Energy-Efficient Task Offloading for Semantic-Aware Networks

The limited computation capacity of user equipments restricts the local implementation of computation-intense applications. Edge computing, especially the edge intelligence system enables local users to offload the computation tasks to the edge servers for reducing the computational energy consumption of user equipments and fast task execution. However, the limited bandwidth of upstream channels may increase the task transmission latency and affect the computation offloading performance. To overcome the challenge of the limited resource of wireless communications, we adopt a semantic-aware task offloading system, where the semantic information of tasks are extracted and offloaded to the edge servers. Furthermore, a proximal policy optimization based multi-agent reinforcement learning algorithm (MAPPO) is proposed to coordinate the resource of wireless communications and the computation, so that the resource management can be performed distributedly and the computational complexity of the online algorithm can be reduced.