Research Projects

6G networks are anticipated to offer unprecedented performance levels, supporting a diverse array of applications, from ultra-reliable low-latency communications to massive machine-type communications and enhanced mobile broadband. However, realizing these advancements necessitates overcoming several critical challenges, particularly in data collection, standardization, and resource management. Currently, one of the most significant challenges lies with the lack of high-quality, standardized datasets and effective tools for RF data collection and management. Existing datasets often suffer from inconsistencies in data formats and channel models, making it difficult for researchers to compare different Artificial Intelligence and Machine Learning (AI/ML) models and adopt broader datasets. The project addresses this gap by leveraging USRP transceiver devices to perform comprehensive RF data recording. This initiative will lead to the development of standardized datasets that will ensure consistency, enable meaningful comparisons, and facilitate the training of robust AI/ML models tailored for 5G and 6G networks. Moreover, developing AI/ML-based solutions for resource management across various 6G scenarios is crucial. As 6G networks will operate in highly dynamic and heterogeneous environments, efficient resource management is essential for optimizing network performance and ensuring seamless user experiences. By exploring different scenarios and developing adaptive AI/ML solutions, the project will contribute to the creation of intelligent systems capable of making data-driven decisions to manage resources effectively. Another critical is to develop resource management strategies under conditions of imperfect Channel State Information (CSI). In real-world network operations, obtaining perfect CSI is often impractical due to factors such as mobility, interference, and hardware limitations. Therefore, developing robust resource management strategies that can operate effectively with imperfect CSI is vital for the reliable performance of 6G networks. This ensures that network resources are allocated efficiently even under less-than-ideal conditions, thereby enhancing the resilience and robustness of the network.