Wireless Communications in the Era of Big Data

The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all the aspects of the wireless system design, such as spectrum efficiency, computing capabilities and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace such a "bigdata" era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing the bigdata traffic in wireless networks. On the other hand, instead of viewing mobile bigdata as a unwanted burden, we introduce methods to capitalize from the vast data traffic, for building a bigdata-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications Magazin

TANGO: Performance and Fault Management in Cellular Networks through Cooperation between Devices and Edge Computing Nodes

Cellular networks have become an essential part of our lives. With increasing demands on its available bandwidth, we are seeing failures and performance degradations for data and voice traffic on the rise. In this paper, we propose the view that fog computing, integrated in the edge components of cellular networks, can partially alleviate this situation. In our vision, some data gathering and data analytics capability will be developed at the edge of the cellular network and client devices and the network using this edge capability will coordinate to reduce failures and performance degradations. We also envisage proactive management of disruptions including prediction of impending events of interest (such as, congestion or call drop) and deployment of appropriate mitigation actions. We show that a simple streaming media pre-caching service built using such device-fog cooperation significantly expands the number of streaming video users that can be supported in a nominal cellular network of today

QoS Provisioning for Multi-Class Traffic in Wireless Networks

Physical constraints, bandwidth constraints and host mobility all contribute to the difficulty of providing Quality of Service (QoS) guarantees in wireless networks. There is a growing demand for wireless networks to support all the services that are available on wired networks. These diverse services, such as email, instant messaging, web browsing, video conferencing, telephony and paging all place different demands on the network, making QoS provisioning for wireless networks that carry multiple classes of traffic a complex problem. We have developed a set of admission control and resource reservation schemes for QoS provisioning in multi-class wireless networks. We present three variations of a novel resource borrowing scheme for cellular networks that exploits the ability of some multimedia applications to adapt to transient fluctuations in the supplied resources. The first of the schemes is shown to be proportionally fair: the second scheme is max-min fair. The third scheme for cellular networks uses knowledge about the relationship between streams that together comprise a multimedia session in order to further improve performance. We also present a predictive resource reservation scheme for LEO satellite networks that exploits the regularity of the movement patterns of mobile hosts in LEO satellite networks. We have developed the cellular network simulator (CNS) for evaluating call-level QoS provisioning schemes. QoS at the call-level is concerned with call blocking probability (CBP), call dropping probability (CDP), and supplied bandwidth. We introduce two novel QoS parameters that relate to supplied bandwidth—the average percent of desired bandwidth supplied (DBS), and the percent of time spent operating at the desired bandwidth level (DBT)