4,860 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
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
Statistical Multiplexing and Traffic Shaping Games for Network Slicing
Next generation wireless architectures are expected to enable slices of
shared wireless infrastructure which are customized to specific mobile
operators/services. Given infrastructure costs and the stochastic nature of
mobile services' spatial loads, it is highly desirable to achieve efficient
statistical multiplexing amongst such slices. We study a simple dynamic
resource sharing policy which allocates a 'share' of a pool of (distributed)
resources to each slice-Share Constrained Proportionally Fair (SCPF). We give a
characterization of SCPF's performance gains over static slicing and general
processor sharing. We show that higher gains are obtained when a slice's
spatial load is more 'imbalanced' than, and/or 'orthogonal' to, the aggregate
network load, and that the overall gain across slices is positive. We then
address the associated dimensioning problem. Under SCPF, traditional network
dimensioning translates to a coupled share dimensioning problem, which
characterizes the existence of a feasible share allocation given slices'
expected loads and performance requirements. We provide a solution to robust
share dimensioning for SCPF-based network slicing. Slices may wish to
unilaterally manage their users' performance via admission control which
maximizes their carried loads subject to performance requirements. We show this
can be modeled as a 'traffic shaping' game with an achievable Nash equilibrium.
Under high loads, the equilibrium is explicitly characterized, as are the gains
in the carried load under SCPF vs. static slicing. Detailed simulations of a
wireless infrastructure supporting multiple slices with heterogeneous mobile
loads show the fidelity of our models and range of validity of our high load
equilibrium analysis
A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks
Cell Switch-Off (CSO) is recognized as a promising approach to reduce the
energy consumption in next-generation cellular networks. However, CSO poses
serious challenges not only from the resource allocation perspective but also
from the implementation point of view. Indeed, CSO represents a difficult
optimization problem due to its NP-complete nature. Moreover, there are a
number of important practical limitations in the implementation of CSO schemes,
such as the need for minimizing the real-time complexity and the number of
on-off/off-on transitions and CSO-induced handovers. This article introduces a
novel approach to CSO based on multiobjective optimization that makes use of
the statistical description of the service demand (known by operators). In
addition, downlink and uplink coverage criteria are included and a comparative
analysis between different models to characterize intercell interference is
also presented to shed light on their impact on CSO. The framework
distinguishes itself from other proposals in two ways: 1) The number of
on-off/off-on transitions as well as handovers are minimized, and 2) the
computationally-heavy part of the algorithm is executed offline, which makes
its implementation feasible. The results show that the proposed scheme achieves
substantial energy savings in small cell deployments where service demand is
not uniformly distributed, without compromising the Quality-of-Service (QoS) or
requiring heavy real-time processing
Context-Aware Handover Policies in HetNets
Next generation cellular systems are expected to entail a wide variety of wireless coverage zones, with cells of different sizes and capacities that can overlap in space and share the transmission resources. In this scenario, which is referred to as Heterogeneous Networks (HetNets), a fundamental challenge is the management of the handover process between macro, femto and pico cells. To limit the number of handovers and the signaling between the cells, it will hence be crucial to manage the user's mobility considering the context parameters, such as cells size, traffic loads, and user velocity. In this paper, we propose a theoretical model to characterize the performance of a mobile user in a HetNet scenario as a function of the user's mobility, the power profile of the neighboring cells, the handover parameters, and the traffic load of the different cells. We propose a Markov-based framework to model the handover process for the mobile user, and derive an optimal context-dependent handover criterion. The mathematical model is validated by means of simulations, comparing the performance of our strategy with conventional handover optimization techniques in different scenarios. Finally, we show the impact of the handover regulation on the users performance and how it is possible to improve the users capacity exploiting context information
- …