Efficient Traffic Management Algorithms for the Core Network using Device-to-Device Communication and Edge Caching

Exponentially growing number of communicating devices and the need for faster, more reliable and secure communication are becoming major challenges for current mobile communication architecture. More number of connected devices means more bandwidth and a need for higher Quality of Service (QoS) requirements, which bring new challenges in terms of resource and traffic management. Traffic offload to the edge has been introduced to tackle this demand-explosion that let the core network offload some of the contents to the edge to reduce the traffic congestion. Device-to-Device (D2D) communication and edge caching, has been proposed as promising solutions for offloading data. D2D communication refers to the communication infrastructure where the users in proximity communicate with each other directly. D2D communication improves overall spectral efficiency, however, it introduces additional interference in the system. To enable D2D communication, efficient resource allocation must be introduced in order to minimize the interference in the system and this benefits the system in terms of bandwidth efficiency. In the first part of this thesis, low complexity resource allocation algorithm using stable matching is proposed to optimally assign appropriate uplink resources to the devices in order to minimize interference among D2D and cellular users. Edge caching has recently been introduced as a modification of the caching scheme in the core network, which enables a cellular Base Station (BS) to keep copies of the contents in order to better serve users and enhance Quality of Experience (QoE). However, enabling BSs to cache data on the edge of the network brings new challenges especially on deciding on which and how the contents should be cached. Since users in the same cell may share similar content-needs, we can exploit this temporal-spatial correlation in the favor of caching system which is referred to local content popularity. Content popularity is the most important factor in the caching scheme which helps the BSs to cache appropriate data in order to serve the users more efficiently. In the edge caching scheme, the BS does not know the users request-pattern in advance. To overcome this bottleneck, a content popularity prediction using Markov Decision Process (MDP) is proposed in the second part of this thesis to let the BS know which data should be cached in each time-slot. By using the proposed scheme, core network access request can be significantly reduced and it works better than caching based on historical data in both stable and unstable content popularity

A dynamic graph optimization framework for multihop device-to-device communication underlaying cellular networks

With emerging demands for local area and popular content sharing services, multihop device-to-device communication is conceived as a vital component of next-generation cellular networks to improve spectral reuse, bring hop gains, and enhance system capacity. Ripening these benefits depends on fundamentally understanding its potential performance impacts and efficiently solving several main technical problems. Aiming to establish a new paradigm for the analysis and design of multihop D2D communications, in this article, we propose a dynamic graph optimization framework that enables the modeling of large-scale systems with multiple D2D pairs and node mobility patterns. By inherently modeling the main technological problems for multihop D2D communications, this framework benefits investigation of theoretical performance limits and studying the optimal system design. Furthermore, these achievable benefits are demonstrated by examples of simulations under a realistic multihop D2D communication underlaying cellular network

Bio-Inspired Resource Allocation for Relay-Aided Device-to-Device Communications

The Device-to-Device (D2D) communication principle is a key enabler of direct localized communication between mobile nodes and is expected to propel a plethora of novel multimedia services. However, even though it offers a wide set of capabilities mainly due to the proximity and resource reuse gains, interference must be carefully controlled to maximize the achievable rate for coexisting cellular and D2D users. The scope of this work is to provide an interference-aware real-time resource allocation (RA) framework for relay-aided D2D communications that underlay cellular networks. The main objective is to maximize the overall network throughput by guaranteeing a minimum rate threshold for cellular and D2D links. To this direction, genetic algorithms (GAs) are proven to be powerful and versatile methodologies that account for not only enhanced performance but also reduced computational complexity in emerging wireless networks. Numerical investigations highlight the performance gains compared to baseline RA methods and especially in highly dense scenarios which will be the case in future 5G networks.Comment: 6 pages, 6 figure

The Coexistence of D2D Communication under Heterogeneous Cellular Networks (HetNets)

Device-to-Device (D2D) communication is a promising technique for supporting the stringent requirements of the fifth-generation cellular network (5G). This new technique has garnered significant attention in cellular network standards for proximity communication as a means to improve cellular spectrum utilization, to decrease user equipment energy consumption, and to reduce end-to-end delay. This dissertation reports an investigation of D2D communication coexistence under 5G heterogeneous cellular network (HetNets) in terms of spectrum allocation and energy efficiency. The work reported herein describes a low-complexity D2D resource allocation algorithm for downlink (DL) resource reuse that can be leveraged to improve network throughput. Notably, cross-tier interference was considered when establishing D2D communication (e.g., macro base station to D2D links; small base station to D2D links; and D2D communication to cellular links served by the macro and small base stations). An allocation algorithm was introduced to reduce interference from D2D to cellular when a single D2D link is sharing cellular resources. Performance of the proposed algorithm was evaluated and compared to various resource allocations. Simulation results demonstrated that the proposed algorithm improves overall system throughput. This allocation algorithm achieved a near-optimal solution when compared with a brute force approach. This dissertation also presents a novel framework for optimizing the energy efficiency of D2D communication coexistence with HetNets in DL transmission. This optimization problem was mathematically formulated in terms of mode selection, power control, and resources allocation (i.e., NP-hard problem). The optimization fraction problem was simplified based on network load and was solved using various optimization methods. An innovative dynamic mode selection based on Fuzzy clustering was also introduced. Proposed scheme performance was evaluated and compared to the standard algorithm. Simulation validated the advantage of the proposed framework in terms of performance gain in both energy efficiency and the number of successfully connected D2D users. Moreover, the energy efficiency of HetNets with D2D compatibility was improved. Finally, this dissertation details a stochastic analytical model for an LTE scheduler with D2D communication. By assuming exponential distributions for users scheduling time, a throughput estimation model was developed using two-dimensional Continuous Time Markov chains (2D-CTMC) of birth-death type. The proposed model will predict the expected number of D2D operated in dedicated and reuse mode, as well as the systems long-term throughput

Survey on the state-of-the-art in device-to-device communication: A resource allocation perspective

Device to Device (D2D) communication takes advantage of the proximity between the communicating devices in order to achieve efficient resource utilization, improved throughput and energy efficiency, simultaneous serviceability and reduced latency. One of the main characteristics of D2D communication is reuse of the frequency resource in order to improve spectral efficiency of the system. Nevertheless, frequency reuse introduces significantly high interference levels thus necessitating efficient resource allocation algorithms that can enable simultaneous communication sessions through effective channel and/or power allocation. This survey paper presents a comprehensive investigation of the state-of-the-art resource allocation algorithms in D2D communication underlaying cellular networks. The surveyed algorithms are evaluated based on heterogeneous parameters which constitute the elementary features of a resource allocation algorithm in D2D paradigm. Additionally, in order to familiarize the readers with the basic design of the surveyed resource allocation algorithms, brief description of the mode of operation of each algorithm is presented. The surveyed algorithms are divided into four categories based on their technical doctrine i.e., conventional optimization based, Non-Orthogonal-MultipleAccess (NOMA) based, game theory based and machine learning based techniques. Towards the end, several open challenges are remarked as the future research directions in resource allocation for D2D communication

Optimization Modeling and Machine Learning Techniques Towards Smarter Systems and Processes

The continued penetration of technology in our daily lives has led to the emergence of the concept of Internet-of-Things (IoT) systems and networks. An increasing number of enterprises and businesses are adopting IoT-based initiatives expecting that it will result in higher return on investment (ROI) [1]. However, adopting such technologies poses many challenges. One challenge is improving the performance and efficiency of such systems by properly allocating the available and scarce resources [2, 3]. A second challenge is making use of the massive amount of data generated to help make smarter and more informed decisions [4]. A third challenge is protecting such devices and systems given the surge in security breaches and attacks in recent times [5]. To that end, this thesis proposes the use of various optimization modeling and machine learning techniques in three different systems; namely wireless communication systems, learning management systems (LMSs), and computer network systems. In par- ticular, the first part of the thesis posits optimization modeling techniques to improve the aggregate throughput and power efficiency of a wireless communication network. On the other hand, the second part of the thesis proposes the use of unsupervised machine learning clustering techniques to be integrated into LMSs to identify unengaged students based on their engagement with material in an e-learning environment. Lastly, the third part of the thesis suggests the use of exploratory data analytics, unsupervised machine learning clustering, and supervised machine learning classification techniques to identify malicious/suspicious domain names in a computer network setting. The main contributions of this thesis can be divided into three broad parts. The first is developing optimal and heuristic scheduling algorithms that improve the performance of wireless systems in terms of throughput and power by combining wireless resource virtualization with device-to-device and machine-to-machine communications. The second is using unsupervised machine learning clustering and association algorithms to determine an appropriate engagement level model for blended e-learning environments and study the relationship between engagement and academic performance in such environments. The third is developing a supervised ensemble learning classifier to detect malicious/suspicious domain names that achieves high accuracy and precision

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