Abstract
Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was 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 integrated 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 was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme.
Highlights
With fifth-generation (5G) networks being available the sixth-generation (6G)wireless network is currently under research, which is expected to provide superior performance to satisfy the growing demands of mobile equipment, such as latency-sensitive, energy-hungry, and computationally intensive services and applications [1,2]
We assumed that the resource allocation of Um,φ is given as a constant in each group since Um,φ is treated as the primary user whose requirements need to be guaranteed in priority, and we only focused on the energy minimization for Un,φ during both non-orthogonal multiple access (NOMA)
The optimization of user grouping is modeled as a deep reinforcement learning (DRL) task, where the base station is treated as the agent to interact with the environment, which is defined as the multi-access edge computing (MEC) network
Summary
With fifth-generation (5G) networks being available the sixth-generation (6G). wireless network is currently under research, which is expected to provide superior performance to satisfy the growing demands of mobile equipment, such as latency-sensitive, energy-hungry, and computationally intensive services and applications [1,2]. The Internet of Things (IoT) networks are being developed rapidly, where massive numbers of nodes are supposed to be connected together, and IoT nodes can communicate with each other, and process acquired data [3,4,5]. Such IoT and many other terminal devices are constrained by the battery life and computational capability, and thereby, these devices cannot fully support computationally intensive tasks. Integrating NOMA with MEC can potentially improve the service quality of MEC including low transmission latency and massive connections compared to the conventional orthogonal multiple access (OMA)
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