1,557 research outputs found
Enhanced Inter-Cell Interference Coordination Challenges in Heterogeneous Networks
3GPP LTE-Advanced has started a new study item to investigate Heterogeneous
Network (HetNet) deployments as a cost effective way to deal with the
unrelenting traffic demand. HetNets consist of a mix of macrocells, remote
radio heads, and low-power nodes such as picocells, femtocells, and relays.
Leveraging network topology, increasing the proximity between the access
network and the end-users, has the potential to provide the next significant
performance leap in wireless networks, improving spatial spectrum reuse and
enhancing indoor coverage. Nevertheless, deployment of a large number of small
cells overlaying the macrocells is not without new technical challenges. In
this article, we present the concept of heterogeneous networks and also
describe the major technical challenges associated with such network
architecture. We focus in particular on the standardization activities within
the 3GPP related to enhanced inter-cell interference coordination.Comment: 12 pages, 4 figures, 2 table
A survey of machine learning techniques applied to self organizing cellular networks
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
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
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