The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) can significantly improve the spectrum efficiency beyond the fifth-generation network. In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions. To minimize the energy consumption, we consider jointly optimizing the task assignment, power allocation and user association. As the main contribution, with imperfect CSI, the optimal closed-form expressions of task assignment and power allocation are analytically derived for the two-BS case. Specifically, the original formulated problem is nonconvex. We first transform the probabilistic problem into a non-probabilistic one. Subsequently, a bilevel programming method is proposed to derive the optimal solution. In addition, by incorporating the matching algorithm with the optimal task and power allocation, we propose a low complexity algorithm to efficiently optimize user association for the multi-user and multi-BS case. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme but also the identical results with lower complexity from the exhaustive search with the small number of BSs.

中文翻译：

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具有不完善 CSI 的 NOMA 启用 MEC 网络的节能资源分配

非正交多址（NOMA）和移动边缘计算（MEC）的结合可以显着提高频谱效率，超越第五代网络。在本文中，我们主要关注具有不完善信道状态信息（CSI）的多用户、多基站 NOMA 辅助 MEC 网络的节能资源分配，其中每个用户可以将其任务上传到多个基站（BS）用于远程执行。为了最小化能源消耗，我们考虑联合优化任务分配、功率分配和用户关联。作为主要贡献，在不完善的 CSI 情况下，针对两个 BS 的情况，解析导出了任务分配和功率分配的最优封闭形式表达式。具体来说，最初制定的问题是非凸的。我们首先将概率问题转化为非概率问题。随后，提出了一种双层规划方法来推导出最优解。此外，通过将匹配算法与最佳任务和功率分配相结合，我们提出了一种低复杂度的算法，以有效地优化多用户和多基站情况下的用户关联。仿真表明，所提出的算法可以产生比传统 OMA 方案更好的性能，但也可以从具有少量 BS 的穷举搜索中获得相同的结果，但复杂度较低。我们提出了一种低复杂度的算法来有效地优化多用户和多基站情况下的用户关联。仿真表明，所提出的算法可以产生比传统的 OMA 方案更好的性能，而且在用少量 BS 进行穷举搜索时也能得到相同的结果，但复杂度较低。我们提出了一种低复杂度的算法来有效地优化多用户和多基站情况下的用户关联。仿真表明，所提出的算法可以产生比传统 OMA 方案更好的性能，但也可以从具有少量 BS 的穷举搜索中获得相同的结果，但复杂度较低。