Operational Fault Detection in cellular wireless base-stations

Rao, Sudarshan A.

doi:10.1109/tnsm.2006.4798311

Cited by 16 publications

(4 citation statements)

References 9 publications

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“…It is worthy to note that the proposed algorithms can be applied to both the IEEE 16j and 3GPP LTE‐A that work at < 6 GHz . Careful frequency planning is important to minimum communication outage probability, neighboring cell interference, and maximum network throughput . However, this paper focused on the coverage problem rather than throughput maximum.…”

Section: Simulation Resultsmentioning

confidence: 99%

Network planning for mobile multi-hop relay networks

Chen

Tseng

Lai

et al. 2013

Wirel. Commun. Mob. Comput.

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In this paper, the coverage problem of network planning in mobile multi-hop relay networks is defined on the basis of integer linear programming. In order to provide desired utilities and also meet deployment limitations for network planning, we propose a supergraph tree algorithm to place base stations and relay stations at the lowest cost position. Furthermore, another algorithm for avoiding the interference between base stations, which is called interference aware tree algorithm is also proposed. Both the proposed algorithms are formulated on the basis of a graph theoretic technique and analyzed in the simulation results. The results show that the supergraph tree algorithm provides the lowest construction cost with different network scenarios, and the interference aware tree algorithm provides the highest communication quality for mobile multi-hop relay infrastructure-based communication network planning.Network planning for MMR networks planning and compare the different approaches with regard to this issue. Section 3 defines the integer linear programming (ILP) for the MMR coverage problem and explains the proposed algorithms. In Section 4, we illustrate a planning case and discuss the simulation results. Finally, the conclusions about this research are given in Section 5.

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Section: Simulation Resultsmentioning

confidence: 99%

Network planning for mobile multi-hop relay networks

Chen

Tseng

Lai

et al. 2013

Wirel. Commun. Mob. Comput.

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“…Unlike fault prediction, fault detection is reactive and identifies and, or classifies a failure after it has occurred, using network symptoms, performance degradation, and other parameters. Rao [382] propose fault detection for cellular networks that can detect faults at different levels, base station, sector, carrier, and channel. They employ a statistical hypothesis testing framework which combines parametric, semi-parametric, and non-parametric test statistics to model expected behavior.…”

Section: Detecting Faultmentioning

confidence: 99%

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Boutaba

Salahuddin

Limam

et al. 2018

J Internet Serv Appl

791

391

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Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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“…However, much of the work focuses on detection of failures [3,6,8,9], and identification of root cause of the failure [1,10]. Their goal is different from ours, which is to predict failures and proactively try to prevent them, or lessen the effects of failures on user experience in the short term.…”

Section: Related Workmentioning

confidence: 99%

Using big data for more dependability

Theera-Ampornpunt

Bagchi

Joshi

et al. 2013

Proceedings of the 9th Workshop on Hot Topics in Dependable Systems

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There are many large infrastructures that instrument everything from network performance metrics to user activities. However, the collected data are generally used for long-term planning instead of improving reliability and user experience in real time. In this paper, we present our vision of how such collections of data can be used in real time to enhance the dependability of cellular network services. We first discuss mitigation mechanisms that can be used to improve reliability, but incur a high cost which prohibit them to be used except in certain conditions. We present two case studies where analyses of real cellular network traffic data show that we can identify these conditions.

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Operational Fault Detection in cellular wireless base-stations

Cited by 16 publications

References 9 publications

Network planning for mobile multi-hop relay networks

Network planning for mobile multi-hop relay networks

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Using big data for more dependability

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