Satellite networks are emerging as core infrastructure for sixth-generation (6G) wireless systems, yet they face stringent constraints on bandwidth, onboard energy, processing capability, and link availability that bit-oriented communication cannot resolve. Semantic communication, which extracts and transmits task-relevant meaning rather than raw bits, offers a principled remedy across the satellite stack. This survey provides a layered overview of satellite semantic communication, covering: (i) the physical layer with channel-aware joint source-channel coding (JSCC) under long propagation delay, severe Doppler, and time-varying signal-to-noise ratio (SNR); (ii) onboard semantic processing with lightweight encoders, model compression, and in-orbit edge inference for radiation-hardened payloads; (iii) inter-satellite link (ISL) and network-layer routing, distributed federated learning across constellations, and 3rd Generation Partnership Project (3GPP) non-terrestrial network (NTN) integration; and (iv) representative applications spanning Earth observation (EO), satellite Internet of Things (IoT), direct-to-device (D2D), and deep-space scenarios. We synthesize quantitative gains across orbital regimes, identify open challenges in security, standardization, and heterogeneous-orbit interoperability, and outline directions including foundation-model compression, neuromorphic onboard computing, and carbon-aware orchestration for sustainable space connectivity.
Tung Son Do, Thanh Phung Truong, The-Vi Nguyen et al.· International Conference on...· 0 citations
Unmanned aerial vehicles (UAVs) are emerging as mobile edge nodes for temporary coverage, aerial sensing, disaster response, public-safety monitoring, intelligent transportation, and smart-agriculture services. Although federated learning (FL) enables distributed model training without transferring raw data, conventional flat FL is not well aligned with UAV-enabled edge systems because ground clients may experience mobility-dependent availability, unstable wireless links, and costly longrange synchronization with a remote coordinator. Hierarchical federated learning (HFL) mitigates these limitations by introducing UAV edge aggregators between ground clients and the global coordinator. This article provides a simulation-based assessment of a secure HFL-UAV architecture implemented in Python. The simulator captures non-independent and identically distributed (non-IID) client data, mobility-aware client-UAV association, two-level model aggregation, malicious client behavior, trust-weighted robust aggregation, optional differential privacy, secure-aggregation overhead, communication cost, latency, and UAV energy consumption. Under a model-replacement attack, the secure HFL-UAV scheme maintains the learning performance of the considered synthetic classification task while substantially reducing the aggregation trust assigned to malicious clients. The results also expose important design tradeoffs: security mechanisms increase local communication overhead, UAV-assisted aggregation introduces energy cost, and trust filtering must be calibrated carefully under non-IID data. The proposed simulation framework therefore provides a reproducible basis for evaluating secure aerial edge learning systems.
Ton That Tam Dinh, Manh Cuong Ho, Ayalneh Bitew Wondmagegn et al.· International Conference on...· 0 citations