  Rate splitting multiple access (RSMA) relies on beamforming design for
attaining spectral efficiency and energy efficiency gains over traditional
multiple access schemes. While conventional optimization approaches such as
weighted minimum mean square error (WMMSE) achieve suboptimal solutions for
RSMA beamforming optimization, they are computationally demanding. A novel
approach based on fractional programming (FP) has unveiled the optimal
beamforming structure (OBS) for RSMA. This method, combined with a hyperplane
fixed point iteration (HFPI) approach, named FP-HFPI, provides suboptimal
beamforming solutions with identical sum rate performance but much lower
computational complexity compared to WMMSE. Inspired by such an approach, in
this work, a novel deep unfolding framework based on FP-HFPI, named
rate-splitting-beamforming neural network (RS-BNN), is proposed to unfold the
FP-HFPI algorithm. Numerical results indicate that the proposed RS-BNN attains
a level of performance closely matching that of WMMSE and FP-HFPI, while
dramatically reducing the computational complexity.
