4 research outputs found
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
Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks
Mobile cellular network operators spend nearly a quarter of their revenue on
network maintenance and management. A significant portion of that budget is
spent on resolving faults diagnosed in the system that disrupt or degrade
cellular services. Historically, the operations to detect, diagnose and resolve
issues were carried out by human experts. However, with diversifying cell
types, increased complexity and growing cell density, this methodology is
becoming less viable, both technically and financially. To cope with this
problem, in recent years, research on self-healing solutions has gained
significant momentum. One of the most desirable features of the self-healing
paradigm is automated fault diagnosis. While several fault detection and
diagnosis machine learning models have been proposed recently, these schemes
have one common tenancy of relying on human expert contribution for fault
diagnosis and prediction in one way or another. In this paper, we propose an
AI-based fault diagnosis solution that offers a key step towards a completely
automated self-healing system without requiring human expert input. The
proposed solution leverages Random Forests classifier, Convolutional Neural
Network and neuromorphic based deep learning model which uses RSRP map images
of faults generated. We compare the performance of the proposed solution
against state-of-the-art solution in literature that mostly use Naive Bayes
models, while considering seven different fault types. Results show that
neuromorphic computing model achieves high classification accuracy as compared
to the other models even with relatively small training dat
AI BASED FAULT DIAGNOSIS IN EMERGING CELLULAR NETWORKS
Mobile cellular network operators spend nearly a quarter of their revenue on network management and maintenance. A significant portion of that budget, is spent on resolving faults diagnosed in the system that degrade or disrupt cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with growing cell density, diversifying cell types and increased complexity, this approach is becoming less and less viable, both technically and financially. To cope with this problem, research on self-healing solutions has gained significant momentum in recent years. One of the most desirable features of the selfhealing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy. They still rely on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step forward towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages neuromorphic computing which uses RSRP map images of faults generated. We compare the performance of theproposedsolutionagainststateoftheartsolutioninliterature that mostly use Naive Bayes models, while considering seven different fault types. Results show that the neuromorphic model achieves high classification accuracy as compared to Random Forests classifier, Convolutional Neural Networks and Naive Bayes even with relatively small training data
Probabilistic Modeling of Process Systems with Application to Risk Assessment and Fault Detection
Three new methods of joint probability estimation (modeling), a maximum-likelihood maximum-entropy method, a constrained maximum-entropy method, and a copula-based method called the rolling pin (RP) method, were developed. Compared to many existing probabilistic modeling methods such as Bayesian networks and copulas, the developed methods yield models that have better performance in terms of flexibility, interpretability and computational tractability. These methods can be used readily to model process systems and perform risk analysis and fault detection at steady state conditions, and can be coupled with appropriate mathematical tools to develop dynamic probabilistic models. Also, a method of performing probabilistic inference using RP-estimated joint probability distributions was introduced; this method is superior to Bayesian networks in several aspects. The RP method was also applied successfully to identify regression models that have high level of flexibility and are appealing in terms of computational costs.Ph.D., Chemical Engineering -- Drexel University, 201