Abstract
Rate split multiple access (RSMA) has been proven as an effective
communication scheme for 5G and beyond, especially in vehicular scenarios.
However, RSMA requires complicated iterative algorithms for proper resource
allocation, which cannot fulfill the stringent latency requirement in resource
constrained vehicles. Although data driven approaches can alleviate this issue,
they suffer from poor generalizability and scarce training data. 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 few learnable parameters in each layer of the DU network.
Through extensive simulation, we have shown that the proposed model-based
neural networks has similar performance as optimal results given by traditional
algorithm but with much lower computational complexity, less training data, and
higher resilience to test set data and out-of-distribution (OOD) data.