27,350 research outputs found
Hierarchical Resource Allocation Framework for Hyper-Dense Small Cell Networks
This paper considers joint power control and subchannel allocation for co-tier interference
mitigation in extremely dense small cell networks, which is formulated as a combinatorial optimization problem. Since it is intractable to obtain the globally optimum assignment policy for existing techniques due to the huge computation and communication overheads in ultra-dense scenario, in this paper, we propose a hierarchical resource allocation framework to achieve a desirable solution. Speci cally, the solution is obtained by dividing the original optimization problem into four stages in partially distributed manner. First, we propose a divide-and-conquer strategy by invoking clustering technique to decompose
the dense network into smaller disjoint clusters. Then, within each cluster, one of the small cell access points is elected as a cluster head to carry out intra-cluster subchannel allocation with a low-complexity algorithm. To tackle the issue of inter-cluster interference, we further develop a distributed learning-base coordination mechanism. Moreover, a local power adjustment scheme is also presented to improve the system performance. Numerical results verify the ef ciency of the proposed hierarchical scheme, and demonstrate that our solution outperforms the state-of-the-art methods, especially for hyper-dense networks
Spectral Efficient and Energy Aware Clustering in Cellular Networks
The current and envisaged increase of cellular traffic poses new challenges
to Mobile Network Operators (MNO), who must densify their Radio Access Networks
(RAN) while maintaining low Capital Expenditure and Operational Expenditure to
ensure long-term sustainability. In this context, this paper analyses optimal
clustering solutions based on Device-to-Device (D2D) communications to mitigate
partially or completely the need for MNOs to carry out extremely dense RAN
deployments. Specifically, a low complexity algorithm that enables the creation
of spectral efficient clusters among users from different cells, denoted as
enhanced Clustering Optimization for Resources' Efficiency (eCORE) is
presented. Due to the imbalance between uplink and downlink traffic, a
complementary algorithm, known as Clustering algorithm for Load Balancing
(CaLB), is also proposed to create non-spectral efficient clusters when they
result in a capacity increase. Finally, in order to alleviate the energy
overconsumption suffered by cluster heads, the Clustering Energy Efficient
algorithm (CEEa) is also designed to manage the trade-off between the capacity
enhancement and the early battery drain of some users. Results show that the
proposed algorithms increase the network capacity and outperform existing
solutions, while, at the same time, CEEa is able to handle the cluster heads
energy overconsumption
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
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