To cope with the unprecedented surge in demand for data computing for the applications, the promising concept of multiaccess edge computing (MEC) has been proposed to enable the network edges to provide closer data processing for mobile devices (MDs). Since enormous workloads need to be migrated, and MDs always remain resource-constrained, data offloading from devices to the MEC server will inevitably require more efficient transmission designs. The integration of nonorthogonal multiple access (NOMA) technique with MEC has been shown to provide applications with lower latency and higher energy efficiency. However, the existing designs of this type have mainly focused on the transmission technique, which is still insufficient. To further advance offloading performance, in this work, we propose an application-driven NOMA-enabled computation offloading scheme by exploring the characteristics of applications, where the common data of the application is offloaded through multidevice cooperation. Under the premise of successfully offloading the common data, we formulate the problem as the maximization of individual offloading throughput, where the time allocation and power control are jointly optimized. By using the successive convex approximation (SCA) method, the formulated problem can be iteratively solved. Simulation results demonstrate the convergence of our method and the effectiveness of the proposed scheme.

中文翻译：

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应用驱动的非正交多路访问计算卸载方案


为了应对应用程序对数据计算的需求空前激增，多路访问边缘计算（MEC）的概念被提出，使网络边缘能够为移动设备（MD）提供更紧密的数据处理。由于需要迁移大量工作负载，而MD始终受到资源限制，因此将数据从设备卸载到MEC服务器将不可避免地需要更高效的传输设计。非正交多址 (NOMA) 技术与 MEC 的集成已被证明可以为应用程序提供更低的延迟和更高的能源效率。但现有此类设计主要集中在传输技术上，尚有不足。为了进一步提高卸载性能，在这项工作中，我们通过探索应用程序的特性，提出了一种应用程序驱动的支持 NOMA 的计算卸载方案，其中通过多设备协作卸载应用程序的公共数据。在成功卸载公共数据的前提下，我们将问题表述为个体卸载吞吐量的最大化，其中时间分配和功率控制被联合优化。通过使用逐次凸逼近（SCA）方法，可以迭代地求解公式化的问题。仿真结果证明了我们方法的收敛性和所提出方案的有效性。