  Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA)
have been regarded as promising technologies to improve computation capability
and offloading efficiency of the mobile devices in the sixth generation (6G)
mobile system. This paper mainly focuses on the hybrid NOMA-MEC system, where
multiple users are first grouped into pairs, and users in each pair offload
their tasks simultaneously by NOMA, and then a dedicated time duration is
scheduled to the more delay-tolerable user for uploading the remaining data by
orthogonal multiple access (OMA). For the conventional NOMA uplink
transmission, successive interference cancellation (SIC) is applied to decode
the superposed signals successively according to the channel state information
(CSI) or the quality of service (QoS) requirement. In this work, we integrate
the hybrid SIC scheme which dynamically adapts the SIC decoding order among all
NOMA groups. To solve the user grouping problem, a deep reinforcement learning
(DRL) based algorithm is proposed to obtain a close-to-optimal user grouping
policy. Moreover, we optimally minimize the offloading energy consumption by
obtaining the closed-form solution to the resource allocation problem.
Simulation results show that the proposed algorithm converges fast, and the
NOMA-MEC scheme outperforms the existing orthogonal multiple access (OMA)
scheme.
