Deep learning-based algorithms for high capacity transmission of orthogonal multiple access and non-orthogonal multiple access systems - Xu, Chuang (Nanyang Technological University, 2024)
Over the past decade, with the massive spike in the use of data-hungry applications such as streaming Netflix in 4k, playing graphic-intensive PC games, and online learning, there is a need for a faster data transmission rate. In this project, we aim to apply deep learning techniques to current data transmission methods such as orthogonal frequency-division multiple access (OFDMA) and upcoming non-orthogonal multiple access (NOMA) method to improve the capacity of a channel. The student will explore the advantage of deep-learning techniques to improve the system capacity and compare with existing conventional methods. Matlab or Python programming will be used to study the performance of the proposed scheme.