Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 27-32· 0 citations· 21 references
Abstract
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.
The proposed Digital Twin Satellite Network (DTSN) framework connects the physical satellite network with a synchronized virtual twin and combines real-time telemetry, Integrated Sensing and Communication (ISAC), predictive intelligence, and resilience-oriented control and successfully isolates compromised nodes and triggers proactive network reconfiguration.
The development of smart transportation systems and the introduction of 6G wireless communication technologies have significantly changed vehicle network topologies. Future connected autonomous vehicle (CAV) networks require bandwidth-efficient, reliable, and low-latency communication for safety-critical applications such as traffic sign recognition and decision-making. Conventional communication systems transmit raw data regardless of task relevance, which is inefficient in resource-constrained satellite channels where uplink bandwidth is scarce and propagation losses are large. Semantic communication addresses this limitation by transmitting task-relevant information instead of full signal representations. It extracts and conveys essential semantic features and leverages deep learning to optimize task performance at the receiver. Therefore, we present a Variational Autoencoder (VAE)-based multi-task semantic communication framework for satellite-assisted autonomous driving. Unlike deterministic autoencoder-based methods, the proposed model uses probabilistic latent representations for more robust and efficient encoding. The learned features are transmitted over noisy wireless channels to perform traffic sign reconstruction and classification. The framework is trained end-to-end to jointly optimize both tasks. Results show that the proposed approach achieves significant bandwidth reduction of up to 87.23\% to 98.17\% while maintaining stable performance across varying signal-to-noise ratio conditions.
S. M. Abtahiul Alam, Niloy Das, Apurba Adhikary et al.· arXiv.org· 0 citations
Deep Space Communication (DSC) is a critical enabler for reliable data exchange between Earth-based infrastructure and spacecraft operating beyond lunar orbit. This paper presents a comprehensive and up-to-date survey of DSC systems, encompassing architectural foundations, enabling technologies, and emerging research challenges. In particular, the structure and operation of Deep Space Communication Networks (DSCNs) are examined, highlighting the functional interactions among deep space stations, communication complexes, signal processing centers, and mission control centers under severe propagation delays and intermittent connectivity. The paper provides a systematic review of traditional Radio Frequency (RF) communication and emerging Free-Space Optical (FSO) technologies, including hybrid RF/FSO architectures, and analyzes their trade-offs in terms of robustness, bandwidth efficiency, power consumption, and operational complexity. Recent mission demonstrations and international technology roadmaps are discussed to illustrate the ongoing transition toward high-capacity optical links for future lunar, Martian, and deep-space missions. Furthermore, advances at the physical and link layers are surveyed, covering modulation techniques, forward error correction schemes, and adaptive link optimization, with particular emphasis on Low-Density Parity-Check (LDPC) codes, Polar codes, hybrid forward error correction (FEC) schemes, and Adaptive Coding and Modulation (ACM) for operation under low signal-to-noise ratios and time-varying channels. At the networking layer, the paper reviews Consultative Committee for Space Data Systems (CCSDS) standards and Delay/Disruption-Tolerant Networking (DTN) protocols, identifying key open research challenges related to scalability, routing, buffering, quality-of-service support, and autonomous operation. By integrating physical-layer technologies, networking protocols, and system-level considerations, this work outlines emerging trends, including AI-native communication architectures, that are expected to shape the design of scalable, autonomous, and interoperable interplanetary communication networks.
Maryam Alshehhi, Doaa Mahmoud, Sara N. Ahmad et al.· IEEE Open Journal of the Com...· 0 citations
A versatile DS2D system that supports cooperative task offloading and non-cooperative access monitoring, and Transformer-based models to enable blind signal detection and automatic modulation classification (AMC) are proposed.
Sai Huang, Wanli Ni, Ke Lv et al.· IEEE Vehicular Technology Ma...· 0 citations
Current digital semantic communication systems have primarily focused on maintaining compatibility with conventional constellation-based modulation. In contrast, index modulation (IM) represents a more spectrally and energy-efficient alternative by exploiting additional dimensions for information conveyance. Recognizing this potential, this paper bridges the gap between IM and semantic communications by proposing a novel spatial semantic communication (SSC) system leveraging cutting-edge fluid antenna-IM (FA-IM) technology. Compatible with existing joint source-channel coding (JSCC) architectures, the proposed SSC system employs the residual quantization (RQ) approach to discretize analog semantic features for subsequent digital IM transmission. Notably, the proposed SSC system synergizes RQ and IM via a semantic-aware stream splitting scheme, which ensures that critical semantic information undergoes less severe channel fading, thereby further optimizing semantic transmission performance. Simulation results validate that the proposed SSC system effectively integrates the high fidelity of RQ, the reliability of semantic-aware splitting, and the spatial efficiency of FA-IM, thereby providing a robust solution for future digital semantic transmission.
Xinghao Guo, Yin Xu, Dazhi He et al.· IEEE Transactions on Communi...· 1 citation
Semantic communication (SemCom) and task-oriented communication (TOC) can reduce wireless resource consumption by focusing on transmitting semantic or task-relevant information instead of raw messages. In practice, a main challenge is to make transmitting information robust to channel noise and fading while keeping it compact. Existing learning-based transceivers often improve reliability by using larger encoders or higher-dimensional channel features, which increase computation complexity and channel uses. Therefore, optimized system design needs explicit rate control to balance performance and transmitting resources e.g., bandwidth and power. For this purpose, we propose a manifold-constrained hyper-connection (mHC) coding scheme with an entropy bottleneck (EB) for resource-efficient SemCom and TOC over wireless channels. Instead of using a single residual path of existing encoders, the proposed mHC-based semantic encoder applies multiple residual streams and constrains their interaction by doubly stochastic (DS) mixing matrices. The new structure improves representation diversity and training stability with negligible parameter and floating-point overhead. The EB quantizes the channel features and estimates the entropy-coded rate, enabling end-to-end rate--distortion/task optimization under bandwidth and transmit-power constraints. We further show that DS-constrained stream mixing does not increase the differential entropy of the transmitted features. This implies no increase in the ideal EB coding length. Experiments on SemCom and TOC under additive white Gaussian noise (AWGN), Rayleigh fading, Rician fading, and imperfect channel state information (CSI) show that the proposed scheme improves semantic/task performance, communication robustness, and convergence stability over residual and unconstrained HC baselines, while requiring no additional channel uses.
Jingwen Fu, Ming Xiao· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
