标题： 通过传输多样性和抗干扰调度的MEC可靠任务卸载 显示英文标题

标题： Reliable Task Offloading in MEC through Transmission Diversity and Jamming-Aware Scheduling

摘要： 移动边缘计算（MEC）通过将计算靠近用户进行，实现了低延迟的应用，但动态任务到达和如干扰等通信威胁使得可靠的任务卸载和资源分配变得复杂。 在本文中，我们构建了一个动态的MEC框架，考虑了传输多样性，共同解决了在存在干扰情况下的任务调度和资源块（RB）分配问题。 首先，我们在考虑边缘服务器对之前卸载任务的现有承诺的情况下，定义并评估关键网络指标——包括丢弃任务比率和带宽利用率，以保持服务连续性。 然后，我们提出了一种考虑干扰的卸载和RB分配框架，利用传输多样性和跨分布式gNB的最优调度。 所提出的解决方案与没有传输多样性的类似场景以及两种基线策略——先到先得（FCFS）和最短任务优先（STF）进行了比较。 所提出的算法有效缓解了干扰的影响，同时提高了资源利用率并最小化了任务丢弃率，使其非常适合关键任务的MEC应用。 在信干噪比（SJNR）为4 dB时，所提出的方法实现了$0.26$的任务丢弃率，优于没有传输多样性的场景（任务丢弃率为0.50），以及STF和FCFS策略（任务丢弃率分别为0.52和0.63）。 显示英文摘要

摘要： Mobile Edge Computing (MEC) enables low-latency applications by bringing computation closer to the user, but dynamic task arrivals and communication threats like jamming complicate reliable task offloading and resource allocation. In this paper, we formulate a dynamic MEC framework considering the transmission diversity that jointly addresses task scheduling and resource block (RB) assignment in the presence of jamming. First, we define and evaluate key network metrics-including dropped task ratio and bandwidth utilization-while maintaining service continuity by accounting for the existing commitments of the edge server to previously offloaded tasks. Then, we propose a jamming-aware offloading and RB allocation framework that leverages transmission diversity and optimal scheduling across distributed gNBs. The proposed solution is compared to a similar scenario without transmission diversity and two baseline strategies of first-come-first-served (FCFS) and shortest task first (STF). The proposed algorithm effectively mitigates the impact of jamming while enhancing resource utilization and minimizing task drop rates, making it highly suitable for mission-critical MEC applications. At signal-to-jamming-and-noise ratio (SJNR) of 4 dB, the proposed method achieves a $0.26$ task drop rate, outperforming the scenario without transmission diversity with a task drop rate of 0.50 and STF and FCFS strategies with 0.52 and 0.63 task drop rates, respectively.