NEXT GENERATION MASSIVE ACCESS TECHNIQUES FOR IOT SYSTEMS IN 6G

Abstract

This thesis document introduces a new communication paradigm for the unsourced and uncoordinated Gaussian multiple access problem. The key idea is to implement a low-complexity version of the already existing sparse IDMA scheme. However, instead of the complex joint Belief Propagation algorithm used in sparse IDMA, this scheme uses a simpler single-user serial LDPC decoding algorithm using a fixed (128, 64)-LDPC code for each number of active users. This eliminates the need for complex optimization procedures. Each user’s message is divided into two parts. The first phase is transmitted using compressed sensing, identifying messages from different users, while the second phase uses the LDPC coding scheme for data recovery. However, the single-user code chosen, although simple, is ineffective. Therefore, an in-depth study will be conducted on the performance of the CS solver in order to maximize multi-user detection, avoiding unnecessarily stressing the data recovery part. Finally, the scheme is integrated with Successive Interference Cancellation and Maximal Ration Combining for further performance improvements. This approach offers good performance with low computational complexity,as demonstrated by simulations compared to existing methods.