustering algorithm is more flexible and outperforms several well-known and state-of-the-art algorithms. The clustering process is subsequently evaluated from an individual user’s perspective for further performance improvement. We believe that some users, sharing common interests, are better off with the eNB rather than being in the clusters. We utilize machine learning algorithms namely, Deep Neural Network, Random Forest, and Support Vector Machine, to identify the users that are better served by the eNB and form clusters for the rest of the users. This proposed user segregation scheme can be used in conjunction with most clustering algorithms including the proposed multi-factor scheme. A comprehensive simulation study demonstrates that with such novel user segregation, the performance of individual users, as well as the whole network, can be significantly improved for throughput, energy consumption, and fairness

Opportunistic Spectrum Utilization for Vehicular Communication Networks

Recently, vehicular networks (VANETs), has become the key technology of the next-generation intelligent transportation systems (ITS). By incorporating wireless communication and networking capabilities into automobiles, information can be efficiently and reliably disseminated among vehicles, road side units, and infrastructure, which enables a number of novel applications enhancing the road safety and providing the drivers/passengers with an information-rich environment. With the development of mobile Internet, people want to enjoy the Internet access in vehicles just as anywhere else. This fact, along with the soaring number of connected vehicles and the emerging data-craving applications and services, has led to a problem of spectrum scarcity, as the current spectrum bands for VANETs are difficult to accommodate the increasing mobile data demands. In this thesis, we aim to solve this problem by utilizing extra spectrum bands, which are not originally allocated for vehicular communications. In this case, the spectrum usage is based on an opportunistic manner, where the spectrum is not available if the primary system is active, or the vehicle is outside the service coverage due to the high mobility. We will analyze the features of such opportunistic spectrum, and design efficient protocols to utilize the spectrum for VANETs. Firstly, the application of cognitive radio technologies in VANETs, termed CR-VANETs, is proposed and analyzed. In CR-VANETs, the channel availability is severely affected by the street patterns and the mobility features of vehicles. Therefore, we theoretically analyze the channel availability in urban scenario, and obtain its statistics. Based on the knowledge of channel availability, an efficient channel access scheme for CR-VANETs is then designed and evaluated. Secondly, using WiFi to deliver mobile data, named WiFi offloading, is employed to deliver the mobile data on the road, in order to relieve the burden of the cellular networks, and provide vehicular users with a cost-effective data pipe. Using queueing theory, we analyze the offloading performance with respect to the vehicle mobility model and the users' QoS preferences. Thirdly, we employ device-to-device (D2D) communications in VANETs to further improve the spectrum efficiency. In a vehicular D2D (V-D2D) underlaying cellular network, proximate vehicles can directly communicate with each other with a relatively small transmit power, rather than traversing the base station. Therefore, many current transmissions can co-exist on one spectrum resource block. By utilizing the spatial diversity, the spectrum utilization is greatly enhanced. We study the performance of the V-D2D underlaying cellular network, considering the vehicle mobility and the street pattern. We also investigate the impact of the preference of D2D/cellular mode on the interference and network throughput, and obtain the theoretical results. In summary, the analysis and schemes developed in this thesis are useful to understand the future VANETs with heterogeneous access technologies, and provide important guidelines for designing and deploying such networks