Zhou Tianqing, Xu Ming, Qin Dong, Nie Xuefang, Li Xuan, Li Chunguo
School of Information Engineering, East China Jiaotong University, Nanchang 330013, China.
School of Information Engineering, Nanchang University, Nanchang 330031, China.
Sensors (Basel). 2023 Nov 9;23(22):9064. doi: 10.3390/s23229064.
In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.
在本文中，为了降低数据传输的能耗和时间，在移动边缘计算（MEC）网络中联合考虑了非正交多址接入（NOMA）和移动边缘缓存技术。对于缓存辅助的车载NOMA-MEC网络，在时间和资源约束下，提出了一个使车辆（移动设备，MD）能耗最小化的问题，该问题联合优化计算资源分配、子信道选择、设备关联、卸载和缓存决策。为了解决所提出的问题，我们基于双延迟深度确定性策略梯度（TD3）算法开发了一种有效的联合计算卸载和任务缓存算法。这种基于TD3的卸载（TD3O）算法包括一个设计的动作变换（AT）算法，用于将连续动作空间转换为离散动作空间。此外，为了以非迭代方式解决所提出的问题，还设计了一种有效的启发式算法（HA）。对于所设计的算法，我们对计算复杂度和收敛性进行了一些详细分析，并通过仿真给出了一些有意义的见解。仿真结果表明，TD3O算法比几种基准算法能实现更低的本地能耗，并且HA比完全卸载算法和本地执行算法能实现更低的能耗。
