164 research outputs found
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Hybrid, Proactive In-Network Caching for Mobile On-Demand Video Streaming
Mobile video streaming has become an essential application in mobile wireless networks,making up most of the mobile data of today’s Internet traffic. Studies have shown that mobile video data is projected to make up about 78 percent of the global mobile data traffic, and that global mobile data traffic is expected to increase sevenfold by 2021.Massive small cell base station (SBS) deployments have emerged as a potential solution promising to fulfill these unprecedented mobile data demands, by offering great coverage enhancements and maintaining high quality of video streaming. However, due to relatively small cell sizes and high user mobility, mobile video streaming in dense SBS networks faces fundamental challenges such as intermittent connectivity and frequent handoffs, causing degradation in video streaming quality. In this thesis, we tackle this issue by introducing a hybrid proactive in-network caching framework that stores some popular videos at the edge of the network, namely at the SBSs, while also pre-caching video contents in advance to better service mobile users. The proposed framework essentially reduces the need for bringing every requested video from the core (original)network, which results in alleviating network congestion by reducing back-haul traffic and in improving mobile video streaming experience by avoiding service discontinuity during handoffs. We develop a simulation framework using MATLAB to study the performance of the proposed hybrid proactive caching technique, and show using simulations that the proposed technique can effectively improve video quality of experience and reduce back-haul traffic.Keywords: hybrid proactive caching, Video Quality of Experience, Small-cell Base Station (SBS)., Mobile video streamin
Active Content Popularity Learning via Query-by-Committee for Edge Caching
Edge caching has received much attention as an effective solution to face the stringent latency requirements in 5G networks due to the proliferation of handset devices as well as data-hungry applications. One of the challenges in edge caching systems is to optimally cache strategic contents to maximize the percentage of total requests served by the edge caches. To enable the optimal caching strategy, we propose an Active Learning approach (AL) to learn and design an accurate content request prediction algorithm. Specifically, we use an AL based Query-by-committee (QBC) matrix completion algorithm with a strategy of querying the most informative missing entries of the content popularity matrix. The proposed AL framework leverage's the trade-off between exploration and exploitation of the network, and learn the user's preferences by posing queries or recommendations. Later, it exploits the known information to maximize the system performance. The effectiveness of proposed AL based QBC content learning algorithm is demonstrated via numerical results
A Comprehensive Survey on Moving Networks
The unprecedented increase in the demand for mobile data, fuelled by new
emerging applications such as HD video streaming and heightened online
activities has caused massive strain on the existing cellular networks. As a
solution, the 5G technology has been introduced to improve network performance
through various innovative features such as mmWave spectrum and HetNets. In
essence, HetNets include several small cells underlaid within macro-cell to
serve densely populated regions. Recently, a mobile layer of HetNet has been
under consideration by the researchers and is often referred to as moving
networks. Moving networks comprise of mobile cells that are primarily
introduced to improve QoS for commuting users inside public transport because
the QoS is deteriorated due to vehicular penetration losses. Furthermore, the
users inside fast moving public transport also exert excessive load on the core
network due to large group handovers. To this end, mobile cells will play a
crucial role in reducing overall handover count and will help in alleviating
these problems by decoupling in-vehicle users from the core network.
To date, remarkable research results have been achieved by the research
community in addressing challenges linked to moving networks. However, to the
best of our knowledge, a discussion on moving networks in a holistic way is
missing in the current literature. To fill the gap, in this paper, we
comprehensively survey moving networks. We cover the technological aspects and
their applications in the futuristic applications. We also discuss the
use-cases and value additions that moving networks may bring to future cellular
architecture and identify the challenges associated with them. Based on the
identified challenges we discuss the future research directions.Comment: This survey has been submitted to IEEE Communications Surveys &
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The design and optimization of cooperative mobile edge
As the world is charging towards the Internet of Things (IoT) era, an enormous amount of sensors will be rapidly empowered with internet connectivity. Besides the fact that the end devices are getting more diverse, some of them are also becoming more powerful, such that they can function as standalone mobile computing units with multiple wireless network interfaces. At the network end, various facilities are also pushed to the mobile edge to foster internet connections. Distributed small scale cloud resources and green energy harvesters can be directly attached to the deployed heterogeneous base stations.
Different from the traditional wireless access networks, where the only dynamics come from the user mobility, the evolving mobile edge will be operated in the constantly changing and volatile environment. The harvested green energy will be highly dependent on the available energy sources, and the dense deployment of a variety of wireless access networks will result in intense radio resource contention. Consequently, the wireless networks are facing great challenges in terms of capacity, latency, energy/spectrum efficiency, and security. Equivalently, balancing the dynamic network resource demand and supply is essential to the smooth network operation.
Leveraging the broadcasting nature of wireless data transmission, network nodes can cooperate with each other by either allowing users to connect with multiple base stations simultaneously or offloading user workloads to neighboring base stations. Moreover, grid facilitated and radio frequency signal enabled renewable energy sharing among network nodes are introduced in this dissertation. In particular, the smart grid can transfer the green energy harvested by each individual network node from one place to another. The network node can also transmit energy from one to another using radio frequency energy transfer.
This dissertation addresses the cooperative network resource management to improve the energy efficiency of the mobile edge. First, the energy efficient cooperative data transmission scheme is designed to cooperatively allocate the radio resources of the wireless networks, including spectrum and power, to the mobile users. Then, the cooperative data transmission and wireless energy sharing scheme is designed to optimize both the energy and data transmission in the network. Finally, the cooperative data transmission and wired energy sharing scheme is designed to optimize the energy flow within the smart grid and the data transmission in the network.
As future work, how to motivate multiple parties to cooperate and how to guarantee the security of the cooperative mobile edge is discussed. On one hand, the incentive scheme for each individual network node with distributed storage and computing resources is designed to improve network performance in terms of latency. On the other hand, how to leverage network cooperation to balance the tradeoff between efficiency (energy efficiency and latency) and security (confidentiality and privacy) is expounded
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