This dataset simulates communication data for 5G-enabled Vehicle-to-Network (V2N) systems, aimed at supporting research on dynamic resource optimization and cybersecurity strategies. It includes comprehensive data on vehicle communication, network performance, user behavior, and cybersecurity events, which are essential for the development of machine learning models for 5G V2N networks. The dataset is designed for predictive resource management, network optimization, and the evaluation of cybersecurity techniques like anomaly detection and encryption.

Key Features:

Vehicle Communication Data:

Vehicle ID: Unique identifier for each vehicle.

Position: Geographic coordinates or grid representation (latitude, longitude).

Speed (km/h): Speed of the vehicle in kilometers per hour.

Data Transfer Rate (Mbps): Rate at which data is transferred between the vehicle and the network.

Connection Duration (s): Duration the vehicle is connected to the network.

Traffic Density (%): The density of vehicles in a specific region impacting network load.

Network Load Data:

Network Load (Mbps): Amount of traffic the network is handling at any given time.

Latency (ms): Time taken for data to travel between the vehicle and network (in milliseconds).

Throughput (Mbps): The actual data rate achieved in communication.

Signal Strength (dBm): Strength of the communication signal between the vehicle and network.

User Behavior Data:

User Behavior Type: Categorical data indicating if the user is engaging in normal or high-bandwidth activities.

Request Type: Type of request initiated by the vehicle (e.g., navigation, media streaming).

Cybersecurity Data:

Intrusion Detected: Binary flag indicating whether an intrusion was detected (0 = No, 1 = Yes).

Anomaly Score: Numeric score indicating the severity of any detected anomaly (0 = normal, 1 = severe anomaly).

Encryption Status: Categorical indicator of whether communication is encrypted (Yes/No).

Intrusion Type: Type of detected intrusion (e.g., DoS, MITM, Data Exfiltration).

Resource Allocation Data:

Resource Allocation (Mbps): Bandwidth allocated to each vehicle for communication.

Optimization Status: Indicator of whether resource optimization was applied (0 = No, 1 = Yes).

Simulations in ns-3 were run to produce the figures submitted. For our simulations, we used up to seven gateway nodes, one network server node and between 100 and 300 end nodes in a 10 km × 10 km network, sending data packets at different time intervals. We coded our algorithms inside the ADR component code of the ns-3 LoRaWAN module. We analysed the system performance of different ADR models, namely, the standard ADR model, which we term Semtech-ADR, the ADR model implemented in the ns-3 LoRaWAN module, which we term ns-3-ADR and our proposed fuzzy logic-based ADR known as FL-ADR. We performed two different evaluations and analyses. In the first evaluation, we used 100 EDs and changed the Application Data Packet Rate to transmit 1 packet per 300 s, 600 s, 900 s, 1200 s and 1500 s. In the second evaluation, we kept the application data rate constant at 1 packet per 600 s and varied the number of EDs to 100, 150, 200, 250 and 300 and analysed the performance. The simulation was configured to simulate for 3.3 h.

Signal transduction is the process of routing information inside cells when receiving stimuli from their environment that modulate the behavior and function. In such biological processes, the receptors, after receiving the corresponding signals, activate a number of biomolecules which eventually transduce the signal to the nucleus. The main objective of our work is to develop a theoretical approach which will help to better understand the behavior of signal transduction networks due to changes in kinetic parameters and network topology. By using an evolutionary algorithm, we designed a mathematical model which performs basic signaling tasks similar to the signaling process of living cells. We use a simple dynamical model of signaling networks of interacting proteins and their complexes. We study the evolution of signaling networks described by mass-action kinetics. The fitness of the networks is determined by the number of signals detected out of a series of signals with varying strength. The mutations include changes in the reaction rate and network topology. We found that stronger interactions and addition of new nodes lead to improved evolved responses. The strength of the signal does not play any role in determining the response type. This model will help to understand the dynamic behavior of the proteins involved in signaling pathways. It will also help to understand the robustness of the kinetics of the output response upon changes in the rate of reactions and the topology of the network.

This dataset provides synthetic data designed to analyze and enhance the security mechanisms in 6G network slices, with a particular focus on AI-driven attack detection and mitigation strategies. The data represents various network slice configurations, including bandwidth, latency, and security mechanisms, alongside potential attack types such as Distributed Denial of Service (DDoS), spoofing, and eavesdropping.

The target column, "Security Status," indicates whether the applied security measures succeeded in mitigating the attack, forming a valuable foundation for training machine learning models for predicting security outcomes in 6G networks. This dataset is ideal for research in cybersecurity, machine learning for network security, and the optimization of 6G network slice configurations.

Key Features: Slice ID: Unique identifier for each network slice. Slice Type: Type of network slice (e.g., Autonomous, IoT, Ultra-High-Speed). Slice Bandwidth (Mbps): Available bandwidth for the network slice. Latency (ms): Latency within the network slice. Packet Loss Rate (%): Percentage of lost packets during transmission. Data Rate (Mbps): Effective data rate after considering congestion and losses. Traffic Load: Level of traffic load (e.g., Low, Medium, High). Signal Strength: Signal strength in the network slice (e.g., Weak, Moderate, Strong). Network Congestion: Level of congestion within the network slice. Transmission Path: The chosen path for data transmission (e.g., Path A, Path B, Path C). Security Mechanism: Type of security measure applied (e.g., Firewall, IDS, Encryption). Attack Type: Type of security threat (e.g., DDoS, Spoofing, Eavesdropping). Attack Detection Time (ms): Time taken to detect the attack. IGJO-SVM Prediction: Prediction score indicating likelihood of an attack. Adjustment Action: Recommended action based on attack prediction (e.g., Increase Security, Optimize Path, Alert Admin). Throughput (Mbps): Actual throughput achieved under current network conditions. Energy Efficiency (J/bit): Energy consumed per bit of data transmitted. Packet Delivery Ratio (%): The percentage of successfully delivered packets. End-to-End Delay (ms): Total time for data to travel from sender to receiver. Accuracy of Security (%): The accuracy of the applied security mechanism in mitigating the attack. Target Column:

Security Status: Binary classification indicating whether the security mechanism successfully mitigated the attack (Yes/No).

A Flexible Machine Learning-Aware Architecture for Future WLANs

Authors: Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram.

Abstract: Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.

Dataset description: This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs).

In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are:

Identifier of the AP to which the STA has been associated.

RSSI obtained from the AP to which the STA has been associated.

Data rate in bits per second (bps) that the STA is allowed to use for the selected AP.

Load in packets per second (pkt/s) that the STA generates.

Percentage of data that the AP is able to serve before the user association is done.

Amount of traffic load in pkt/s handled by the AP before the user association is done.

Airtime in % that the AP enjoys before the user association is done.

Throughput in pkt/s that the STA receives after the user association is done.

The dataset has been generated through random simulations, based on the model provided in https://github.com/toniadame/WiFi_AP_Selection_Framework. More details regarding the dataset generation have been provided in https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans.

The 6G Wireless Network Slicing QoS Prediction and Optimization Dataset contains a comprehensive collection of data designed to support the prediction and optimization of Quality of Service (QoS) in 6G network slices. The dataset includes 2345 rows and multiple features, capturing various network parameters such as traffic load, network utilization, latency, packet loss, signal strength, and more. These features are essential for understanding and improving the performance of network slices under different conditions, including varying traffic types, device types, and network failure scenarios.

The dataset is intended for use in machine learning models, particularly for predicting the throughput (QoS metric) and optimizing network resources. Preprocessing steps like Min-Max normalization and label encoding have been applied to ensure the data is ready for analysis. This dataset is ideal for researchers and engineers working on 6G networks, aiming to improve reliability, security, and efficiency.

Features Included: Traffic Load (bps): The network traffic load in bits per second. Network Utilization (%): The percentage of network resources being used. Latency (ms): The delay in the network, measured in milliseconds. Packet Loss Rate (%): The percentage of packets lost during transmission. Signal Strength (dBm): The strength of the wireless signal. Bandwidth Utilization (%): The percentage of available bandwidth used. Device Type: The type of device (e.g., Mobile, IoT). Traffic Type: The type of network traffic (e.g., Voice, Video, Data). Network Slice Failure: Indicates if there was a network slice failure. Overload Status: Indicates if the network is overloaded. QoS Metric (Throughput): The target column representing the Quality of Service metric (throughput in Mbps).

This dataset presents a large-scale synthetic simulation of performance parameters in fiber-optic communication systems, specifically designed to evaluate the impact of EDFA (Erbium-Doped Fiber Amplifier) and FBG (Fiber Bragg Grating) components under varying transmission distances and conditions. The dataset comprises 1,000,000 rows of simulated records, each capturing key optical network metrics including Q-Factor, Bit Error Rate (BER), and Receiver Sensitivity for both downstream and upstream transmission directions.

The core objective of this dataset is to provide a controlled and reproducible framework for studying how signal quality degrades or improves under different fiber distances and the presence or absence of EDFA/FBG devices. The dataset generation process was carefully crafted using stochastic modeling based on empirical trends observed in real-world optical experiments. The following parameters are included in the dataset:

• Distance_km: The length of the optical fiber in kilometers. Values include typical transmission spans (0, 10, 20, 40, 45, 70, and 75 km).
• Q_Factor_Downstream and Q_Factor_Upstream: Quality factor metrics for downstream and upstream channels respectively. These values are adjusted based on the transmission distance and the presence of EDFA/FBG to reflect signal integrity.
• BER_Downstream and BER_Upstream: Bit Error Rates computed from the Q-factor using an exponential decay model that mimics realistic signal degradation in optical fibers.
• Receiver_Sensitivity_Downstream and Receiver_Sensitivity_Upstream: Values representing how sensitive the receiver is to signal quality degradation at different distances, influenced by the transmission condition.
• Power_Level_dBm: Simulated variation in power levels (in dBm) to account for fluctuations due to hardware or environmental conditions. -mNoise_Factor: A synthetic noise parameter that introduces random variations to simulate physical imperfections and disturbances in signal transmission. Condition: Indicates whether the data point is simulated under "No_EDFA_FBG" or "With_EDFA_FBG" conditions.

The dataset serves as a valuable resource for research in several domains including:

• Optical Network Simulation and Modeling
• Machine Learning for Optical Systems
• Performance Prediction and Optimization in Fiber-Optic Networks
• Benchmarking of AI-based Diagnostic Tools in Telecommunications

Researchers can use this dataset to train, validate, and benchmark machine learning models for signal classification, fault detection, adaptive modulation, or link quality prediction. The inclusion of both amplified and unamplified scenarios makes the dataset versatile for comparative studies and ablation analysis.

This synthetic dataset is fully reproducible, extendable, and free from real-world acquisition constraints, making it suitable for academic and industrial experimentation, prototyping, and algorithm development in next-generation optical communication systems.

The InfiniBand market is poised for significant expansion, projected to reach a substantial market size of approximately $5,500 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 22% anticipated throughout the forecast period of 2025-2033. This growth is primarily fueled by the escalating demand for high-performance computing (HPC) solutions across various sectors, including scientific research, financial services, and artificial intelligence workloads. The increasing complexity and data-intensiveness of these applications necessitate network interconnects that can deliver unparalleled speed and low latency, positioning InfiniBand as a critical technology. Furthermore, the continuous advancements in InfiniBand technology, such as the evolution from Single Data Rate (SDR) to higher data rates like Enhanced Data Rate (EDR) and beyond, are enhancing its capabilities and expanding its applicability, driving adoption in data centers and enterprise environments. The market's trajectory is further shaped by key trends such as the widespread adoption of AI and machine learning, which demand massive data transfer capabilities for training and inference. The burgeoning cloud computing infrastructure, with its emphasis on scalability and efficiency, also contributes significantly to InfiniBand market growth. While the market is generally robust, potential restraints include the high initial cost of implementation and the availability of competitive high-speed networking technologies. However, the superior performance characteristics of InfiniBand in demanding HPC and AI environments are expected to outweigh these concerns for many organizations. Key players like Intel and Mellanox are continually innovating, introducing newer generations of InfiniBand that offer even greater bandwidth and lower latency, ensuring the market's sustained upward momentum. The market is segmented by application into Residential Use, Commercial Use, and Other, with Commercial Use expected to dominate due to enterprise HPC demands. By type, the market is segmented into Single Data Rate, Double Data Rate, Quad Data Rate, Fourteen Data Rate, and Enhanced Data Rate, with higher data rates gaining prominence. This report offers an in-depth analysis of the global InfiniBand market, projecting its trajectory from 2019-2033, with a Base Year of 2025 and a Forecast Period of 2025-2033. The Study Period encompasses the Historical Period of 2019-2024, providing crucial context for future market dynamics. We estimate the market to reach tens of millions in value during the forecast period.

Optimum transmission data rate in kbps for M-QAM based schemes at various distances.

This dataset contains the predicted prices of the asset NETWORK STATE over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset contains the predicted prices of the asset Multiple Network over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset contains the predicted prices of the asset network over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

R&D in wireless networking typically depends on experimentation to make realistic evaluations, since simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility.

Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce: either the original community testbed is unavailable – offline or running other experiments – or the custom testbed used is inaccessible.

Fed4FIRE+ wireless testbeds such as w-iLab.t and NITOS, although deployed in controlled environments, do not fully address the problem. The CONCRETE tool used in such testbeds assures the repeatability and reproducibility of experiments, but ignores executions whose results are also representative of the system operation and often reveal unpredicted behaviour that must be understood.

What if we could make any wireless experiment repeatable and reproducible under the same exact conditions? What if we could share the same Fed4FIRE+ testbed execution conditions among an "infinite" number of users? What if we could run wireless experiments faster than in real time?

INESC TEC has been developing the Offline Experimentation (OE) approach that combines the best of simulation and experimentation to achieve the above-mentioned goals. By relying on Network Simulator 3 (ns-3) and its good simulation capabilities from the MAC to the application layer, we have been exploring how ns-3 can be used to replicate real-world wireless experiments using real traces containing 1) position of nodes and 2) the quality of each radio link.

The SIMBED project aimed at running a set of wireless experiments on top of the controlled environments of w-ilab.t and NITOS Fed4FIRE+ testbeds to further validate the OE approach. For that purpose, we configured different fixed and mobile experimental scenarios, representative of Wi-Fi range of operation, and measured the attained network performance using metrics such as throughput and Round-Trip Time (RTT). Then, we repeated each experiment using, both, Pure Simulation (PS) and OE approaches based on ns-3, also measuring the network performance for the same set of executions of experiments for all the different scenarios.

By comparing the performance metrics of each real experiment with its PS and OE counterparts, we were able to measure the relative error of each simulation approach relatively to the real experiments, as well as the accuracy gains introduced by the OE approach when compared to the PS traditional alternative. The main results show that it is possible to repeat and reproduce real experiments in ns-3, using the OE approach, achieving closer to real performance than using the PS approach. For all the experiments performed in SIMBED, using the OE approach resulted in an average accuracy gain of 59% when comparing to the PS approach.

These results were important for validating a PhD thesis contribution related to the OE approach, as well as for producing two conference papers and one journal paper. The SIMBED results increased our confidence on the accuracy of the OE approach and are envisioned to foster the adoption of the OE approach by the networking community, in complement to the use of real experimentation.

The following dataset presents the results of the SIMBED project, organized in different folders, for each subset of experiments carried on:

SubExp#1: Static point-to-point Wi-Fi communications using auto-rate (Minstrel)

SubExp#1.1: Using w-iLab.2 (medium to high SNR scenarios)

SubExp#1.2: Using w-iLab.2 (low SNR scenarios)

SubExp#1.3: Using NITOS

SubExp#1.4: Using w-iLab.1 (datacenter room)

SubExp#2: Static point-to-point Wi-Fi communications using fixed rate

SubExp#3: Mobile point-to-point Wi-Fi communications using auto-rate (Minstrel)

SubExp#4: Static multiple access Wi-Fi communications using auto-rate (Minstrel)

SubExp#4.1: Using w-iLab.2 (bidirectional) (medium to high SNR scenarios)

SubExp#4.2: Using w-iLab.2 (bidirectional) (low SNR scenarios)

SubExp#4.3: Using NITOS (bidirectional)

SubExp#4.4: Using w-iLab.1 (bidirectional)

SubExp#4.5: Using NITOS (2 STAs)

SubExp#4.6: Using w-iLab.2 (2 STAs)

SubExpExample: contains raw experimental logs, parsed data and simulation results, to show how data extracted from the nodes is processed to be compatible with the OE approach and comparable with OE and PS simulation results.

Each experiment has an individual folder, named according to the date and time of the experiment and the nodes used. Inside, there’s a folder for the parsed experimental results, which contains

This folder contains the details and parsed logs of the experiment, as follows:

date_time.cfg – configuration details of the experiment

date_time_NodeID[1]_SenderID[2]_ReceiverID[3]_FlowType[4]_Params[5].snr – logs of the Signal/Noise ratio (1 file per node/flow)

date_time_NodeID_SenderID_ReceiverID_FlowType_Params.stats – logs of the packets received (1 file per node/flow)

NodeID.waypoints – coordinates of the static nodes

date_time_MobileNodeID.waypoints – waypoints of the mobile nodes (when applicable)

The experiment’s folder also contains a folder for the simulations output with the simulations statistics files, for the multiple simulations approaches considered, as follows:

date_time_NodeID_SenderID_ReceiverID_FlowType_Params.simstats – logs of the packets received (simulation)

[1] ID of the node Logging node

[2] ID of the Sender node

[3] ID of the Receiver node

[4] Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access

[5] Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)

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5G NTN Market Size 2024-2028

The 5g ntn market size is valued to increase USD 18.35 billion, at a CAGR of 39.19% from 2023 to 2028. Rising demand for data-intensive services and applications will drive the 5g ntn market.

Major Market Trends & Insights

North America dominated the market and accounted for a 40% growth during the forecast period.
By Component - Hardware segment was valued at USD 462.84 billion in 2022
By Application - EMBB segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 2.00 million
Market Future Opportunities: USD 18347.83 million
CAGR : 39.19%
North America: Largest market in 2022

Market Summary

The 5G Non-Terrestrial Network (NTN) Market represents a dynamic and evolving landscape, driven by the rising demand for data-intensive services and applications. With the integration of 5G NTNs into existing Mobile Network Operators' (MNOs) networks, this technology is poised to revolutionize industries such as aviation, maritime, and energy. However, the high cost of implementing 5G NTNs and the substantial investments required for infrastructure development pose significant challenges. According to a recent study, the global market share for 5G NTN is projected to reach 15% by 2026, underscoring the growing importance of this technology. Despite these challenges, opportunities abound, particularly in the areas of enhanced connectivity, low latency, and improved reliability. Regulations and standards, such as those set by the International Telecommunication Union (ITU), continue to shape the market's evolution.

What will be the Size of the 5G NTN Market during the forecast period?

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How is the 5G NTN Market Segmented and what are the key trends of market segmentation?

The 5g ntn industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ComponentHardwareSoftwareServiceApplicationEMBBMMTCURLCCGeographyNorth AmericaUSEuropeGermanyUKAPACChinaIndiaRest of World (ROW)

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period.

The 5G Non-Terrestrial Network (NTN) market is experiencing substantial growth, particularly in the hardware segment. Satellite-based communication systems are a key driver of this expansion, as they provide extensive coverage and enable 5G connectivity in areas where terrestrial networks are limited. This is crucial for bridging the digital divide and ensuring universal Internet access. Propagation modeling and authentication mechanisms are essential components of these satellite-based solutions. Performance evaluation and system integration are also critical for ensuring seamless communication and minimal latency. Massive MIMO technology and non-terrestrial networks are being integrated to enhance network capacity and coverage. Network management systems, edge computing, and power efficiency are other significant trends in the market. Data rate and network security are essential considerations for these systems, with link budgets and deployment strategies being optimized to minimize signal interference. Data encryption, inter-satellite links, network slicing, and spectrum allocation are all crucial aspects of securing and managing these networks. Satellite gateways, latency optimization, and IoT connectivity are also gaining traction in the market. Machine-type communication (MTC) and 5G satellite communication are expected to revolutionize industries such as transportation, energy, and healthcare. According to recent studies, the market is projected to grow by 30% in the next year. Additionally, long-term industry expectations suggest a potential expansion of up to 45% over the next five years. These figures underscore the significant potential of this market and its role in shaping the future of global connectivity. Key players in the market are investing heavily in research and development, focusing on advancing technologies such as satellite constellations, ground segment infrastructure, security protocols, and spectrum allocation. These efforts are expected to lead to further growth and innovation in the sector. In conclusion, the market is experiencing rapid growth, driven by the deployment of satellite-based communication systems. Key trends include the integration of massive MIMO, network management systems, edge computing, and power efficiency. The market is projected to expand significantly in the coming years, with a potential growth rate of up to 45%.

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The Hardware segment was valued at USD 462.84 billion in 2018 and showed a gradual increase during the forecast peri

• DS1: the symbol rate is fixed and equals 64 Gbaud, and the channel loading factor is selected from [25 − 100];
• DS2: the symbol rate and channel occupancy status is randomly selected (uniformly distributed) from {32, 64, 96 (GBaud) and {0, 1}, respectively;
• DS3: both symbol rate and the channel loading factor are fixed and equal to 64 GBaud and 25%, respectively.

Explore the dynamic Bit Error Rate Testers (BERT) market, projected to reach USD 750 million by 2025 and grow at an 8.5% CAGR. Discover drivers like 5G, fiber optics, and data center expansion shaping this vital sector.

The high-speed data rate satellite modem market is booming, projected to reach $156 million by 2025 with a 6.6% CAGR. Discover key trends, drivers, and regional insights shaping this rapidly expanding sector, including leading companies and applications like mobile backhaul and offshore communication. Explore the future of satellite connectivity.