Context-Aware Resource Management and Performance Analysis of Millimeter Wave and Sub-6 GHz Wireless Networks
| dc.contributor.author | Semiari, Omid | en |
| dc.contributor.committeechair | Saad, Walid | en |
| dc.contributor.committeemember | Dhillon, Harpreet Singh | en |
| dc.contributor.committeemember | Reed, Jeffrey H. | en |
| dc.contributor.committeemember | Zheng, Xiaoyu | en |
| dc.contributor.committeemember | Plassmann, Paul E. | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2018-12-21T07:00:29Z | en |
| dc.date.available | 2018-12-21T07:00:29Z | en |
| dc.date.issued | 2017-08-28 | en |
| dc.description.abstract | Emerging wireless networks are foreseen as an integration of heterogeneous spectrum bands, wireless access technologies, and backhaul solutions, as well as a large-scale interconnection of devices, people, and vehicles. Such a heterogeneity will range from the proliferation of multi-tasking user devices with different capabilities such as smartphones and tablets to the deployment of multi-mode access points that can operate over heterogeneous frequency bands spanning both sub-6 GHz microwave and high-frequency millimeter wave (mmW) frequencies bands. This heterogeneous ecosystem will yield new challenges and opportunities for wireless resource management. On the one hand, resource management can exploit user and network-specific context information, such as application type, social metrics, or operator pricing, to develop application-driven, context-aware networks. Similarly, multiple frequency bands can be leveraged to meet the stringent and heterogeneous quality-of-service (QoS) requirements of the new wireless services such as video streaming and interactive gaming. On the other hand, resource management in such heterogeneous, multi-band, and large-scale wireless systems requires distributed frameworks that can effectively utilize all available resources while operating with manageable overhead. The key goal of this dissertation is therefore to develop novel, self-organizing, and low-complexity resource management protocols -- using techniques from matching theory, optimization, and machine learning -- to address critical resource allocation problems for emerging heterogeneous wireless systems while explicitly modeling and factoring diverse network context information. Towards achieving this goal, this dissertation makes a number of key contributions. First, a novel context-aware scheduling framework is developed for enabling dual-mode base stations to efficiently and jointly utilize mmW and microwave frequency resources while maximizing the number of user applications whose stringent delay requirements are satisfied. The results show that the proposed approach will be able to significantly improve the QoS per application and decrease the outage probability. Second, novel solutions are proposed to address both network formation and resource allocation problems in multi-hop wireless backhaul networks that operate at mmW frequencies. The proposed framework motivates collaboration among multiple network operators by resource sharing to reduce the cost of backhauling, while jointly accounting for both wireless channel characteristics and economic factors. Third, a novel framework is proposed to exploit high-capacity mmW communications and device-level caching to minimize handover failures as well as energy consumption by inter-frequency measurements, and to provide seamless mobility in dense heterogeneous mmW-microwave small cell networks (SCNs). Fourth, a new cell association algorithm is proposed, based on matching theory with minimum quota constraints, to optimize load balancing in integrated mmW-microwave networks. Fifth, a novel medium access control (MAC) protocol is proposed to dynamically manage the wireless local area network (WLAN) traffic jointly over the unlicensed 60 GHz mmW and sub-6 GHz bands to maximize the saturation throughput and minimize the delay experienced by users. Finally, a novel resource management approach is proposed to optimize device-to-device (D2D) communications and improve traffic offload in heterogeneous wireless SCNs by leveraging social context information that is dynamically learned by the network. In a nutshell, by providing novel, context-aware, and self-organizing frameworks, this dissertation addresses fundamentally challenging resource management problems that mainly stem from large scale, stringent service requirements, and heterogeneity of next-generation wireless networks. | en |
| dc.description.abstractgeneral | The emergence of bandwidth-intensive applications along with vast proliferation of smart, multi-tasking handhelds have strained the capacity of wireless networks. Furthermore, the landscape of wireless communications is shifting towards providing connectivity, not only to humans, but also to automated cars, drones, and robots, among other critical applications. These new technologies will enable devices, machines, and things to be more intuitive, while being more capable, in order to improve the quality of life for human. For example, in future networked life, smartphones will predict our needs and help us with providing timely and relevant information from our surrounding. As an another example, autonomous vehicles and smart transportation systems with large number of connected safety features will minimize road incidents and yield a safe and joyful driving experience. Turning such emerging services into reality will require new technology innovations that provide high efficiency and substantial levels of scalability. To this end, wireless communication is the key candidate to provide large-scale and ubiquitous connectivity. However, existing wireless networks operate at congested microwave (µW) frequency bands and cannot manage the exponential growth in wireless data traffic or support low latency and ultra-high reliability communications, required by many emerging critical applications. Therefore, the goal of this dissertation is to develop novel network resource utilization frameworks to efficiently manage the heterogeneous traffic in next-generation wireless networks, while meeting their stringent quality-of-service (QoS) requirements. This transformative, fundamental research will expedite the deployment of communications at very high frequencies, at the millimeter wave (mmW) frequency bands, in next-generation wireless networks. The developed frameworks will advance new concepts from matching theory and machine learning for resource management in cellular networks, wireless local area networks (WLANs), and the intersection of these systems at both mmW and µW unlicensed frequency bands. This multi-band networking will leverage the synergies between mmW and µW wireless networks to provide robust and cost-effective solutions that enable the support of heterogeneous traffic from future wireless services. The anticipated results will transform the way in which spectral and time resources are used in both cellular networks and WLANs. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:11053 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/86482 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Heterogeneous Networks | en |
| dc.subject | Millimeter Wave Communications | en |
| dc.subject | Cellular Networks | en |
| dc.subject | Matching Theory | en |
| dc.subject | Context Information | en |
| dc.title | Context-Aware Resource Management and Performance Analysis of Millimeter Wave and Sub-6 GHz Wireless Networks | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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