5G-Network-Metrics-for-High-Traffic-Event

Dataset Description:

Based on some real-world events, the dataset offers a synthetic representation of 5G network states and metrics during a high traffic event, such as a major sports gathering in a city. Each row corresponds to a unique record capturing the attributes of the network at a particular moment, and each column corresponds to a specific feature or attribute.

Significance:

§  Network Monitoring & Analysis: The dataset can be used to monitor the network's performance, identifying areas of strength and potential bottlenecks or issues.

§  Resource Allocation: It can assist in making decisions about resource allocation, ensuring efficient utilization and optimal user experience.

§  Predictive Analysis: The dataset can be used to predict future network states or user demands, helping operators prepare in advance.

§  Network Optimization: The detailed attributes can aid in fine-tuning network configurations for performance, reliability, and efficiency.

§  Event-Driven Network Analysis: This dataset can be pivotal in understanding how a 5G network behaves during high-demand events. It can shed light on the network's adaptability, resilience, and potential pressure points.

§  Capacity Planning: By analyzing the dataset, network operators can anticipate the kind of load their infrastructure might face during similar future events and can plan capacity upgrades accordingly.

§  Service Assurance: The dataset can highlight areas where service quality might degrade during such events, enabling preemptive measures.

§  Traffic Management: Insights from the dataset can guide strategies for traffic shaping, load balancing, and content delivery optimization during high traffic periods.

Nature:

§  Diverse: The dataset covers a wide range of network metrics, from signal strength to user equipment status, providing a holistic view.

§  Multifaceted: It provides both categorical (e.g., UE Connection Status) and numerical (e.g., RSSI) data, offering a comprehensive view of the network.

§  Synthetic: Though the data is artificially generated, it's shaped to emulate real-world behavior during high traffic events and is valuable for simulations, model training, and theoretical analysis.

Beneficial Features:

Comprehensiveness: The dataset encompasses almost all crucial aspects of a 5G network, making it valuable for a multitude of use-cases.

§  Granularity: Features like RSSI, RSRP, and latency provide detailed insights into the network's performance at a granular level.

§  Resource Allocation Metrics: With attributes related to bandwidth utilization, resource block allocation, and current resource allocation, operators can gauge the efficiency of resource utilization.

§  User-Centric Metrics: Features like UE demand and connection status ('UE_Status' column) give insights into user behavior and requirements.

§  Network Health Indicators: Metrics like retransmission rates, channel conditions, and interference levels serve as indicators of the network's overall health and performance.

Event-Centric Metrics: The heightened values in traffic load, bandwidth utilization, and user demand capture the essence of the high traffic event.