Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

Azari, Amin; Salehi, Fateme; Papapetrou, Panagiotis; Çavdar, Çiçek

doi:10.1109/tgcn.2021.3126286

Cited by 14 publications

(5 citation statements)

References 42 publications

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“…Table 1 provides a summary of these papers in terms of the tasks they address, the applications that generate traffic, the models used, and the metrics used to evaluate these models. Most of the papers focus on traffic forecasting, but the tasks of capacity planning [27] and resource optimization [28] are also addressed. The data are collected from several sources, including cells [29][30][31][32], devices [33], switches [34], and servers [35].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models

Kochetkova,

Kushchazli,

Burtseva

et al. 2023

Future Internet

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Fifth-generation (5G) networks require efficient radio resource management (RRM) which should dynamically adapt to the current network load and user needs. Monitoring and forecasting network performance requirements and metrics helps with this task. One of the parameters that highly influences radio resource management is the profile of user traffic generated by various 5G applications. Forecasting such mobile network profiles helps with numerous RRM tasks such as network slicing and load balancing. In this paper, we analyze a dataset from a mobile network operator in Portugal that contains information about volumes of traffic in download and upload directions in one-hour time slots. We apply two statistical models for forecasting download and upload traffic profiles, namely, seasonal autoregressive integrated moving average (SARIMA) and Holt-Winters models. We demonstrate that both models are suitable for forecasting mobile network traffic. Nevertheless, the SARIMA model is more appropriate for download traffic (e.g., MAPE [mean absolute percentage error] of 11.2% vs. 15% for Holt-Winters), while the Holt-Winters model is better suited for upload traffic (e.g., MAPE of 4.17% vs. 9.9% for SARIMA and Holt-Winters, respectively).

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Section: Related Workmentioning

confidence: 99%

Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models

Kochetkova,

Kushchazli,

Burtseva

et al. 2023

Future Internet

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“…Reinforcement learning methods also have been used to lower the energy consumption of base stations [62], whereas an evaluation of cellular traffic and communication parameters based on machine learning tools was addressed in Ref. [63]. The power consumption was considered as an optimization problem in Ref.…”

Section: Introductionmentioning

confidence: 99%

Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators

Neves

Sanches

Nobrega

et al. 2023

Sensors

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High data rate coverage, security, and energy efficiency will play a key role in the continued performance scaling of next-generation mobile systems. Dense, small mobile cells based on a novel network architecture are part of the answer. Motivated by the recent mounting interest in free-space optical (FSO) technologies, this paper addresses a novel mobile fronthaul network architecture based on FSO, spread spectrum codes, and graphene modulators for the creation of dense small cells. The network uses an energy-efficient graphene modulator to send data bits to be coded with spread codes for achieving higher security before their transmission to remote units via high-speed FSO transmitters. Analytical results show the new fronthaul mobile network can accommodate up to 32 remote antennas under error-free transmissions with forward error correction. Furthermore, the modulator is optimized to provide maximum efficiency in terms of energy consumption per bit. The optimization procedure is carried out by optimizing both the amount of graphene used on the ring resonator and the modulator’s design. The optimized graphene modulator is used in the new fronthaul network and requires as low as 4.6 fJ/bit while enabling high-speed performance up to 42.6 GHz and remarkably using one-quarter of graphene only.

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“…The authors of [2] use traffic classification to estimate the amount of traffic generated by the most popular applications. Other studies focus on identifying potential risks to users' privacy [3], and energy saving [4].…”

Section: Introductionmentioning

confidence: 99%

Mobile traffic classification through burst traffic statistical features

Anamuro,

Lagrange

2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

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Mobile traffic classification is a topic of interest for researchers focused on improving the network capacity or for those seeking to identify potential risks to users' privacy. In recent years, traffic classification accuracy has significantly improved thanks to machine learning techniques. These techniques allow traffic identification even if it is encrypted, as in mobile networks. In this paper, we show that it is feasible to classify mobile traffic applications with high accuracy using downlink control information (DCI) messages and machine learning. The DCI messages are collected using a sniffer located near the base station. Then we extract the statistical features of the bursts and inter-burst periods of the traffic generated by mobile applications at the physical layer. This strategy uses few features and does not require a big dataset. We have tested our approach on a 4G cellular network testbed and a commercial 4G cellular network. The results show an accuracy greater than 92% and 95% for application and category classification, respectively.

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Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

Cited by 14 publications

References 42 publications

Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models

Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models

Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators

Mobile traffic classification through burst traffic statistical features

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