Deep Learning Based Link Failure Mitigation

Khunteta, Shubham; Chavva, Ashok Kumar Reddy

doi:10.1109/icmla.2017.00-58

Cited by 29 publications

(24 citation statements)

References 5 publications

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“…In this systematic review, we located two papers that addressed handover prediction. In [51], Khunteta et al proposed a deep learning model to avoid handover failures. For that, the deep learning model was trained to detect if the handover will fail or be successful based on the historical signal condition data.…”

Section: Handover Predictionmentioning

confidence: 99%

“…In [35,36,51,59], the authors used LSTM to deal with sequential data generated through simulation. In [59], the LSTM model was used to predict if a new network slice can be allocated given the sequential data of allocated resources and channel conditions.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…In [59], the LSTM model was used to predict if a new network slice can be allocated given the sequential data of allocated resources and channel conditions. In [51], the LSTM model was used to evaluate historical signal condition in order to classify event in either handover fail or success in advance. In [36], the developed LSTM model was applied to learn the users mobility pattern in order to predict their movement trends in the future based on historical trajectories.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

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When 5G Meets Deep Learning: A Systematic Review

et al. 2020

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This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.

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Section: Handover Predictionmentioning

confidence: 99%

Section: Recurrent Neural Networkmentioning

confidence: 99%

Section: Recurrent Neural Networkmentioning

confidence: 99%

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When 5G Meets Deep Learning: A Systematic Review

et al. 2020

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“…Machine Learning has been vastly used to improve the networks' performance, especially in the last few years [38]- [41]. The authors of [38] propose a method based on Deep Neural Networks to mitigate the link failure caused by unsuccessful handovers and congested cells, among other reasons.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach for Handover in LTE Networks with Signal Obstructions

Guerra¹,

Dantas²,

Sousa³

2020

JCIS

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“…Por esses motivos, o procedimento de handoveré pressionado a evoluir e apresentar alternativas eficientes que sejam inteligentes e versáteis, evitando handovers ping-pong, reduzindo a sinalização de rede, oferecendo maior eficiência espectral com baixa latência e conexões sem emendas.Recentemente, vários esforços foram produzidos no sentido de implementar algoritmos de inteligência computacional para otimizar diversos parâmetros e desafios futuros para os sistemas de comunicação sem fio, como discutido em[5]. A mitigação de falhas de links estabelecidos oriundos de más decisões de handoveré discutida em[6]. No contexto do uso de lógica fuzzy,[7] e[8] oferecem novas estratégias de otimização para um método tradicional de handover.…”

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Sistemas Fuzzy aplicados ao Handover em Redes LTE com Falhas de Cobertura

Lima¹,

Medeiros²,

Aguiar³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Nas futuras gerações de comunicações móveis, há uma tendência de aumento significativo de requisições de handover, a qual torna essencial a formulação de melhores estratégias para esse processo a fim de fazer uso mais eficiente do espectro e maximizar a QoE. Nesse sentido, esse trabalho apresenta uma comparação concisa entre diferentes técnicas baseadas em sistemas fuzzy, a fim de otimizar a escolha do alvo para handover, em cenários com mobilidade de usuários, obstruções e falhas de coberturas. Os resultados apontam alta precisão para classificação de melhor alvo, mesmo em situações adversas.

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Deep Learning Based Link Failure Mitigation

Cited by 29 publications

References 5 publications

When 5G Meets Deep Learning: A Systematic Review

When 5G Meets Deep Learning: A Systematic Review

A Machine Learning Approach for Handover in LTE Networks with Signal Obstructions

Sistemas Fuzzy aplicados ao Handover em Redes LTE com Falhas de Cobertura

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