5G and beyond networks

Ruíz, Silvia; Ahmadi, Hamed; Gardasevic, Gordana; Haddad, Yoram; Katzis, Konstantinos; Grazioso, P.; Petrini, Valeria; Reichman, Arie; Özdemir, Mehmet Kemal; Velez, Fernando J.; Paulo, Rui R.; Fortes, Sergio; Correia, Luıs M.; Rouzbehani, Behnam; Barahman, Mojgan; Deruyck, Margot; Mignardi, Silvia; Nasr, Karim M.; Zhang, Haibin

doi:10.1016/b978-0-12-820581-5.00012-2

Cited by 8 publications

(2 citation statements)

References 49 publications

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“…S promised by the fifth-generation (5G) of radio communication networks, future wireless mobile systems will be inclusive, i.e., technologies for supporting wireless connectivity for any rates, type of communicating units, and scenarios [1]. To meet this ambitious aim, reliable path loss modeling for millimeter-wave (mmWave) frequencies is essential to provide accurate predictions for the deployment of 5G wireless service.…”

Section: Introductionmentioning

confidence: 99%

Towards Practical Path Loss Predictions in Indoor Corridors Considering 5G mmWave Three-Dimensional Measurements

Diago-Mosquera

Aragón‐Zavala

Rodríguez

2022

Antennas Wirel. Propag. Lett.

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Theoretical and empirical foundations of how radio waves behave in practical wireless channels need to be fully revisited for millimeter-wave (mmWave) frequencies so that fifthgeneration (5G) technologies may be successful. Kriging is an outstanding geostatistal interpolation technique that employs variography to understand the spatial variability of known samples at specific locations to predict unmeasured samples, based on the fact that there is an implied connection between the measured value and its location in space. The research we here report is aimed at validating the improvement in predictions when this tool is included for mmWave frequency path loss modeling in a long indoor corridor with break. In order to quantify the accuracy of the proposed methodology, it is compared with a wellestablished procedure described in the literature. Extensive pathloss measurements were collected through specialized narrowband sounders at 28 and 60 GHz. Spatially averaged power measurements using omnidirectional and directive antennas at different heights provided the empirical basis for the threedimensional (3D) Kriging-aided model. It was found that this method significantly improves the accuracy as it considers all the singularities and site-associated features that are implicit in measured samples. This is important to obtain a reliable path loss model for planning and deployment of mmWave wireless communication systems in indoor scenarios.

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Section: Introductionmentioning

confidence: 99%

Towards Practical Path Loss Predictions in Indoor Corridors Considering 5G mmWave Three-Dimensional Measurements

Diago-Mosquera

Aragón‐Zavala

Rodríguez

2022

Antennas Wirel. Propag. Lett.

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“…Fifth-generation (5G) and 6G networks [1,2] are expected to support a wide range of new technologies, such as drones and virtual/augmented reality, which require high bit rates, lower latency, and increased throughput [3][4][5]. The number of connected devices is increasing, resulting in a dramatic growth in traffic volume, causing anomalies such as network congestion, decreased quality of service, network delays, data loss, and blocking of new connections [6]. The network architecture should adapt to the volumes of traffic generated by various applications and use it for decision-making, taking into account several types of traffic with different service and priority requirements [7][8][9].…”

Section: Introductionmentioning

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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Overcoming Real-World IoT Deployment Challenges with Enhanced Fuzzy Logic Decision Algorithms

Bannoura,

Chekenbah,

Meyer

et al. 2024

J Netw Syst Manage

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5G and beyond networks

Cited by 8 publications

References 49 publications

Towards Practical Path Loss Predictions in Indoor Corridors Considering 5G mmWave Three-Dimensional Measurements

Towards Practical Path Loss Predictions in Indoor Corridors Considering 5G mmWave Three-Dimensional Measurements

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

Overcoming Real-World IoT Deployment Challenges with Enhanced Fuzzy Logic Decision Algorithms

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