Due to the rapid technology evolution and standardization activity in the mobile communication networks, there is the need for the research community to be able to develop, test and evaluate new ...and/or already existing solutions before industrial or real-network implementation. As such, it is essential to have an open-source tool that provides an alternative solution to that of industrial proprietary simulators that are not available for public usage. ns-3 5G-LENA simulator is an end-to-end open-source NR system-level simulator that allows extensive research to be performed. However, it is of great importance to guarantee that the results obtained using the simulator can be comparable to that of industrial simulators and real networks. For this reason, calibrating the simulator based on 3GPP defined specifications is crucial. Based on the above, in this paper we calibrate the ns-3 5G-LENA simulator according to the 3GPP reference results for NR-based outdoor deployments. Moreover, we explore the REM feature provided by the simulator, to ease the calibration process and understand better the radio environment. Results show the resemblance of the simulator performance to that of simulators used as references by 3GPP.
5G networks are designed to support a variety of services with highly demanding Quality-of-Service (QoS) requirements. This opened the door for novel extended reality (XR) media applications to ...emerge with 5G. However, recent 5G field tests and system-level simulation studies show that further XR enhancements are required to support a massive adoption of XR services in 5G networks. Such enhancements are expected to come into play with 5G-Advanced. In this line, we propose and study an XR loopback mechanism that adapts the XR traffic to the instantaneous 5G network conditions by exploiting an XR application feedback. We propose various XR loopback algorithms, strategies, and parameters' configurations and study their impact on the 5G end-to-end performance. We conduct extensive simulation campaigns by building realistic end-to-end 5G network scenarios with 3GPP mixed XR traffic setups. Results show that the proposed XR loopback mechanism can boost XR performance in 5G networks by adapting to 5G network conditions, while keeping the XR QoS requirements under control. We provide various insights and practical directions on XR loopback design that allow us to take full advantage of the 5G network capabilities and progress toward 5G-Advanced network design.
Fifth Generation New Radio (5G NR) presents a new framework for Quality-of-Service (QoS) management that especially fits cases for eXtended Reality (XR) traffic applications thanks to the new ...presented concept of QoS flows that can handle various service data flows. In that respect, in this paper we exploit the QoS flow model and we present a QoS MAC scheduler that can classify XR and mixed traffic based on the assigned QoS indicators to each traffic. We perform an end-to-end system level evaluation under two scenarios using the ns-3 5G-LENA simulator. First, we validate the correct operation of the QoS MAC scheduler in a single-cell scenario under non-saturation and saturation conditions. Then, we consider a realistic multi-cell scenario defined by the ITU-R and used for calibration of the 5G-LENA simulator, in order to evaluate the proposed solution and study its performance under realistic network conditions. Results demonstrate the correct functionality of the proposed QoS MAC scheduler and prove its ability to classify XR and other types of traffic based on the desired QoS indicators.
Despite the robust security features inherent in the 5G framework, attackers will still discover ways to disrupt 5G unmanned aerial vehicle (UAV) operations and decrease UAV control communication ...performance in Air-to-Ground (A2G) links. Operating under the assumption that the 5G UAV communications infrastructure will never be entirely secure, we propose Deep Attention Recognition (DAtR) as a solution to identify attacks based on a small deep network embedded in authenticated UAVs. Our proposed solution uses two observable parameters: the Signal to Interference plus Noise Ratio (SINR) and the Received Signal Strength Indicator (RSSI) to recognize attacks under Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and a probabilistic combination of the two conditions. Several attackers are located in random positions in the tested scenarios, while their power varies between simulations. Moreover, terrestrial users are included in the network to impose additional complexity on attack detection. Additionally to the application and deep network architecture, our work innovates by mixing both observable parameters inside DAtR and adding two new pre-processing and post-processing techniques embedded in the deep network results to improve accuracy. We compare several performance parameters in our proposed Deep Network. For example, the impact of Long Short-Term-Memory (LSTM) and Attention layers in terms of their overall accuracy, the window size effect, and test the accuracy when only partial data is available in the training process. Finally, we benchmark our deep network with six widely used classifiers regarding classification accuracy. The eXtreme Gradient Boosting (XGB) outperforms all other algorithms in the deep network, for instance, the three top scoring algorithms: Random Forest (RF), CatBoost (CAT), and XGB obtain mean accuracy of 83.24 %, 85.60 %, and 86.33% in LoS conditions, respectively. When compared to XGB, our algorithm improves accuracy by more than 4% in the LoS condition (90.80% with Method 2) and by around 3% in the short-distance NLoS condition (83.07% with Method 1).
Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a ...particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.
Due to the rapid technology evolution and standardization activity in the mobile communication networks, there is the need for the research community to be able to develop, test and evaluate new ...and/or already xisting solutions before industrial or real-network implementation. As such, it is essential to have an open-source tool that provides an alternative solution to that of industrial proprietary simulators that are not available for public usage. ns-3 5G-LENA simulator is an end-to-end open-source NR system-level simulator that allows extensive research to be performed. However, it is of great importance to guarantee that the results obtained using the simulator can be comparable to that of industrial simulators and real networks. For this reason, calibrating the simulator based on 3GPP defined specifications is crucial. Based on the above, in this paper we calibrate the ns-3 5G-LENA simulator according to the 3GPP reference results for NR-based outdoor deployments. Moreover, we explore the REM feature provided by the simulator, to ease the calibration process and understand better the radio environment. Results show the resemblance of the simulator performance to that of simulators used as references by 3GPP.
Synthetic datasets are beneficial for machine learning researchers due to the possibility of experimenting with new strategies and algorithms in the training and testing phases. These datasets can ...easily include more scenarios that might be costly to research with real data or can complement and, in some cases, replace real data measurements, depending on the quality of the synthetic data. They can also solve the unbalanced data problem, avoid overfitting, and can be used in training while testing can be done with real data. In this paper, we present, to the best of our knowledge, the first synthetic dataset for Unmanned Aerial Vehicle (UAV) attacks in 5G and beyond networks based on the following key observable network parameters that indicate power levels: the Received Signal Strength Indicator (RSSI) and the Signal to Interference-plus-Noise Ratio (SINR). The main objective of this data is to enable deep network development for UAV communication security. Especially, for algorithm development or the analysis of time-series data applied to UAV attack recognition. Our proposed dataset provides insights into network functionality when static or moving UAV attackers target authenticated UAVs in an urban environment. The dataset also considers the presence and absence of authenticated terrestrial users in the network, which may decrease the deep networks ability to identify attacks. Furthermore, the data provides deeper comprehension of the metrics available in the 5G physical and MAC layers for machine learning and statistics research. The dataset will available at link archive-beta.ics.uci.edu
Despite the robust security features inherent in the 5G framework, attackers will still discover ways to disrupt 5G unmanned aerial vehicle (UAV) operations and decrease UAV control communication ...performance in Air-to-Ground (A2G) links. Operating under the assumption that the 5G UAV communications infrastructure will never be entirely secure, we propose Deep Attention Recognition (DAtR) as a solution to identify attacks based on a small deep network embedded in authenticated UAVs. Our proposed solution uses two observable parameters: the Signal-to-Interference-plus-Noise Ratio (SINR) and the Reference Signal Received Power (RSSI) to recognize attacks under Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and a probabilistic combination of the two conditions. In the tested scenarios, a number of attackers are located in random positions, while their power is varied in each simulation. Moreover, terrestrial users are included in the network to impose additional complexity on attack detection. To improve the systems overall performance in the attack scenarios, we propose complementing the deep network decision with two mechanisms based on data manipulation and majority voting techniques. We compare several performance parameters in our proposed Deep Network. For example, the impact of Long Short-Term-Memory (LSTM) and Attention layers in terms of their overall accuracy, the window size effect, and test the accuracy when only partial data is available in the training process. Finally, we benchmark our deep network with six widely used classifiers regarding classification accuracy. Our algorithms accuracy exceeds 4% compared with the eXtreme Gradient Boosting (XGB) classifier in LoS condition and around 3% in the short distance NLoS condition. Considering the proposed deep network, all other classifiers present lower accuracy than XGB.
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