2,572 research outputs found

    A Sharing- and Competition-Aware Framework for Cellular Network Evolution Planning

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    Mobile network operators are facing the difficult task of significantly increasing capacity to meet projected demand while keeping CAPEX and OPEX down. We argue that infrastructure sharing is a key consideration in operators' planning of the evolution of their networks, and that such planning can be viewed as a stage in the cognitive cycle. In this paper, we present a framework to model this planning process while taking into account both the ability to share resources and the constraints imposed by competition regulation (the latter quantified using the Herfindahl index). Using real-world demand and deployment data, we find that the ability to share infrastructure essentially moves capacity from rural, sparsely populated areas (where some of the current infrastructure can be decommissioned) to urban ones (where most of the next-generation base stations would be deployed), with significant increases in resource efficiency. Tight competition regulation somewhat limits the ability to share but does not entirely jeopardize those gains, while having the secondary effect of encouraging the wider deployment of next-generation technologies

    TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks

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    Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this paper, we extend the research over BS switching operations, which should match up with traffic load variations. Instead of depending on the dynamic traffic loads which are still quite challenging to precisely forecast, we firstly formulate the traffic variations as a Markov decision process. Afterwards, in order to foresightedly minimize the energy consumption of RANs, we design a reinforcement learning framework based BS switching operation scheme. Furthermore, to avoid the underlying curse of dimensionality in reinforcement learning, a transfer actor-critic algorithm (TACT), which utilizes the transferred learning expertise in historical periods or neighboring regions, is proposed and provably converges. In the end, we evaluate our proposed scheme by extensive simulations under various practical configurations and show that the proposed TACT algorithm contributes to a performance jumpstart and demonstrates the feasibility of significant energy efficiency improvement at the expense of tolerable delay performance.Comment: 11 figures, 30 pages, accepted in IEEE Transactions on Wireless Communications 2014. IEEE Trans. Wireless Commun., Feb. 201

    A survey of machine learning techniques applied to self organizing cellular networks

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    In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

    Performance Analysis of Cell Zooming Based Centralized Algorithm for Energy Efficient in Surabaya

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    The cellular subscribers’s growth over the years increases the traffic volume at Base Stations (BSs) significantly. Typically, in central business district (CBD) area, the traffic load in cellular network in the daytime is relatively heavy, and light in the daynight. But, Base Station still consumes energy normally. It can cause the energy consumption is wasted. On the other hand, energy consumption being an important issue in the world. Because, higher energy consumption contributes on increasing of emission. Thus, it requires for efficiency energy methods by switching BS dynamically. The methods are Lower-to-Higher (LH) and Higher-to-Lower (HL) scheme on centralized algorithm. In this paper propose cell zooming technique  which can adjusts the cell size dynamic based on traffic condition. The simulation result by using Lower-to-Higher (LH) scheme can save the network energy consumption up to 70.7917% when the number of mobile user is 37 users and 0% when the number of mobile user is more than or equal to 291 users. While, Higher-to-Lower (HL) scheme can save the network energy consumption up to 32.3303% when the number of mobile user is 37 users and 0% when the number of mobile user is more than or equal to 292 users. From both of these schemes, we can analyze that by using Lower-to-Higher (LH) scheme reduces energy consumption greater than using Higher-to-Lower (HL) scheme. Nevertheless, both of them can be implemented for energy-efficient method in CBD area. Eventually, the cell zooming technique by using two schemes on centralized algorithm which leads to green cellular network in Surabaya is investigated

    Energy Saving Strategies on Mobile Devices

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    On Content-centric Wireless Delivery Networks

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    The flux of social media and the convenience of mobile connectivity has created a mobile data phenomenon that is expected to overwhelm the mobile cellular networks in the foreseeable future. Despite the advent of 4G/LTE, the growth rate of wireless data has far exceeded the capacity increase of the mobile networks. A fundamentally new design paradigm is required to tackle the ever-growing wireless data challenge. In this article, we investigate the problem of massive content delivery over wireless networks and present a systematic view on content-centric network design and its underlying challenges. Towards this end, we first review some of the recent advancements in Information Centric Networking (ICN) which provides the basis on how media contents can be labeled, distributed, and placed across the networks. We then formulate the content delivery task into a content rate maximization problem over a share wireless channel, which, contrasting the conventional wisdom that attempts to increase the bit-rate of a unicast system, maximizes the content delivery capability with a fixed amount of wireless resources. This conceptually simple change enables us to exploit the "content diversity" and the "network diversity" by leveraging the abundant computation sources (through application-layer encoding, pushing and caching, etc.) within the existing wireless networks. A network architecture that enables wireless network crowdsourcing for content delivery is then described, followed by an exemplary campus wireless network that encompasses the above concepts.Comment: 20 pages, 7 figures,accepted by IEEE Wireless Communications,Sept.201
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