  Achieving significant performance gains both in terms of system throughput
and massive connectivity, non-orthogonal multiple access (NOMA) has been
considered as a very promising candidate for future wireless communications
technologies. It has already received serious consideration for implementation
in the fifth generation (5G) and beyond wireless communication systems. This is
mainly due to NOMA allowing more than one user to utilise one transmission
resource simultaneously at the transmitter side and successive interference
cancellation (SIC) at the receiver side. However, in order to take advantage of
the benefits, NOMA provides in an optimal manner, power allocation needs to be
considered to maximise the system throughput. This problem is non-deterministic
polynomial-time (NP)-hard which is mainly why the use of deep learning
techniques for power allocation is required. In this paper, a state-of-the-art
review on cutting-edge solutions to the power allocation optimisation problem
using deep learning is provided. It is shown that the use of deep learning
techniques to obtain effective solutions to the power allocation problem in
NOMA is paramount for the future of NOMA-based wireless communication systems.
Furthermore, several possible research directions based on the use of deep
learning in NOMA systems are presented.
