Project Description
This project is dedicated to providing a robust platform for developing and validating cutting-edge solutions within the 5G cellular network domain, including the multi-source fusion of data in C-V2X networks and the application of Reinforcement Learning (RL) algorithms for dynamic optimization. These solutions are tailored to adaptively respond to fluctuating Quality of Service (QoS) demands and resource needs. At the heart of this initiative is the construction of a digital twin of the 5G network, which meticulously replicates the Verizon 5G lab environment. This simulated framework not only facilitates rigorous analysis but also ensures that all architectural and operational characteristics are precisely aligned with those of the actual lab environment, thereby providing a foundation for detailed data analysis and performance benchmarking. Through this platform, the project aims to drive the effective development and validation of AI/ML solutions, ensuring these technologies can significantly contribute to the sophistication and adaptability of 5G network strategies.
Verizon Lab Digital Twin Testbed
In partnership with Keysight Technologies, and Verizon, this initiative utilizes the Exata platform to develop highly accurate digital twins of existing 5G networks. The network architecture in the digital twin encompasses elements such as the AMF, AUSF, SMF, UPF, and DN, integrated with a RAN network that includes a GnodeB and two UEs. This setup is crucial for simulating realistic wireless channel models and traffic patterns, which are essential for the subsequent data analysis phase.
Data Analysis to Identify Real-to-Simulation Gaps
Our ongoing analysis of 5G network performance under various traffic conditions (CBR and FTP) is essential for tuning our key performance indicators—latency, throughput, packet loss, jitter, and buffering rate. Through time series and histogram analyses, we're pinpointing trends and understanding how different loads and UE numbers affect network performance. Initial trends indicate a significant inverse correlation between latency and throughput, with increased traffic exacerbating jitter and packet loss. These insights from statistical testing are crucial for guiding our network optimization strategies. As we progress, we aim to minimize the discrepancies between our digital twin simulations and real Verizon 5G lab data. This gap analysis is not only pivotal for refining our simulation models but also sets the foundation for developing and implementing an AI/ML algorithm. By understanding these gaps, we can better predict how AI/ML solutions might differentially impact network performance in simulated versus real settings, thereby enhancing the accuracy and efficacy of our AI-driven optimization strategies.
