549 research outputs found
Self-optimizing load balancing with backhaul-constrained radio access networks
Self-Organizing Network (SON) technology aims at autonomously deploying,
optimizing and repairing the Radio Access Networks (RAN). SON algorithms
typically use Key Performance Indicators (KPIs) from the RAN. It is shown that
in certain cases, it is essential to take into account the impact of the
backhaul state in the design of the SON algorithm. We revisit the Base Station
(BS) load definition taking into account the backhaul state. We provide an
analytical formula for the load along with a simple estimator for both elastic
and guaranteed bit-rate (GBR) traffic. We incorporate the proposed load
estimator in a self-optimized load balancing algorithm. Simulation results for
a backhaul constrained heterogeneous network illustrate how the correct load
definition can guarantee a proper operation of the SON algorithm.Comment: Wireless Communications Letters, IEEE, 201
Distributed coordination of self-organizing mechanisms in communication networks
The fast development of the Self-Organizing Network (SON) technology in
mobile networks renders the problem of coordinating SON functionalities
operating simultaneously critical. SON functionalities can be viewed as control
loops that may need to be coordinated to guarantee conflict free operation, to
enforce stability of the network and to achieve performance gain. This paper
proposes a distributed solution for coordinating SON functionalities. It uses
Rosen's concave games framework in conjunction with convex optimization. The
SON functionalities are modeled as linear Ordinary Differential Equation
(ODE)s. The stability of the system is first evaluated using a basic control
theory approach. The coordination solution consists in finding a linear map
(called coordination matrix) that stabilizes the system of SON functionalities.
It is proven that the solution remains valid in a noisy environment using
Stochastic Approximation. A practical example involving three different SON
functionalities deployed in Base Stations (BSs) of a Long Term Evolution (LTE)
network demonstrates the usefulness of the proposed method.Comment: submitted to IEEE TCNS. arXiv admin note: substantial text overlap
with arXiv:1209.123
Self Organizing strategies for enhanced ICIC (eICIC)
Small cells have been identified as an effective solution for coping with the
important traffic increase that is expected in the coming years. But this
solution is accompanied by additional interference that needs to be mitigated.
The enhanced Inter Cell Interference Coordination (eICIC) feature has been
introduced to address the interference problem. eICIC involves two parameters
which need to be optimized, namely the Cell Range Extension (CRE) of the small
cells and the ABS ratio (ABSr) which defines a mute ratio for the macro cell to
reduce the interference it produces. In this paper we propose self-optimizing
algorithms for the eICIC. The CRE is adjusted by means of load balancing
algorithm. The ABSr parameter is optimized by maximizing a proportional fair
utility of user throughputs. The convergence of the algorithms is proven using
stochastic approximation theorems. Numerical simulations illustrate the
important performance gain brought about by the different algorithms.Comment: Submitted to WiOpt 201
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
Self-optimization of pilot power in enterprise femtocells using multi objective heuristic
Deployment of a large number of femtocells to jointly provide coverage in an enterprise environment raises critical challenges especially in future self-organizing networks which rely on plug-and-play techniques for configuration. This paper proposes a multi-objective heuristic based on a genetic algorithm for a centralized self-optimizing network containing a group of UMTS femtocells. In order to optimize the network coverage in terms of handled load, coverage gaps, and overlaps, the algorithm provides a dynamic update of the downlink pilot powers of the deployed femtocells. The results demonstrate that the algorithm can effectively optimize the coverage based on the current statistics of the global traffic distribution and the levels of interference between neighboring femtocells. The algorithm was also compared with the fixed pilot power scheme. The results show over fifty percent reduction in pilot power pollution and a significant enhancement in network performance. Finally, for a given traffic distribution, the solution quality and the efficiency of the described algorithm were evaluated by comparing the results generated by an exhaustive search with the same pilot power configuration
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
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