Priority-Based Data Transmission in Wireless Networks using Network Coding
AdvisorWu, Jie, 1961-
Committee memberJi, Bo, 1982-
DepartmentComputer and Information Science
Triangular Network Coding
Unequal Data Protection
Permanent link to this recordhttp://hdl.handle.net/20.500.12613/3369
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AbstractWith the rapid development of mobile devices technology, they are becoming very popular and a part of our everyday lives. These devices, which are equipped with wireless radios, such as cellular and WiFi radios, affect almost every aspect of our lives. People use smartphone and tablets to access the Internet, watch videos, chat with their friends, and etc. The wireless connections that these devices provide is more convenient than the wired connections. However, there are two main challenges in wireless networks: error-prone wireless links and network resources limitation. Network coding is widely used to provide reliable data transmission and to use the network resources efficiently. Network coding is a technique in which the original packets are mixed together using algebraic operations. In this dissertation, we study the applications of network coding in making the wireless transmissions robust against transmission errors and in efficient resource management. In many types of data, the importance of different parts of the data are different. For instance, in the case of numeric data, the importance of the data decreases from the most significant to the least significant bit. Also, in multi-layer videos, the importance of the packets in different layers of the videos are not the same. We propose novel data transmission methods in wireless networks that considers the unequal importance of the different parts of the data. In order to provide robust data transmissions and use the limited resources efficiently, we use random linear network coding technique, which is a type of network coding. In the first part of this dissertation, we study the application of network coding in resource management. In order to use the the limited storage of cache nodes efficiently, we propose to use triangular network coding for content distribution. We also design a scalable video-on-demand system, which uses helper nodes and network coding to provide users with their desired video quality. In the second part, we investigate the application of network coding in providing robust wireless transmissions. We propose symbol-level network coding, in which each packet is partitioned to symbols with different importance. We also propose a method that uses network coding to make multi-layer videos robust against transmission errors.
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Network Update and Service Chain Management in Software Defined NetworksWu, Jie, 1961-; He, Xubin; Ji, Bo, 1982-; Bai, Li (Temple University. Libraries, 2020)Software Defined Networking (SDN) emerged in recent years to fundamentally change how we design, build and manage networks. To maximize the network utilization, its control plane needs to frequently update the data plane via flow migration as the network conditions change dynamically, which is known as network update. Network Function Virtualization (NFV) addresses the problems of traditional expensive hardware appliances by leveraging virtualization technology to implement network functions in software modules (middleboxes). These software modules, also called Virtual Network Functions (VNFs), are provisioned most commonly in modern networks to demonstrate their increasing importance. The technical combination of SDN and NFV enables network service providers to pick service locations from multiple available servers and maneuvers traffic through appropriate VNFs, which is known as VNF deployment. A service chain consists of multiple chained VNFs in some order. VNFs are executed on virtualization platforms, which makes them more prone to error compared with dedicated hardware. As a result, one important issue of service chain is its reliability, meaning that each type of VNF in a service chain acts properly on its function, which is known as service chain resilience. This dissertation lists our research on the above three mentioned topics in order to improve the network performance. Details are as follows: 1. Network Update: SDNs always need to migrate flows to update the network configuration for a better system performance. However, the existing literature does not take flow path overlapping information into consideration when flows’ routes are re-allocated. Consequently, congestion happens, resulting in deadlocks among flows and link resources, which will block the update process and cause severe packet loss. We propose multiple solutions with various kinds of leisure resources in the network. 2. VNF Deployment: We focus on the VNF deployment problem with different settings and constraints, including: (1) network topology; (2) vertex capacity constraint; (3) traffic-changing effect; (4) heterogeneous or homogeneous model for one VNF kind; (5) dependency relations between VNFs. We efficiently deploy VNF instances and at the same time make sure that the processing requirement of all flows are satisfied. 3. Resilient Service Chain Management: One effective way of ensuring VNF robustness is to provision redundancy in the form of deploying backup instances besides active ones. In order to guarantee the service chain reliability, we consider both the server resource allocation and the VNF backup assignment. We aim at minimizing the total cost in terms of transmission delay and rule changes.
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A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio networkZhao, D; Qin, H; Song, B; Han, B; Du, X; Guizani, M; Du, Xiaojiang|0000-0003-4235-9671 (2020-09-02)© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved.