Abstract
Next generation communications demand for better spectrum management, lower
latency, and guaranteed quality-of-service (QoS). Recently, Artificial
intelligence (AI) has been widely introduced to advance these aspects in next
generation wireless systems. However, such AI applications suffer from limited
training data, low robustness, and poor generalization capabilities. To address
these issues, a model-driven deep unfolding (DU) algorithm is introduced in
this paper to bridge the gap between traditional model-driven communication
algorithms and data-driven deep learning. Focusing on the QoS-aware
rate-splitting multiple access (RSMA) resource allocation problem in multi-user
communications, a conventional fractional programming (FP) algorithm is first
applied as a benchmark. The solution is then refined by the application of
projection gradient descent (PGD). DU is employed to further speed up
convergence procedure, hence improving the efficiency of PGD. Moreover, the
feasibility of results is guaranteed by designing a low-complexity projection
based on scale factors, plus adding violation control mechanisms into the loss
function that minimizes error rates. Finally, we provide a detailed analysis of
the computational complexity and analysis design of the proposed DU algorithm.
Extensive simulations are conducted and the results demonstrate that the
proposed DU algorithm can reach the optimal communication efficiency with a
mere $0.024\%$ violation rate for 4 layers DU. The DU algorithm also exhibits
robustness in out-of-distribution tests and can be effectively trained with as
few as 50 samples.