  In this paper, a deep neural network (DNN)-based detector for an uplink
single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA)
system is proposed, where SC-IM-NOMA allows users to use the same set of
subcarriers for transmitting their data modulated by the sub-carrier index
modulation technique. More particularly, users of SC-IMNOMA simultaneously
transmit their SC-IM data at different power levels which are then exploited by
their receivers to perform successive interference cancellation (SIC)
multi-user detection. The existing detectors designed for SC-IM-NOMA, such as
the joint maximum-likelihood (JML) detector and the maximum likelihood
SIC-based (ML-SIC) detector, suffer from high computational complexity. To
address this issue, we propose a DNN-based detector whose structure relies on
the model-based SIC for jointly detecting both M-ary symbols and index bits of
all users after trained with sufficient simulated data. The simulation results
demonstrate that the proposed DNN-based detector attains near-optimal error
performance and significantly reduced runtime complexity in comparison with the
existing hand-crafted detectors.
