1,375 research outputs found
Leveraging intelligence from network CDR data for interference aware energy consumption minimization
Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo
Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks
In this paper, we study the problem of cooperative interference management in
an OFDMA two-tier small cell network. In particular, we propose a novel
approach for allowing the small cells to cooperate, so as to optimize their
sum-rate, while cooperatively satisfying their maximum transmit power
constraints. Unlike existing work which assumes that only disjoint groups of
cooperative small cells can emerge, we formulate the small cells' cooperation
problem as a coalition formation game with overlapping coalitions. In this
game, each small cell base station can choose to participate in one or more
cooperative groups (or coalitions) simultaneously, so as to optimize the
tradeoff between the benefits and costs associated with cooperation. We study
the properties of the proposed overlapping coalition formation game and we show
that it exhibits negative externalities due to interference. Then, we propose a
novel decentralized algorithm that allows the small cell base stations to
interact and self-organize into a stable overlapping coalitional structure.
Simulation results show that the proposed algorithm results in a notable
performance advantage in terms of the total system sum-rate, relative to the
noncooperative case and the classical algorithms for coalitional games with
non-overlapping coalitions
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation
This paper presents a method for automated healing as part of off-line
automated troubleshooting. The method combines statistical learning with
constraint optimization. The automated healing aims at locally optimizing radio
resource management (RRM) or system parameters of cells with poor performance
in an iterative manner. The statistical learning processes the data using
Logistic Regression (LR) to extract closed form (functional) relations between
Key Performance Indicators (KPIs) and Radio Resource Management (RRM)
parameters. These functional relations are then processed by an optimization
engine which proposes new parameter values. The advantage of the proposed
formulation is the small number of iterations required by the automated healing
method to converge, making it suitable for off-line implementation. The
proposed method is applied to heal an Inter-Cell Interference Coordination
(ICIC) process in a 3G Long Term Evolution (LTE) network which is based on
soft-frequency reuse scheme. Numerical simulations illustrate the benefits of
the proposed approach.Comment: IEEE Transactions On Vehicular Technology 2010 IEEE transactions on
vehicular technolog
Interference Management Based on RT/nRT Traffic Classification for FFR-Aided Small Cell/Macrocell Heterogeneous Networks
Cellular networks are constantly lagging in terms of the bandwidth needed to
support the growing high data rate demands. The system needs to efficiently
allocate its frequency spectrum such that the spectrum utilization can be
maximized while ensuring the quality of service (QoS) level. Owing to the
coexistence of different types of traffic (e.g., real-time (RT) and
non-real-time (nRT)) and different types of networks (e.g., small cell and
macrocell), ensuring the QoS level for different types of users becomes a
challenging issue in wireless networks. Fractional frequency reuse (FFR) is an
effective approach for increasing spectrum utilization and reducing
interference effects in orthogonal frequency division multiple access networks.
In this paper, we propose a new FFR scheme in which bandwidth allocation is
based on RT/nRT traffic classification. We consider the coexistence of small
cells and macrocells. After applying FFR technique in macrocells, the remaining
frequency bands are efficiently allocated among the small cells overlaid by a
macrocell. In our proposed scheme, total frequency-band allocations for
different macrocells are decided on the basis of the traffic intensity. The
transmitted power levels for different frequency bands are controlled based on
the level of interference from a nearby frequency band. Frequency bands with a
lower level of interference are assigned to the RT traffic to ensure a higher
QoS level for the RT traffic. RT traffic calls in macrocell networks are also
given a higher priority compared with nRT traffic calls to ensure the low
call-blocking rate. Performance analyses show significant improvement under the
proposed scheme compared with conventional FFR schemes
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