SemSAN: Semantic Satellite Access Network Slicing for NextG Non-Terrestrial Networks

Satellites equipped with computing capabilities serve as invaluable access platforms for 5G and beyond (NextG) non-terrestrial networks (NTNs). They facilitate the continuous execution of resource-intensive edge-assisted deep learning (DL) tasks that are offloaded from Internet-of-Things (IoT) user equipment (UEs) in remote areas. To this end, satellite access network (SAN) resources need to be carefully 'sliced', consid-ering both the constrained energy availability and the scarcity of SAN resources. Existing SAN slicing approaches tend to treat offloaded tasks conventionally, overlooking the intricate semantics associated with DL tasks. In this paper, we propose semantic SAN (SemSAN), the first semantic SAN slicing algorithm for NextG AI-native NTNs. Our keen observations reveal that various DL tasks (i) can tolerate different degrees of image compression, and (ii) may yield equivalent model accuracy when employing DNN models with different sizes. These observations inspire us to further exploit the computation capability of a SAN to support more tasks while still minimizing overall energy consumption. After analyzing the characteristics of this optimization problem, we propose an online greedy SemSAN slicing algorithm to approximate its optimal solution. Extensive experiments verify the effectiveness of SemSAN in energy saving and its ability to support a substantial number of tasks, compared with other baselines.