Mobile edge computing (MEC) is an important research topic in the field of wireless communication and mobile computing, as it can effectively decrease the latency and energy consumption due to the trade-off between the communication and computing, where some intensive computing tasks can be offloaded to computational access points (CAPs), especially when the wireless transmission channel is in good condition. This article studies how to intelligently allocate the computing capability and wireless bandwidth among users for a cache-aided multi-terminal multi-CAP MEC network with non-ideal channel estimation, where there are mobile terminals and CAPs in the network. Each terminal has some tasks that need to be computed in a fast and efficient way. For such a system, we first design the system by jointly considering the computing capability and wireless bandwidth allocation, where the computing and communication delay is used as the performance of metric. To optimize the system performance, we then employ deep deterministic policy gradient to learn an effective strategy on the allocation of computing capability and wireless bandwidth, in order to decrease the system delay as much as possible. Simulations are finally conducted to show the superiority of the proposed studies in this article, especially about the advantages from cache.

中文翻译：

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用于智能电网的缓存辅助多用户 UAV-MEC 网络：DDPG 方法

移动边缘计算（MEC）是无线通信和移动计算领域的一个重要研究课题，由于通信和计算之间的权衡，它可以有效降低延迟和能耗，可以将一些密集的计算任务进行处理。卸载到计算接入点 (CAP)，尤其是当无线传输信道状况良好时。本文研究了如何在非理想信道估计的缓存辅助多终端多CAP MEC网络中在用户之间智能分配计算能力和无线带宽，其中移动终端和网络中的 CAP。每个终端都有一些需要快速有效地计算的任务。对于这样的系统，我们首先综合考虑计算能力和无线带宽分配来设计系统，其中计算和通信延迟作为性能指标。为了优化系统性能，我们采用深度确定性策略梯度来学习计算能力和无线带宽分配的有效策略，以尽可能减少系统延迟。最后进行模拟以显示本文所提出的研究的优越性，特别是缓存的优势。