The burgeoning and ubiquitous deployment of the Internet of Things (IoT) landscape struggles with ultra-low latency demands for computation-intensive tasks in massive connectivity scenarios. In this paper, we propose an innovative uplink non-orthogonal multiple access (NOMA)-assisted multi-base station (BS) mobile edge computing (BS-MEC) network tailored for massive IoT connectivity. To fulfill the quality-of-service (QoS) requirements of delay-sensitive and computation-intensive IoT applications, we formulate a joint task offloading, user grouping, and power allocation optimization problem with the overarching objective of minimizing the system's total delay, aiming to address issues of unbalanced subchannel access, inter-group interference, computational load disparities, and device heterogeneity. To effectively tackle this problem, we first reformulate task offloading and user grouping into a non-cooperative game model and propose an exact potential game-based joint decision-making (EPG-JDM) algorithm, which dynamically selects optimal task offloading and subchannel access decisions for each IoT device based on its channel conditions, thereby achieving the Nash Equilibrium. Then, we propose a majorization-minimization (MM)-based power allocation algorithm, which transforms the original subproblem into a tractable convex optimization paradigm. Extensive simulation experiments demonstrate that our proposed EPG-JDM algorithm significantly outperforms state-of-the-art decision-making algorithms and classic heuristic algorithms, yielding performance improvements of up to 19.3% and 14.7% in terms of total delay and power consumption, respectively.

翻译：物联网(IoT)的大规模普及部署在巨量连接场景下面临着计算密集型任务对超低时延的严苛需求。本文提出一种创新的上行链路非正交多址接入(NOMA)辅助多基站移动边缘计算(BS-MEC)网络架构，专为海量物联网连接场景设计。为满足时延敏感与计算密集型物联网应用的服务质量(QoS)要求，我们构建了联合任务卸载、用户分组与功率分配的优化问题，其核心目标在于最小化系统总时延，旨在解决子信道访问不均衡、组间干扰、计算负载差异及设备异构性等问题。为有效求解该问题，我们首先将任务卸载与用户分组重构为非合作博弈模型，并提出基于精确势博弈的联合决策(EPG-JDM)算法，该算法能根据各物联网设备的信道状态动态选择最优任务卸载与子信道接入策略，从而实现纳什均衡。随后，我们提出基于主最小化(MM)的功率分配算法，将原子问题转化为可处理的凸优化范式。大量仿真实验表明，所提出的EPG-JDM算法在系统总时延与功耗方面分别取得高达19.3%与14.7%的性能提升，显著优于现有先进决策算法与经典启发式算法。