Efficient Offloading for Minimizing Task Computation Delay of NOMA-Based Multiaccess Edge Computing

• Published in: IEEE transactions on communications 2022-05, Vol.70 (5), p.3186-3203
• Main Authors: Zhu, Bincheng, Chi, Kaikai, Liu, Jiajia, Yu, Keping, Mumtaz, Shahid
• Format: Article
• Language: English
• Subjects: Algorithms; computation delay minimization; Deep learning; deep reinforcement learning; Delay; Delays; Edge computing; Energy consumption; Frequency division multiple access; Machine learning; Mobile computing; Multi-access edge computing; NOMA; Nonorthogonal multiple access; Optimization; Resource management; Servers; Task analysis; Wireless communication; Workload; Workloads
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2022.3162263

Multi-access edge computing (MEC) has been one promising solution to reduce the computation delay of wireless devices. Due to the high spectrum efficiency of non-orthogonal multiple access (NOMA), this paper studies the single-user multi-edge-server MEC system based on downlink NOMA, aiming to minimize task computation delay by jointly optimizing the NOMA-based transmission duration (TD) and workload offloading allocation (WOA) among edge computing servers. This task computation delay minimization (CDM) problem is formulated as a nonconvex optimization problem. To solve the CDM problem efficiently, we decompose it into the sub-problem of determining the optimal WOA with a given TD and the top-problem of optimizing the TD. For the sub-problem, we first derive its some important properties and then design an efficient channel quality ranking based algorithm to obtain the optimal WOA. We solve the top-problem for the static-channel and dynamic-channel scenarios, respectively. For the static-channel scenario, we design an optimal algorithm which only apply once the golden section search method to obtain the optimal TD of first task and directly obtain the optimal offloading solution for any consequently arrived task with different workloads. For the dynamic-channel scenario where the channel qualities from the wireless device to the edge-computing servers are varying, it is critical to quickly determine the current task's offloading solution under the current channel state and task workload, which is very challenging for the traditional optimization methods. In order to conquer this challenge, we propose the deep reinforcement learning (DRL) based algorithm, which can obtain the near-optimal offloading solution instantly after enough learning. Finally, we validate through simulations the advantages of NOMA over frequency division multiple access (FDMA).