164 research outputs found

    5G optimized caching and downlink resource sharing for smart cities

    Get PDF

    Active Content Popularity Learning via Query-by-Committee for Edge Caching

    Get PDF
    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

    Get PDF
    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 & Tutorial

    The design and optimization of cooperative mobile edge

    Get PDF
    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
    corecore