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The architectural framework for facilitating multi-satellite semantic communication in Earth observation for disaster relief systems.
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Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to tr...
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... illustrated in Fig. 1, we assume a set K of K LEO satellites and a set I of I images captured by each satellite, all of which are positioned within the LEO constellation. These LEO satellites can establish a communication link with a ground terminal (GT), (i.e., gateway) during a specific temporal interval known as the access window, characterized by the ...
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Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to tr...
Citations
... N OWADAYS, wireless communications [1], [2] have become the backbone of modern society, playing an essential role in a wide range of applications, including edge networks [3]- [5], satellite communication [6]- [8], the Internet of Things (IoT) [9], [10], drone communication [11], [12], and vehicular networks (V2X) [13], [14]. However, as the demand for efficient information services continues to surge, the pressure on wireless networks has increased, prompting numerous efforts to develop advanced algorithms aimed at alleviating the network burden [15]- [18]. ...
Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have gained significant attention for their exceptional capabilities in natural language processing and multimodal data understanding. Meanwhile, the rapid expansion of information services has driven the growing need for intelligence, efficient, and adaptable wireless networks. Wireless networks require the empowerment of RL-based LLMs while these models also benefit from wireless networks to broaden their application scenarios. Specifically, RL-based LLMs can enhance wireless communication systems through intelligent resource allocation, adaptive network optimization, and real-time decision-making. Conversely, wireless networks provide a vital infrastructure for the efficient training, deployment, and distributed inference of RL-based LLMs, especially in decentralized and edge computing environments. This mutual empowerment highlights the need for a deeper exploration of the interplay between these two domains. We first review recent advancements in wireless communications, highlighting the associated challenges and potential solutions. We then discuss the progress of RL-based LLMs, focusing on key technologies for LLM training, challenges, and potential solutions. Subsequently, we explore the mutual empowerment between these two fields, highlighting key motivations, open challenges, and potential solutions. Finally, we provide insights into future directions, applications, and their societal impact to further explore this intersection, paving the way for next-generation intelligent communication systems. Overall, this survey provides a comprehensive overview of the relationship between RL-based LLMs and wireless networks, offering a vision where these domains empower each other to drive innovations.
... The perception-communication-computing-actuation-integrated paradigm (PCCAIP), as proposed in [26], optimizes communication process through the integration of sensing, computing, and actuation technologies. In terms of delay optimization, papers [24,25,27] have improved transmission efficiency by integrating SemCom with different modulation methods. ...
Satellite–ground communication is a critical component in the global communication system, significantly contributing to environmental monitoring, radio and television broadcasting, aerospace operations, and other domains. However, the technology encounters challenges in data transmission efficiency, due to the drastic alterations in the communication channel caused by the rapid movement of satellites. In comparison to traditional transmission methods, semantic communication (SemCom) technology enhances transmission efficiency by comprehending and leveraging the intrinsic meaning of information, making it ideal for image transmission in satellite communications. Nevertheless, current SemCom methods still struggle to adapt to varying channel conditions. To address this, we propose a SemCom transmission model based on a Channel Code-Book (CCB) for adaptive image transmission in diverse channel environments. Our model reconstructs and restores the original image by documenting fading and noise states under various channel conditions and dynamically adjusting the denoiser’s model parameters. Extensive experimental results demonstrate that our CCB model outperforms three representative baseline models, including Deep JSCC, ASCN, and WITT in various environments and task conditions, achieving an advantage of more than 10 dB under high signal-to-noise ratio conditions.
... An adaptive encoder-decoder is introduced to safeguard critical features and minimize re-transmissions, while a novel error detection method at both the satellite and gateway addresses long propagation delays. In our recent study [29], we present an SC-based approach aimed at optimizing latency in the satellite imagery downlink process for Earth observation, focusing solely on direct communication scenarios. By harnessing the inherent efficiency of SC, the proposed solution significantly reduces communication overhead and enhances spectrum utilization. ...
... Then the second stage is to allocate the subcarrier frequency from the gateway to GUs. The problem formulation for satellite to gateways is given in (29): ...
... (34e) Given the identical objective function and constraints of problems (34) and (29), the DWOA algorithm, previously employed for (29), is also applicable to (34). To avoid redundancy, the comprehensive solution algorithm is provided in the subsequent subsection. ...
The sixth-generation (6G) non-terrestrial networks (NTNs) are crucial for real-time monitoring in critical applications like disaster relief. However, limited bandwidth, latency, rain attenuation, long propagation delays, and co-channel interference pose challenges to efficient satellite communication. Therefore, semantic communication (SC) has emerged as a promising solution to improve transmission efficiency and address these issues. In this paper, we explore the potential of SC as a bandwidth-efficient, latency-minimizing strategy specifically suited to 6G satellite communications. While existing SC methods have demonstrated efficacy in direct satellite-terrestrial transmissions, they encounter limitations in satellite networks due to distortion accumulation across gateway hop-relays. Additionally, certain ground users (GUs) experience poor signal-to-noise ratios (SNR), making direct satellite communication challenging. To address these issues, we propose a novel framework that optimizes gateway hop-relay selection for GUs with low SNR and integrates gateway-based denoising mechanisms to ensure high-quality-of-service (QoS) in satellite-based SC networks. This approach directly mitigates distortion, leading to significant improvements in satellite service performance by delivering customized services tailored to the unique signal conditions of each GU. Our findings represent a critical advancement in reliable and efficient data transmission from the Earth observation satellites, thereby enabling fast and effective responses to urgent events. Simulation results demonstrate that our proposed strategy significantly enhances overall network performance, outperforming conventional methods by offering tailored communication services based on specific GU conditions.
