In this article, we establish a novel framework for a multi-user mobile edge computing (MEC) network in which a set of users with high downlink rate demands and a set of users with intensive computation tasks can collaborate to achieve a mutually-beneficial scenario such that completion time of the tasks is reduced and the base station (BS) can send more information at a higher rate to the downlink users. Specifically, by leveraging non-orthogonal multiple access (NOMA) for uplink and downlink traffic, the user with the computation task can offload shares of the computation task to the edge cloud and the downlink user. At the same time, this user forwards the information it receives from the BS to the downlink user. In this set up, we jointly optimize the communication resources, computational resources at the edge cloud and user devices, pairings among the two sets of users, the shares of computation tasks, and relay bits to minimize the total task completion time while satisfying downlink users’ incentive requirements. For a network with a single computation demanding user and a single downlink user, the optimal solution to the problem is provided. For a network with multiple users, the problem is non-convex and computationally challenging. Hence, we propose an efficient, low complexity algorithm that utilizes the bottleneck matching algorithm, convex optimization, and the block coordinate descent scheme to obtain a locally-optimal solution. Simulation results demonstrate that, as compared with the state-of-the-art, the total task completion time is greatly reduced (32%–51%), and a large computational energy savings at the edge cloud (38%–55%) is achieved. Simultaneously, the downlink users’ rates improve compared to the orthogonal transmission.

中文翻译：

---

多用户 MEC 系统的支持 NOMA 的计算和通信资源交易

在本文中，我们为多用户移动边缘计算（MEC）网络建立了一个新颖的框架，其中一组具有高下行速率需求的用户和一组具有密集计算任务的用户可以协作以实现互惠互利的场景这样可以减少任务的完成时间，并且基站（BS）可以以更高的速率向下行链路用户发送更多信息。具体来说，通过对上行链路和下行链路流量利用非正交多址 (NOMA)，具有计算任务的用户可以将计算任务的份额卸载到边缘云和下行链路用户。同时，该用户将从BS接收到的信息转发给下行用户。在这个设置中，我们共同优化了边缘云和用户设备的通信资源、计算资源，两组用户之间的配对、计算任务的份额和中继比特，以最小化总任务完成时间，同时满足下行用户的激励需求。对于单个计算需求用户和单个下行用户的网络，提供了该问题的最优解。对于具有多个用户的网络，问题是非凸的并且在计算上具有挑战性。因此，我们提出了一种高效、低复杂度的算法，该算法利用瓶颈匹配算法、凸优化和块坐标下降方案来获得局部最优解。仿真结果表明，与最先进的技术相比，总任务完成时间大大减少（32%–51%），并且在边缘云处节省了大量计算能源（38%–55%）已完成。同时，