With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is proposed to solve the unbalanced network load, in which a seamless handover algorithm for wireless access points is used to calculate the access point with the highest weight, and a software-defined network controller controls the switching process. A joint allocation method of communication and computing resources based on deep reinforcement learning is proposed to minimize the terminal energy consumption and the system delay. A resource allocation model is based on minimizing terminal energy consumption, and system delay is built. The optimal value of task offloading decision and resource allocation vector are calculated with deep reinforcement learning. Experimental results show that the proposed method can reduce the network load and the task execution cost.

由于接入设备密集部署在多接入边缘计算环境中，用户在移动时频繁切换接入设备，导致网络负载不平衡和服务质量下降。针对上述问题，提出了一种基于感知的无线接入点无缝切换方案。首先，提出一种基于负载感知的无缝切换模型来解决网络负载不均衡的问题，采用无线接入点无缝切换算法计算权重最高的接入点，由软件定义的网络控制器控制。切换过程。提出了一种基于深度强化学习的通信和计算资源联合分配方法，以最小化终端能耗和系统延迟。资源分配模型基于终端能耗最小化，建立系统时延。通过深度强化学习计算任务卸载决策和资源分配向量的最优值。实验结果表明，所提出的方法可以降低网络负载和任务执行成本。