2024/05/02 by Hanwen Zhang, Mingzhe Chen, Zhang, Hanwen +5 · 1 citation
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #IoT Networks and Protocols #Signal Processing (eess.SP) #Telecommunications and Broadcasting Technologies #Wireless Body Area Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.01515
openalex publication_date 2024/05/02 · openalex created_date 2024/05/05 · openalex updated_date 2026/07/28
Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require complicated iterative algorithms, which cannot meet the stringent latency requirement by users with limited resources. Recently, data-driven methods are explored to alleviate this issue. However, they suffer from poor generalizability and scarce training data to achieve satisfactory performance. In this paper, we propose a fractional programming (FP) based deep unfolding (DU) approach to address resource allocation problem for a weighted sum rate optimization in RSMA. By carefully designing the penalty function, we couple the variable update with projected gradient descent algorithm (PGD). Following the structure of PGD, we embed a few learnable parameters in each layer of the DU network. Through extensive simulation, we have shown that the proposed model-based neural networks can yield similar results compared to the traditional optimization algorithm for RSMA resource management but with much lower computational complexity, less training data, and higher resilience to out-of-distribution (OOD) data.