5GEN: A tool to generate 5G infrastructure graphs

A DNN-based estimate of the PRACH traffic load for massive IoT scenarios in 5G networks and beyond

The operation of local private cellular radio networks in licensed frequency bands constitutes one of the core innovations of current 5G and future 6G networks. The predicted performance of an exclusively utilizable and thus interference-free frequency range of, for example, private 5G networks, so-called campus networks, are generally of great interest to industrial companies. However, the integration of 5G network infrastructures into existing brownfield environments has to overcome major technological and administrative challenges. In contrast to very rarely encountered greenfield scenarios, the potential of 5G can only be realized in practice if significant performance advantages over existing wireless network infrastructures (e.g., Wi-Fi) can be demonstrated while guaranteeing seamless integration into the process landscape. To this end, this paper presents an agile system for continuous, cross-network monitoring of end-to-end guarantees in terms of throughput, latency and reliability. While single pointwise measurements during network deployment often indicate the expected performance peaks, this contribution specifically explores the potential of a spatially distributed stress test that actively monitors network quality on a continuous basis. An extensive case study is conducted to demonstrate the performance of the distributed approach for performance evaluation of multi-user and cell edge environments. In addition, it is illustrated that the distributed system can be used to estimate how mission-critical service guarantees affect the overall network performance.

Blockchain-enabled anonymous mutual authentication and location privacy-preserving scheme for [formula omitted] networks

Abstract—Fifth generation (5G) networks should use millimeter-wave (mmWave) frequencies to provide high data transmission rate. However, the performance of such networks significantly suffers from the Transmission Control Protocol (TCP) used by various applications for data transmission. In this article, it is shown that the data download rate in 5G networks is much lower than the capacity of the mmWave link because of the Slow Start algorithm used in the TCP. We analyze various approaches to the problem and propose to use the xStream platform for adjusting TCP parameters. This approach significantly increases the download rate and does not require any changes to the software of client devices, which simplifies the implementation of the proposed approach in real systems.

The advent of 5G technology promises a paradigm shift in the realm of telecommunications, offering unprecedented speeds and connectivity. However, the efficient management of traffic in 5G networks remains a critical challenge. It is due to the dynamic and heterogeneous nature of network traffic, including bursty patterns, varying user behaviors, and diverse applications, all of which demand highly accurate and adaptable prediction models to optimize network resource allocation and management. This dissertation investigates the intricate domain of traffic prediction within 5G networks, addressing the specific challenges posed by both massive machine type communication (mMTC) networks and 5G cellular networks. The first segment of this research focuses on mMTC networks, where the event-driven and bursty nature of traffic patterns poses a formidable obstacle to accurate prediction. Forecasting bursty traffic in such environments is a non-trivial task due to the inherent randomness of events, and these challenges intensify within live network environments. Consequently, there is a compelling imperative to design an efficient and agile framework capable of assimilating continuously collected data from the network and accurately forecasting bursty traffic in mMTC networks. The first section of this dissertation is an attempt to addresses these challenges by presenting a machine learning-based framework tailored for forecasting bursty traffic in multi-channel slotted ALOHA networks. We develop a new low-complexity online prediction algorithm that dynamically updates the states of the long-term short-term memory (LSTM) network by leveraging frequently collected data from the mMTC network. Moreover, to evaluate the performance of the proposed framework, we synthesized a realistic mMTC traffic considering both uniform and bursty traffic patterns. In this setup, we considered a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics. Transitioning to the realm of 5G cellular networks, we explore the efficacy of convolutional neural network (CNN)-LSTM and convolutional LSTM (ConvLSTM) models for traffic prediction. Building upon insights from the preceding section, we integrate the proposed live prediction algorithm into these models. Results demonstrate a notable enhancements in prediction accuracy and computational efficiency, signifying a promising avenue for traffic management in 5G cellular networks. Moreover, we study the performance of the proposed live prediction algorithm under the various data collection scenarios. In the final section, we introduce an innovative compression framework to mitigate the data transfer overhead between base stations and centralized nodes. We propose a novel user-specific asymmetric autoencoder (AE)-based data compression framework tailored for data transfer in 5G cellular networks. Leveraging user-specific local AE models and a centralized joint decoder, our framework aims to efficiently compress traffic data while preserving the reconstruction accuracy. In the proposed framework, we utilize a simplified FFNN models in local AEs and CNN layers in the centralized decoder to simultaneously decode the data of all cells by leveraging the spatio-temporal correlations in traffic patterns. Simulation results and complexity analysis highlight the superiority of our proposed live prediction algorithm in terms of both accuracy and computational efficiency, making it well-suited for time-critical live scenarios. In the first section, simulations conducted on synthesized mMTC traffic demonstrate the remarkable accuracy of our machine learning approach in long-term predictions compared to traditional methods, all while imposing minimal additional processing load on the system. The application of our proposed live prediction algorithm to forecast cellular traffic in the second section reveals its superior robustness under both synchronous and asynchronous data gathering scenarios, outperforming traditional methods. Moreover, in asynchronous data gathering scenarios, our algorithm demonstrates the potential to halve the required bandwidth for reporting traffic statistics, illustrating another advantageous aspect of the proposed algorithm. Finally, the performance evaluation of our proposed data compression method on the Telecom Italia dataset in the third section underscores the effectiveness of our approach, achieving superior performance compared to symmetric universal AEs. Furthermore, our framework exhibits reduced complexity, positioning it as a promising solution for practical applications in 5G networks. In summary, this dissertation presents novel methodologies and frameworks aimed at tackling the multifaceted challenges of traffic prediction within diverse 5G network environments. Through the integration of advanced prediction algorithms with innovative data compression techniques, the proposed solutions pave the way for resilient and efficient traffic management in 5G networks, offering promising avenues for future research and implementation.

Initial deployment of 5G networks presents two challenges: Trust in safety of 5G and a reliable methodology for Electromagnetic Field (EMF) estimation. A testbed is presented for direct and indirect EMF measurements where 5G network characteristics are not known a priori. A signal analyser, a field strength analyser and a software radio were used to compare three methodologies – direct EMF measurement, EMF derived from Radio Frequency (RF) measurements, and an extrapolation method to estimate the field. The testbed was deployed in different traffic scenarios in a commercial network in the sub-6 GHz band. Results show that with no induced 5G traffic, the EMF due to 5G signals was negligible. With increased traffic, the EMF increased. The overall EMF due to all mobile services, including 5G, in the test location remained well below the regulatory limit. Directly measured EMF compared well with the RF-derived value. The extrapolation method gave an overestimate due to small variations experienced in the downlink traffic. It however confirms the reliability of the method for a theoretical maximum power density value. The study contributes to developing reliable test methods for EMF in a 5G network without a priori knowledge and adds transparency about the levels of EMF generated by 5G networks.

Mobile Edge Computing (MEC) in 5G networks has emerged as a promising technology to enable efficient and low-latency services for mobile users. In this paper, we present a novel synthetic data generation approach tailored for evaluating MEC in 5G networks. Our methodology incorporates resource-efficient techniques to generate realistic synthetic datasets that capture the spatio-temporal patterns of mobile traffic and user behavior. By leveraging advanced modeling techniques, including multi-head attention and bidirectional LSTM, we accurately model the complex dependencies in the data while optimizing computational resources. The proposed synthetic data generator enables the creation of diverse datasets that closely resemble real-world scenarios, facilitating the evaluation of MEC performance and optimizing resource utilization. Through extensive experiments and evaluations, we demonstrate the effectiveness of our approach in enabling accurate assessments of MEC in 5G networks. Our work contributes to the field by providing a robust methodology for synthetic data generation specifically tailored for MEC evaluation, addressing the need for resource-efficient evaluation frameworks in the context of emerging technologies. The results of our study provide valuable insights for the design and optimization of MEC systems in real-world deployments.

In 5G networks, time-series data will be omnipresent for the monitoring and management of network performance metrics. With the increase in the number of Internet of Things (IoT) devices, it is expected that the number of real-time time-series data streams will increase at a fast pace, making forecasting essential for the proactive successful management of the network. In this article, we discuss to use both linear and non-linear forecasting methods, including machine learning, deep learning, and neural networks to improve 5G networks’ management. For this purpose, we design and implement a real-time distributed forecasting framework, used to make simultaneous predictions of different network performance metrics, and with different learning algorithms. By using our framework, we compare the use of forecasting methods in two network scenarios, in a real vehicular network and in a 4G network, representing two different slices in a 5G network. We also integrate our framework in a 5G architecture. Using the best forecasting models assessed previously, we propose a dynamic threshold algorithm for multi-slice management, to ensure that the resources of each slice are updated according to the slices’ needs, while avoiding congestion and saving resources for other slices. The experimental results show that it is possible to forecast the slices’ needs and congestion probability, selecting the best forecasting approach or an ensemble of the best ones, and act accordingly in the network to optimize its management.

The fifth-generation (5G) mobile network promises to offer low latency services. Hence, there is interest in assessing various power distribution grid applications that can be deployed with a 5G infrastructure. This paper presents a smart grid cyber-physical testbed for protection systems. It consists of power system applications deployed on OPAL-RT, a real-time platform, and a 5G communication network modeled in ns-3. The testbed is used to assess the performance of a power system protection application (Permissive Underreaching Transfer Trip (PUTT) protection scheme) deployed over a 5G communication network. The proposed approach enables real-time protection traffic to be analyzed in an emulated 5G network and gives insights into how such a testbed can be used to assess the performance of protection traffic in 5G networks and beyond.

The current industrial Internet is usually composed of heterogeneous network infrastructure. In order to adapt to different business scenarios, 5G network needs to improve the ability to flexible orchestration and realize the vertical deployment of service and infrastructure integration. Based on 5G network orchestration architecture and open source mano (OSM) technology, a 5G network scenario orchestration platform superimposed on heterogeneous 5G network resource orchestrator is designed. Furthermore, in order to effectively combine heterogeneous 5G network resources and provide reliable bearer for high-performance business scenarios, this paper designs a reliable path orchestration algorithm to establish equivalent backup paths between unrelated heterogeneous networks. This paper implements the scenario-based orchestration platform design for heterogeneous 5G networks and provides an equivalent path orchestration algorithm. Simulation experiments show that the proposed algorithm can provide highly reliable orchestration services under the condition of sharing heterogeneous network resources.

Due to the development of mobile communication, user demand and traffic increased, and various services were produced. With existing 3G networks, it was hard to demand these services and users' demand. Therefore, the 4G network has been developed. As evolving into 4G networks, much experimental and development software have been created. OAI is a hardware and software platform for 4G networks that can be used for various experiments and developments. In this paper, we have implemented a system to solve the inefficiencies in the configuration of 4G networks created by combining SDN technology and OAI. This makes it possible to set up and monitor eNodeBs and UEs. This paper consists of two parts. The system structure part deals with the structure of the implemented system. The next section deals with the actual operation of the implemented system and how it is configured and monitored.

This article mainly analyzes the application scenarios of computer network and 6G network in electronic information engineering. This article briefly analyzes the practice of computer network technology in electronic information engineering, and puts forward some application suggestions. The progress of the engineering industry contributes to its strength. In the 6G era, endogenous intelligence is expected to be formed. However, most of the current research focuses on artificial intelligence-based transmission and network optimization methods, and seldom discusses how to efficiently support the implementation of artificial intelligence methods at the architecture design level.

5G networks have an efficient effect in energy consumption and provide a quality experience to many communication devices. Device-to-device communication is one of the key technologies of 5G networks. Internet of Things (IoT) applying 5G infrastructure changes the application scenario in many fields especially real-time communication between machines, data, and people. The 5G network has expanded rapidly around the world including in healthcare. Telemedicine provides long-distance medical communication and services. Patient can get help with ambulatory care or other medical services in remote areas. 5G and IoT will become important parts of next generation smart medical healthcare. Telemedicine is a technology of electronic message and telecommunication related to healthcare, which is implemented in public networks. Privacy issue of transmitted information in telemedicine is important because the information is sensitive and private. In this paper, 5G-based federated anonymous identity management for medical privacy protection is proposed, and it can provide a secure way to protect medical privacy. There are some properties below. (i) The proposed scheme provides federated identity management which can manage identity of devices in a hierarchical structure efficiently. (ii) Identity authentication will be achieved by mutual authentication. (iii) The proposed scheme provides session key to secure transmitted data which is related to privacy of patients. (iv) The proposed scheme provides anonymous identities for devices in order to reduce the possibility of leaking transmitted medical data and real information of device and its owner. (v) If one of devices transmit abnormal data, proposed scheme provides traceability for servers of medical institute. (vi) Proposed scheme provides signature for non-repudiation.

Flexibility and energy efficiency are considered two principal requirements of future fifth generation (5G) systems. From an architectural point of view, centralized processing and a dense deployment of small cells will play a vital role in enabling the efficient and dynamic operation of 5G networks. In this context, reconfigurable hotspots will provide on-demand services and adapt their operation in accordance to traffic re quirements, constituting a vital element of the heterogeneous 5G network infrastructure. In this paper we present a reconfigurable hotspot which is able to flexibly distribute its underlying communication functions across the network, as well as to adapt various parameters affecting the generation of the transmitted signal. The reconfiguration of the hotspot focuses on minimizing its energy footprint, while accounting for the current operative requirements. A real-time hotspot prototype has been developed to facilitate the realistic evaluation of the energy saving gains of the proposed scheme. The development flexibly combines software (SW) and hardware (HW) accelerated (HWA) functions in order to enable the agile reconfiguration of the hotspot. Actual power consumption measurements are presented for various relevant 5G networking scenarios and hotspot configurations. This thorough characterization of the energy footprint of the different subsystems of the prototype allows to map reconfiguration strategies to different use cases. Finally, the energy-aware design and implementation of the hotspot prototype is widely detailed in an effort to underline its importance to the provision of the flexibility and energy efficiency to future 5G systems.

The new generation mobile network 5G has become the main technology foundation in the architecture of the Internet of Things (IoT). In particular, millimeter wave communication technology in 5G is the core technology of the new generation network. Research on 5G networks faces many challenges in deploying real-life models to evaluate effectiveness. To resolve those issues, the article introduced the mmWave module integrated in the NS-3 network simulation toolkit to simulate and evaluate 5G network performance. The mmWave module is applied to simulate 5G network scenarios with the signal-to-noise ratio parameter SINR (Signal Interference + Noise Ration), thereby evaluating the wave quality from the base station to the terminal users to determine the most optimal location to build the base station.