739 research outputs found
A cell outage management framework for dense heterogeneous networks
In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner
Fixed Rank Kriging for Cellular Coverage Analysis
Coverage planning and optimization is one of the most crucial tasks for a
radio network operator. Efficient coverage optimization requires accurate
coverage estimation. This estimation relies on geo-located field measurements
which are gathered today during highly expensive drive tests (DT); and will be
reported in the near future by users' mobile devices thanks to the 3GPP
Minimizing Drive Tests (MDT) feature~\cite{3GPPproposal}. This feature consists
in an automatic reporting of the radio measurements associated with the
geographic location of the user's mobile device. Such a solution is still
costly in terms of battery consumption and signaling overhead. Therefore,
predicting the coverage on a location where no measurements are available
remains a key and challenging task. This paper describes a powerful tool that
gives an accurate coverage prediction on the whole area of interest: it builds
a coverage map by spatially interpolating geo-located measurements using the
Kriging technique. The paper focuses on the reduction of the computational
complexity of the Kriging algorithm by applying Fixed Rank Kriging (FRK). The
performance evaluation of the FRK algorithm both on simulated measurements and
real field measurements shows a good trade-off between prediction efficiency
and computational complexity. In order to go a step further towards the
operational application of the proposed algorithm, a multicellular use-case is
studied. Simulation results show a good performance in terms of coverage
prediction and detection of the best serving cell
LTE-advanced self-organizing network conflicts and coordination algorithms
Self-organizing network (SON) functions have been introduced in the LTE and LTEAdvanced standards by the Third Generation Partnership Project as an excellent solution that promises enormous improvements in network performance. However, the most challenging issue in implementing SON functions in reality is the identification of the best possible interactions among simultaneously operating and even conflicting SON functions in order to guarantee robust, stable, and desired network operation. In this direction, the first step is the comprehensive modeling of various types of conflicts among SON functions, not only to acquire a detailed view of the problem, but also to pave the way for designing appropriate Self-Coordination mechanisms among SON functions. In this article we present a comprehensive classification of SON function conflicts, which leads the way for designing suitable conflict resolution solutions among SON functions and implementing SON in reality. Identifying conflicting and interfering relations among autonomous network management functionalities is a tremendously complex task. We demonstrate how analysis of fundamental trade-offs among performance metrics can us to the identification of potential conflicts. Moreover, we present analytical models of these conflicts using reference signal received power plots in multi-cell environments, which help to dig into the complex relations among SON functions. We identify potential chain reactions among SON function conflicts that can affect the concurrent operation of multiple SON functions in reality. Finally, we propose a selfcoordination framework for conflict resolution among multiple SON functions in LTE/LTEAdvanced networks, while highlighting a number of future research challenges for conflict-free operation of SON
A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters
Current LTE network is faced with a plethora of Configuration and
Optimization Parameters (COPs), both hard and soft, that are adjusted manually
to manage the network and provide better Quality of Experience (QoE). With 5G
in view, the number of these COPs are expected to reach 2000 per site, making
their manual tuning for finding the optimal combination of these parameters, an
impossible fleet. Alongside these thousands of COPs is the anticipated network
densification in emerging networks which exacerbates the burden of the network
operators in managing and optimizing the network. Hence, we propose a machine
learning-based framework combined with a heuristic technique to discover the
optimal combination of two pertinent COPs used in mobility, Cell Individual
Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key
Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio
(SINR) of all the connected users. The first part of the framework leverages
the power of machine learning to predict the KPI of interest given several
different combinations of CIO and HOM. The resulting predictions are then fed
into Genetic Algorithm (GA) which searches for the best combination of the two
mentioned parameters that yield the maximum mean SINR for all users.
Performance of the framework is also evaluated using several machine learning
techniques, with CatBoost algorithm yielding the best prediction performance.
Meanwhile, GA is able to reveal the optimal parameter setting combination more
efficiently and with three orders of magnitude faster convergence time in
comparison to brute force approach
Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication
While the automotive industry is currently facing a contest among different
communication technologies and paradigms about predominance in the connected
vehicles sector, the diversity of the various application requirements makes it
unlikely that a single technology will be able to fulfill all given demands.
Instead, the joint usage of multiple communication technologies seems to be a
promising candidate that allows benefiting from characteristical strengths
(e.g., using low latency direct communication for safety-related messaging).
Consequently, dynamic network interface selection has become a field of
scientific interest. In this paper, we present a cross-layer approach for
context-aware transmission of vehicular sensor data that exploits mobility
control knowledge for scheduling the transmission time with respect to the
anticipated channel conditions for the corresponding communication technology.
The proposed multi-interface transmission scheme is evaluated in a
comprehensive simulation study, where it is able to achieve significant
improvements in data rate and reliability
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