Mobile Edge Computing (MEC) is a modern paradigm that involves moving computing and storage resources closer to the network edge, reducing latency, and enabling innovative, delay-sensitive applications. Within MEC, computation offloading refers to the process of transferring computationally intensive tasks or processes from mobile devices to edge servers, optimizing the performance of mobile applications. Traditional numerical optimization methods for computation offloading often necessitate numerous iterations to attain optimal solutions. In this paper, we provide a tutorial on how Deep Neural Networks (DNNs) resolve the challenges of computation offloading. The article explores various applications of DNNs in computation offloading, encompassing channel estimation, caching, AR and VR applications, resource allocation, mode selection, unmanned aerial vehicles (UAVs), and vehicle management. We present a comprehensive taxonomy that categorizes these applications, and offer an overview of existing schemes, comparing their effectiveness. Additionally, we outline the open research issues that can be addressed through the application of DNNs in MEC offloading. We also highlight specific challenges related to DNN utilization in computation offloading. In conclusion, we affirm that DNNs are widely acknowledged as invaluable tools for optimizing computation offloading in MEC.

中文翻译：

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深度神经网络满足移动边缘网络中的计算卸载：应用程序、分类法和开放问题

移动边缘计算 (MEC) 是一种现代范例，涉及将计算和存储资源移近网络边缘、减少延迟并支持创新的延迟敏感型应用程序。在 MEC 中，计算卸载是指将计算密集型任务或流程从移动设备转移到边缘服务器，从而优化移动应用程序性能的过程。用于计算卸载的传统数值优化方法通常需要多次迭代才能获得最佳解决方案。在本文中，我们提供了有关深度神经网络 (DNN) 如何解决计算卸载挑战的教程。本文探讨了 DNN 在计算卸载方面的各种应用，包括信道估计、缓存、AR 和 VR 应用、资源分配、模式选择、无人机 (UAV) 和车辆管理。我们提出了一个全面的分类法，对这些应用程序进行分类，并概述现有方案，比较它们的有效性。此外，我们概述了可以通过在 MEC 卸载中应用 DNN 来解决的开放研究问题。我们还强调了与计算卸载中 DNN 使用相关的具体挑战。总之，我们确认 DNN 被广泛认为是优化 MEC 中计算卸载的宝贵工具。