Multi-access edge computing (MEC) can enhance the computing capability of mobile devices, while non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two strategies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task delay minimization in multi-user NOMA-MEC networks, where multiple users can offload their tasks simultaneously through the same frequency band. We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts. We aim to minimize the task delay among users by optimizing their tasks partition ratios and offloading transmit power. The delay minimization problem is first formulated, and it is shown that it is a nonconvex one. By carefully investigating its structure, we transform the original problem into an equivalent quasi-convex. In this way, a bisection search iterative algorithm is proposed in order to achieve the minimum task delay. To reduce the complexity of the proposed algorithm and evaluate its optimality, we further derive closed-form expressions for the optimal task partition ratio and offloading power for the case of two-user NOMA-MEC networks. Simulations demonstrate the convergence and optimality of the proposed algorithm and the effectiveness of the closed-form analysis.

中文翻译：

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NOMA-MEC 网络中延迟最小化的最佳资源分配

多址边缘计算（MEC）可以增强移动设备的计算能力，而非正交多址（NOMA）可以提供高数据速率。结合这两种策略可以有效地使网络受益于频谱和能源效率。在本文中，我们研究了多用户 NOMA-MEC 网络中的任务延迟最小化，其中多个用户可以通过相同的频段同时卸载他们的任务。我们采用部分卸载策略，其中每个用户可以将其计算任务划分为卸载和本地计算部分。我们的目标是通过优化用户的任务分配比和卸载传输功率来最小化用户之间的任务延迟。首先制定延迟最小化问题，并证明它是一个非凸问题。通过仔细研究其结构，我们将原始问题转化为等价的拟凸问题。通过这种方式，提出了一种二分搜索迭代算法，以实现最小的任务延迟。为了降低所提出算法的复杂性并评估其最优性，我们进一步推导出了最优任务分配比和两用户 NOMA-MEC 网络情况下的卸载能力的闭式表达式。仿真证明了所提出算法的收敛性和最优性以及封闭形式分析的有效性。我们进一步推导出最优任务分配比和卸载能力的闭式表达式，适用于两用户 NOMA-MEC 网络的情况。仿真证明了所提出算法的收敛性和最优性以及封闭形式分析的有效性。我们进一步推导出最优任务分配比和卸载能力的闭式表达式，适用于两用户 NOMA-MEC 网络的情况。仿真证明了所提出算法的收敛性和最优性以及封闭形式分析的有效性。