Joint Scheduling-Offloading policies in NOMA-based Mobile Edge Computing Systems.

We consider a Non Orthogonal Multiple Access (NOMA)-based wireless network where User Equipments (UEs) are connected to a Base Station (BS) equipped with a Mobile Edge Computing (MEC) server. The UEs can process their buffered data packets with strict delay either locally or by offloading them to the base station's MEC server. In order to minimize the dropped packets due to buffer overflow or delay violation, the scheduling-offloading problem is formulated as a Markov Decision Process (MDP) and solved using various optimal and Reinforcement Learning (RL) algorithms. The output of each policy is, for each user, the number of packets to be processed and the type of processing (locally or remotely). The decisions rely on the channel state information and the buffers states. The numerical results show the great advantage of using NOMA compared to Orthogonal Multiple Access (OMA). We further analyze the scalability capabilities of the used algorithms, which validates the benefits of using Deep Reinforcement Learning (DRL) techniques.