Performance Optimization for Large-Scale Random Access Communication Using Deep Learning and Information Theory

Reference No.	2023a013
Type/Category	Grant for Supporting the Advancement of Female Researchers- Short-term Visiting Researcher
Title of Research Project	Performance Optimization for Large-Scale Random Access Communication Using Deep Learning and Information Theory
Principal Investigator	SHAN LU（Department of Electrical, Electronic and Computer Engineering, Gifu University・Assistant professor ）
Research Period	September 4,2023. ～ September 8,2023. March 25,2024. ～ March 29,2024.
Keyword(s) of Research Fields	large-scale random access, information theory, deep learning
Abstract for Research Report	In the 6th generation mobile communication network (6G), a large-scale random access communication systems are crucial for accommodating a massive number of communication devices on the same frequency band with high reliability, low latency, and scalability. In these communication systems, the number of users is immense (virtually infinite), allowing for simultaneous transmission by multiple users. In this case, the central station in the receiver needs to estimate the messages and communication channel states of active users. Recently, reseaches have focused on methods to modele the estimation and decoding of communication channels and messages by processing extensive communication data through machine learning and deep learning techniques. However, it is generally infeasible for the learning data to encompass all patterns since the range of communication channel states and active user patterns is infinite. This limitation may lead to performance degradation or inadequate scalability. This research employ information theory and statistical methods to extract the features of learning and output data from communication models based on deep learning. This objective of this research is to optimize system performance by utilizing the features of the learned communication model and reconfiguring the communication system. The reseach requires algebraic methods and probabilistic and statistical methods and anticipated to generate new problem statements in related mathematical sciences fields Simultaneously. From a practical standpoint, it is expected to contribute to further development of 6G networks by achieving large-scale random access communication channel capacity, and innovative mobile communication methods (codes).
Organizing Committee Members (Workshop) Participants (Short-term Joint Usage)	Yujie Gu（Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University・Assistant Professor）