LSTM-based throughput prediction for LTE networks

Na, Hyeonjun; Shin, Yongjoo; Lee, Dongwon; Lee, Joohyun

doi:10.1016/j.icte.2021.12.001

Cited by 17 publications

(9 citation statements)

References 19 publications

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“…As one may be expected, in [6] and [8] LSTM also got some praise. However, as different people are using different datasets, and the results between different algorithms are quite matched in strength, it is hard to say there is a best algorithm or a game-changer in this topic.…”

Section: Related Workmentioning

confidence: 74%

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A Conditional-Probability-Distribution Model for Bandwidth Estimation with Application in Live Video Streaming

Zheng¹

2022

Preprint

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Experience of live video streaming can be improved if the video uploader have more accurate knowledge about the future available bandwidth. Because with such knowledge, one is able to know what sizes should he encode the frames to be in an ever-changing network. Researchers have developed some algorithms to predict throughputs in the literature, from where some simple hence practical ones. However, limitation remains as most current bandwidth prediction methods are predicting a value, or a point estimate, of future bandwidth. Because in many practical scenarios, it is desirable to control the performance to some targets, e.g., video delivery rate over a given target percentage, which cannot be easily achieved via most current methods.In this work, we propose the use of probability distribution to model future bandwidth. Specifically, we model future bandwidth using past data transfer measurements, and then derive a probability model for use in the application. This changes the selection of parameters in application into a probabilistic manner such that given target performance can be achieved in the long run. Inside our model, we use the conditional-probability method to correlate past and future bandwidth and hence further improve the estimating performance.

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Section: Related Workmentioning

confidence: 74%

“…Besides sophisticated methods explored in [6], [7] and [8], two seemly-trivial throughput predicting algorithms got suggested in [9]. They are arithmetic mean (AM) and multiple linear regression (MLR).…”

Section: Related Workmentioning

confidence: 99%

A Conditional-Probability-Distribution Model for Bandwidth Estimation with Application in Live Video Streaming

Zheng¹

2022

Preprint

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“…One of the most practicable known solutions to the RNN gradient problem are the LSTMs (Wong et al 2023), being able to deal with long term dependencies, hosting three state cells or logic gates that control the flow of information called input gate, forgetting gate and output gate (Brauns et al 2022). This method has a cell state that, when added to the RNN algorithm thanks to the new gates added, solves the gradient problem, passing the state to the next layer and keeping the existing state (Na et al 2023). Due to this method, it can be trained for much longer, making it especially useful for predicting time series and for capturing dependency data from the beginning of the data, if it is relevant.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Forecasting Time Series Covid 19 Using Deep Learning Methods: South America Comparative Case Study

Alfaro,

Vargas,

Fuertes

et al. 2023

Preprint

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The global pandemic of COVID-19 that took place in 2020 and 2021 posed different challenges for health systems worldwide, revealing various deficiencies and generating supply problems and a breakdown in medical services. Given this situation, it is crucial to have predictive methodologies that can accurately estimate the behavior of diseases of this type. This would allow countries to be better prepared in the future and respond effectively to future similar situations, avoiding a repetition of large-scale events. In the literature, deep learning techniques, in particular, have shown promise in this field. In this paper, a comparative study is performed between individual Deep Learning models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), along with hybrid models that combine Conv1D with LSTM or GRU. The objective is to predict contagion curves in Latin American countries, specifically in Argentina, Brazil, Chile, Colombia, and Peru. These countries present a dwindling number of studies in the existing literature, which makes this work especially relevant. The results emphasize the competitiveness of the hybrid models, which show MAPE values ranging from 0.1–1%. In contrast, the individual models present slightly higher MAPE, in the range of 0.2–0.8%. These results demonstrate the effectiveness of the proposed Deep Learning models in predicting the spread of COVID-19 in South America.

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“…The selection of neural network structures which we employ in our proposed solutions was made on the basis of literature studies. Following a number of researches (e.g., [ 50 , 86 , 87 , 88 ]) related to the use of deep learning in wireless networks, we decided to utilize network structures employing both Long Short-Term Memory (LSTM) [ 89 ] and Dense neurons layers [ 90 ].…”

Section: Proposed Wireless Link Selection Based On Deep Learningmentioning

confidence: 99%

Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach

Hoeft

Gierłowski

Woźniak

2022

Sensors

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In recent years, we have been witnessing a growing interest in the subject of communication at sea. One of the promising solutions to enable widespread access to data transmission capabilities in coastal waters is the possibility of employing an on-shore wireless access infrastructure. However, such an infrastructure is a heterogeneous one, managed by many independent operators and utilizing a number of different communication technologies. If a moving sea vessel is to maintain a reliable communication within such a system, it needs to employ a set of network mechanisms dedicated for this purpose. In this paper, we provide a short overview of such requirements and overall characteristics of maritime communication, but our main focus is on the link selection procedure—an element of critical importance for the process of changing the device/system which the mobile vessel uses to retain communication with on-shore networks. The paper presents the concept of employing deep neural networks for the purpose of link selection. The proposed methods have been verified using propagation models dedicated to realistically represent the environment of maritime communications and compared to a number of currently popular solutions. The results of evaluation indicate a significant gain in both accuracy of predictions and reduction of the amount of test traffic which needs to be generated for measurements.

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LSTM-based throughput prediction for LTE networks

Cited by 17 publications

References 19 publications

A Conditional-Probability-Distribution Model for Bandwidth Estimation with Application in Live Video Streaming

A Conditional-Probability-Distribution Model for Bandwidth Estimation with Application in Live Video Streaming

Forecasting Time Series Covid 19 Using Deep Learning Methods: South America Comparative Case Study

Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach

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