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

Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks

The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm which incorporates the cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets. Characterizing the cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improving energy efficiency (EE) in H-CRANs. An energy-efficient optimization objective function with individual fronthaul capacity and inter-tier interference constraints is presented in this paper for queue-aware multimedia H-CRANs. To solve this non-convex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework. Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power and inter-tier interference constraints, which can be regarded as the weighted sum EE maximization problem and solved by a generalized weighted minimum mean square error approach. The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Mobility and resource management for 5G heterogeneous networks

The conventional topology of current cellular networks is a star structure, where central control points usually serve as base stations (BSs). This provides the advantage of simplicity while still providing quality of service (QoS). For next-generation networks, however, this topology is disadvantageous and difficult to use due to the insufficient availability of network access. The hybrid topology radio network will thus naturally be the future mobile access network that can help to overcome current and future challenges efficiently. Therefore, relay technology can play an important role in a hybrid cellular network topology. Today, with the recent long-term evolution-advanced (LTE-A) standards, the 3rd Generation Partnership Project (3GPP) supports a single-hop relay technology in which the radio access link between the BS and users is relayed by only one relay station (RS). With the help of multi-hop relay, however, the radio link between the BS and users can be extended to more than two hops to improve the coverage and network capacity. Multiple hops to transmit data to and from the corresponding BS results in the reduction of path loss. However, using a multi-hop relay system requires more radio resources to transmit data through different hops. More interference is also created due to a greater number of simultaneous transmissions in the network. New mobility and resource management schemes are thus important for achieving a high QoS while increasing the whole network capacity. In the first part, the problem of relay selection and radio resource allocation is studied, and choosing how the bandwidth should be shared between direct, backhaul, and access links in multi-hop relay networks is discussed. In such a network, resource allocation plays a critical role because it manages channel access in both time and frequency domains and determines how resources are allocated for different links. The proposed solution includes a nonlinear programming technique and a heuristic method. First, the problem formulation of resource allocation and relay selection is presented to provide an integrated framework for multi-hop relay networks. Second, an analytical solution to the problem is presented using a nonlinear programming technique. Finally, an iterative two-stage algorithm is presented to address the joint resource allocation and relay selection problem in multi-hop relay networks Under backhaul and capacity limitation constraints. In particular, the first stage proposed a fast approximation analytical solution for a resource allocation algorithm that takes into account the trade-off between the optimality and the complexity of the multi-hop relay architecture; the second stage presented a heuristic relay selection strategy that considers the RS load and helps to keep the relay from being overloaded is proposed. In the second part, the mobility problem in downlink multi-hop relay networks is addressed. In addition to the resource allocation issue, the relay selection problem is studied from a network layer perspective. Therefore, this part includes the issue of radio path selection. As an alternative to the heuristic algorithm developed in the previous part, the presented work describes the development and evaluation of a relay-selection scheme based on a Markov decision process (MDP) that considers the RS load and the existing radio-link path to improve handoff performance. First, the problem formulation of resource allocation and relay selection is presented. Second, an MDP mathematical model is developed to solve the relay selection problem in a decentralized way and to make the selection process simple. This relay selection scheme has the objective of maintaining the throughput and ensuring seamless mobility and service continuity to all mobile terminals while reducing the handoff frequency and improving handoff performance. In the third part, the admission and power control problem of a general heterogeneous network (HetNet) consisting of several small cells (SCs) is solved. Compared to the first two parts of this work, the system is expanded from a multi-hop RS to a general SC context. This part therefore focuses only on the access link problem, assuming the capacity of the SC backhaul links are large enough not to be bottlenecks. This part mainly deals with the problem of how to maximize the number of admitted users in an overloaded system while minimizing the transmit power given a certain QoS level. First, the problem is formulated to address concerns about QoS requirements in a better way. Second, a Voronoi-based user association scheme for maximizing the number of admitted users in the system under QoS and capacity limitation constraints is proposed to find near-optimal solutions. Finally, a twostage algorithm is presented to address the joint admission and power control problem in a downlink heterogeneous SC network. In particular, the first stage proposes a dynamic call admission control policy that considers the SC load and call-level QoS while also helping to keep the system from being overloaded. The second stage presents an adaptive power allocation strategy that considers both user distribution and the density of SCs in HetNets. Finally, the proposed solutions are evaluated using extensive numerical simulations, and the numerical results are presented to provide a comparison with related works found in the literature

Efficient Scheduling Algorithms for Wireless Resource Allocation and Virtualization in Wireless Networks

The continuing growth in demand for better mobile broadband experiences has motivated rapid development of radio-access technologies to support high data rates and improve quality of service (QoS) and quality of experience (QoE) for mobile users. However, the modern radio-access technologies pose new challenges to mobile network operators (MNO) and wireless device designers such as reducing the total cost of ownership while supporting high data throughput per user, and extending battery life-per-charge of the mobile devices. In this thesis, a variety of optimization techniques aimed at providing innovative solutions for such challenges are explored. The thesis is divided into two parts. In the first part, the challenge of extending battery life-per-charge is addressed. Optimal and suboptimal power-efficient schedulers that minimize the total transmit power and meet the QoS requirements of the users are presented. The second outlines the benefits and challenges of deploying wireless resource virtualization (WRV) concept as a promising solution for satisfying the growing demand for mobile data and reducing capital and operational costs. First, a WRV framework is proposed for single cell zone that is able to centralize and share the spectrum resources between multiple MNOs. Consequently, several WRV frameworks are proposed, which virtualize the spectrum resource of the entire network for cloud radio access network (C-RAN)- one of the front runners for the next generation network architecture. The main contributions of this thesis are in designing optimal and suboptimal solutions for the aforementioned challenges. In most cases, the optimal solutions suffer from high complexity, and therefore low-complexity suboptimal solutions are provided for practical systems. The optimal solutions are used as benchmarks for evaluating the suboptimal solutions. The results prove that the proposed solutions effectively contribute in addressing the challenges caused by the demand for high data rates and power transmission in mobile networks