Artificial Neural Networks for Traffic Prediction in 4G Networks

Loumiotis, Ioannis; Adamopoulou, Evgenia; Demestichas, Konstantinos; Kosmides, Pavlos; Theologou, M.

doi:10.1007/978-3-319-18802-7_20

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…In this type of networks, dynamic resource allocations to BSs are needed for better robustness and efficiency and the neural network-based schemes have been investigated. In References [106,107], an intelligent agent has been deployed at the BS for monitoring and collecting the data from the environment. Then the future bandwidth requirements have been predicted to request resources in the access network.…”

Section: Supervised Learning In Rof Network Management and Resource Allocationmentioning

confidence: 99%

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

Lee

Kandeepan

et al. 2020

Photonics

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The radio-over-fiber (RoF) technology has been widely studied during the past decades to extend the wireless communication coverage by leveraging the low-loss and broad bandwidth advantages of the optical fiber. With the increasing need for wireless communications, using millimeter-waves (mm-wave) in wireless communications has become the recent trend and many attempts have been made to build high-throughput and robust mm-wave RoF systems during the past a few years. Whilst the RoF technology provides many benefits, it suffers from several fundamental limitations due to the analog optical link, including the fiber chromatic dispersion and nonlinear impairments. Various approaches have been proposed to address these limitations. In particular, machine learning (ML) algorithms have attracted intensive research attention as a promising candidate for handling the complicated physical layer impairments in RoF systems, especially the nonlinearity during signal modulation, transmission and detection. In this paper, we review recent advancements in ML techniques for RoF systems, especially those which utilize ML models as physical layer signal processors to mitigate various types of impairments and to improve the system performance. In addition, ML algorithms have also been widely adopted for highly efficient RoF network management and resource allocation, such as the dynamic bandwidth allocation and network fault detection. In this paper, we also review the recent works in these research domains. Finally, several key open questions that need to be addressed in the future and possible solutions of these questions are also discussed.

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Section: Supervised Learning In Rof Network Management and Resource Allocationmentioning

confidence: 99%

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

Lee

Kandeepan

et al. 2020

Photonics

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“…In (Loumiotis et al, 2014), the efficient management of the backhaul resources in 4G networks is examined. The authors raise this issue in the case that the backhaul network has been leased by the mobile operator.…”

Section: Related Workmentioning

confidence: 99%

Hidden Markov Model Traffic Characterisation in Wireless Networks

Spyrou

Mitrakos

2016

Proceedings of the Fifth International Conference on Telecommunications and Remote Sensing

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Quality of service wireless traffic that often exhibits burstiness, occasionally occurring due to mobility, provides a critical networking issue. Traffic patterns in wireless networks are not of a traditional nature. Nodes transmit their information in batches over a short period of time, before they lose connection. Prediction of wireless incoming load plays an important role in the design of wireless local area networks. The issues of load balancing and Quality of Service constraints are a major problem, which is responsible for the increase of throughput of the network; thus, predicting traffic can be of a great assistance in the aforementioned research directions, leading to a significant optimisation of the wireless network operation. This paper addresses the problem of traffic prediction using Hidden Markov Models. The data is clustered using the Information Based Similarity index that classifies different types of traffic. We show the limitation of this approach and we finally select Euclidean distance for data clustering. Together, they provide an efficient solution towards the solution of wireless traffic characterisation and prediction. We show the efficiency of our scheme in a series of simulations 78

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“…These models are non-linear and can potentially capture any non-linearity in the data. The authors in [9] applied a general multilayer perceptron (MLP) network to predict the base station traffic under different wireless network setups. Recurrent neural networks (RNN) which are specific to sequence data are used in [5], [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

Network Traffic Modeling and Prediction Using Graph Gaussian Processes

Mehrizi

Chatzinotas

2022

IEEE Access

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Traffic modeling and prediction is a vital task for designing efficient resource allocation strategies in telecommunication networks. This is challenging because network traffic data exhibits complex nonlinear spatiotemporal interactions. Moreover, the data can have missing values when traffic statistic collection is unavailable in certain nodes. In this paper, we introduce a graph Gaussian Process (GP) model for this challenging problem. The GP is a Bayesian non-parametric model and highly flexible in capturing complex patterns in the data. Additionally, it provides uncertainty information which can be exploited for robust resource allocation problems. The developed graph GP model is almost free of hyper-parameter tuning, can accurately capture short-term and long-term temporal patterns and can infer missing values by learning spatiotemporal interactions among the nodes in the network. Subsequently, we approximate the intractable posterior distribution using Variational Bayes (VB) algorithm which can be efficiently implemented. Finally, we evaluate the accuracy of the proposed model for predicting the data traffic using two real-world network datasets. Our simulation results shows that the proposed model can achieve better prediction accuracy with respect to the state-of-the-art approaches.

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Artificial Neural Networks for Traffic Prediction in 4G Networks

Cited by 8 publications

References 6 publications

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

Hidden Markov Model Traffic Characterisation in Wireless Networks

Network Traffic Modeling and Prediction Using Graph Gaussian Processes

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