Mobile Augmented Reality (MAR) applications are fast becoming popular with the growth in use of smartphones and smart wearable devices. Apart from gaming, MAR finds useful application in any field for attractive visualization of the environment. The computer vision algorithms used in MAR applications are both data and computation extensive which renders them difficult to use in delay sensitive applications, given the present network scenario. But the network standard 6G expected to be deployed around 2030 is supposed to operate at a GHz to THz frequency. This will increase the bandwidth of the network in manifolds and can support the seamless real time transfer of the multimedia data. The article proposes to divide the various phases of an MAR application into sequential and parallel tasks and attempts to offload the task to the nearby devices with the help of Deep Reinforcement Algorithm (DRL) depending on transmission, task and energy constraints.

随着智能手机和智能可穿戴设备使用的增长，移动增强现实 (MAR) 应用程序正迅速流行起来。除了游戏之外，MAR 在任何领域都可以找到有用的应用，以实现对环境的有吸引力的可视化。考虑到当前的网络场景，MAR 应用中使用的计算机视觉算法数据和计算量都很大，这使得它们难以在延迟敏感的应用中使用。但预计将在 2030 年左右部署的网络标准 6G 应该在 GHz 至 THz 频率下运行。这将多方面增加网络的带宽，并可以支持多媒体数据的无缝实时传输。