Generative AI for Space-Air-Ground Integrated Networks

SAGIN network represents a cross-layer architecture in the B5G/6G communication, and is committed to providing enlarged coverage, high speed and seamless connection, strong security and reliability. The SAGIN combines technologies from the ground, air and space networks, where the details are as follows.

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  Ground-based network: The ground-based network is the foundation of SAGIN, including elements such as ground-based Internet and mobile communication networks. To meet the communication requirements from cloud centers to individual mobile users, a variety of machine learning is adopted for optimization. For example, in [4], a traditional machine learning method based on two-dimensional long short-term memory (LSTM) is proposed to predict network traffic and achieve resource allocation. However, due to the dynamic nature of ground-based networks (i.e., user mobility and traffic fluctuations), LSTM may encounter delays in adapting to network changes, thus preventing timely and optimal resource allocation.

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  Air-based network: The air-based network establishes high-altitude communication platforms and UAV self-organized networks. The high-altitude platforms provide a wide range of communication services, in which UAV networks composed of multiple UAVs offer autonomous connections to enhance communication support for ground users. Several studies apply traditional machine learning-based methods to air-based networks. For example, in [5], a deep reinforcement learning (DRL) approach was proposed to optimize UAV trajectories to minimize the weighted sum of execution time and expected transmission interruption duration. However, traditional DRL requires a high computing resource consumption, a large amount of calculation, and a long convergence time, which bring challenges to real-time decision-making in highly-dynamic aerial environments.

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  Space-based network: The space-based network is composed of a variety of satellite systems as backbone networks that ensure global connectivity and facilitate specialized communications. To achieve the goals of wide coverage, high bandwidth and low latency, machine learning-based methods were developed for the space-based networks. For example, in [6], a cooperative transmission scheme for satellites and airborne base stations was developed using K-means and K-medoids. However, due to the sensitivity of K-means methods to the initial centroid location and their two-dimensional assumption of clusters, they may have difficulty in fully covering complex channel data of three-dimensional spatial communication. This is especially in situations such as satellite signal interference or multi-path propagation.

Therefore, to achieve better design and optimization, there is an urgent need to introduce entirely new machine learning algorithms, especially generative AI.

Generative AI is a distinguished subdomain of AI, exmphasizing on the conceptualization and generation of content. It emerges from the aspiration to enable machines to fabricate innovative and unprecedented data that faithfully mirrors the inherent patterns, structures, and nuances encapsulated in the training datasets. Generative AI integrates a plethora of sophisticated models, including VAE, GAN, GDM, and transformer-based model (TBM), each espousing unique methodologies for assimilating and generating data.

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  Variational Autoencoders (VAEs): VAEs are generative models that use variational inference to model high-dimensional data, allowing them to generate new, consistent data instances. In SAGINs, VAEs facilitate the modeling and generation of adaptive communication protocols and diverse network configurations. For instance, in [7], a generative framework based on VAE was proposed to generate synthetic data for LOS estimation in non-terrestrial networks. In this case, the generated synthetic data does not contain identification information from the original dataset, allowing for its public use without violating privacy.

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  Generative Adversarial Networks (GANs): GANs are based on a game-theoretic approach involving two parts, i.e., a generator and a discriminator， which are effective in creating high-quality data. In SAGINs, GANs are used to mimic real network traffic patterns, aiding in better network traffic management and anomaly detection. For example, in [8], a network traffic prediction method based on LSTM and GAN was proposed to address the significant variations in service traffic access across different regions of satellite networks. Here, GAN was utilized to augment the dataset and prevent the occurrence of training overfitting issues.

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  Generative Diffusion Model (GDMs): GDMs replicate random diffusion processes to map data structures, leading to the creation of realistic data instances. In SAGINs, generative diffusion models are instrumental for simulating diverse network states and communication channel conditions. For example, in [9], a GDM detection algorithm was proposed that can capture fast-moving anomalies in satellite data without any moving components. Simulation results showed that the proposed GDM detection algorithm outperforms other baseline methods.

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  Transformer-Based Models (TBMs): TBMs are characterized by a self-attention mechanism that can manage data, learn complex dependencies, and generate coherent contextual instances. In SAGINs, TBM is used to simplify sequential communication protocols and ensure consistent exchanges between different network domains. For example, in [10], a model based on TBM was proposed for semantic segmentation of remote sensing images in satellite networks, where an adaptive fusion module was designed to blend semantic information between features of different scales through a self-attention mechanism.

The features of generative AI models are summarized in Table I. Moreover, note that deep reinforcement learning (DRL) focuses on learning suitable policies through environmental interactions, primarily emphasizing decision-making and control strategies. In contrast, generative AI is designed to generate new data points, strategies, or content based on existing data, with a strong focus on data augmentation and scenario simulation. Although some related work has combined these two approaches [2], generative AI complements RL by providing enriched data and potential strategies that enhance RL’s decision-making capabilities, particularly in complex and dynamic environments.

As shown in Fig. 1, generative AI-enabled SAGIN can enhance channel estimation, optimize resource allocation, and ensure intelligent networks by incorporating these advanced generative AI technologies. For generative AI, it can be primarily implemented in the cloud. The cloud provides substantial computational power and storage capabilities, which are essential for running generative AI models. By deploying these models in the cloud, we can leverage these resources to handle the intensive computations required for training and inference. Given SAGIN’s diverse and distributed components, such as satellites, UAVs, and ground-based transmitters, the cloud-based implementation allows for centralized processing and management of the AI models. Once trained, the generative AI models can generate strategies and data that can be used by these SAGIN components. This approach is reasonable because it allows the generative AI module to utilize the extensive computational resources of the cloud while distributing the results to the end or edge for application. Next, we summarize major research issues and solutions of generative AI-enabled SAGIN.

As generative AI is integrated into SAGIN, the system’s intelligence and seamless communication can be improved. However, certain challenges still exist as follows.

The SAGIN framework integrates different networks in a hierarchical structure. However, in densely urban landscapes, a significant issue is regarding the non-line-of-sight (NLOS) signals. In essence, while direct line-of-sight (LOS) signals originate from satellites, drones, or ground-based base stations, non-line-of-sight signals are subject to interference from urban obstructions such as tall buildings and giant bridges. This causes these signals, especially those from satellites and UAVs, to reflect multiple times before reaching their target. These multiple reflections introduce “multipath errors", where the direct and reflected signals have different arrival times and phases [15]. This discrepancy results in notable position errors, making it challenging for applications that demand high accuracy of signal.

Addressing the implications and seeking solutions to the challenges posed by multipath errors in SAGIN are possible through harnessing the potential of generative AI technologies. Accordingly, as shown in Fig. 2, a GDM-based channel map framework is proposed. Note that while equalizers and MRC are highly effective in mitigating multipath errors and improving signal quality, they primarily rely on linear signal processing techniques. These methods are well-suited for certain environments but can struggle in complex scenarios where channel characteristics are highly non-linear and dynamic, such as dense urban areas with significant reflections and obstructions. Moreover, non-ML frameworks such as equalizers and MRC often require precise knowledge of the CSI and involve complex calculations. These methods can be less adaptable to rapidly changing conditions, as they are typically designed for specific scenarios and may not generalize well to diverse and evolving network environments. In contrast, generative AI models, such as GDM, excel in capturing and modeling non-linear relationships within data. This makes them particularly suitable for handling multipath environments’ intricate and variable nature. Generative AI models can learn from vast amounts of data and continuously adapt to changing conditions, providing more accurate and robust channel modeling and state estimation even in highly dynamic scenarios. In particular, GANs utilize latent representations to transform raw input data into a more abstract and flexible form [11], which is especially beneficial for complex signal-processing tasks. These latent representations allow GANs to capture the underlying structure and patterns of the data, leading to more effective and adaptive signal processing than traditional methods. This flexibility enables generative AI to provide more comprehensive solutions in scenarios where traditional linear techniques may fall short. Next, our GDM-based channel map framework consists of the following functions.

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  Step 1: Image Acquisition: Within the scope of SAGIN, data collection is a continuous process. Satellites and UAVs are regularly deployed to capture detailed ground information from both urban and rural landscapes. While satellites play a major role in capturing overview ground information, UAVs are capable of providing more detailed local images. Once acquired, the information, e.g., captured images, are transferred to the processing centers, providing a foundation for subsequent steps of the framework.

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  Step 2: Image Processing: At the processing center, attention should be paid to the division of areas from the image where the LOS signal is inferred. From these delineated areas, relevant data such as excess delay, signal paths, and transmit power of the transmitters (i.e., UAVs or satellites) are extracted through ground-based receivers. For a comprehensive representation of the indicators, this data is structured into a graphical network.

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  Step 3: Elimination of Redundant Information: In SAGIN, especially under complex multipath conditions, there is always potential for noise or superfluous information. Specifically, during image processing, unintended anomalies can emerge from data redundancy, leading to an environment representation (EP) that might be cluttered with irrelevant details, hindering the accurate identification of the target transmitters. To solve this, a continuous noise reduction mechanism based on a GDM is initiated. This process refines the diffusion network by removing unnecessary noise, ensuring that the resulting classification is relevant to the current target.

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  Step 4: Zero-shot Classification: Once the data is in place, stable diffusion density estimation⁴⁴4Stable diffusion density is a probabilistic modeling technique used to estimate the distribution of data points in a high-dimensional space. In our framework, this technique involves modeling the spread and concentration of data points to understand their underlying structure. is utilized. These estimates possess the capability to drive zero-shot classification⁵⁵5Zero-shot classification is a method in machine learning where the model can correctly classify instances from classes that it has never encountered during training. In our framework, zero-shot classification enables the model to identify and categorize new types of channel conditions without requiring additional training data for those specific conditions., removing the necessity for additional training. The classification is accomplished by discerning patterns and variations within the network and aligning them with density estimates.

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  Step 5: Channel Information Construction: Finally, the channel information map is constructed to classify each potential LOS signal to improve the communication and service quality of the entire system.

As mentioned above, using images taken by UAVs and satellites, ground receivers are first used to extract corresponding picture information, and then map construction and channel classification are performed. Here, the channel map is generated at a cloud-based processing center. The cloud provides substantial computational power and storage capabilities essential for running sophisticated generative AI models like GDM. In our framework, data collection is a continuous process facilitated by satellites and UAVs. These devices capture detailed ground information, which is then transferred to the cloud for processing. The use of ground-based sensor nodes further enhances the accuracy and richness of the data collected. In particular, the collected images are preprocessed to identify and segment areas where the LOS signals are inferred. This segmentation process helps in isolating the relevant data needed for further analysis. The data is then subjected to noise reduction using the GDM, eliminating redundant information and focusing on the essential details. Stable diffusion density estimation is employed to classify the data, removing the necessity for additional training. Finally, the channel information map is constructed, categorizing each potential LOS signal, thereby improving the communication and service quality of the entire system. Here, SAGIN plays two critical roles, i.e., capturing image information and using this information to extract spectrum map features that enhance the overall network performance. This creates a beneficial cycle where the collected data improves the network, and the improved network, in turn, facilitates better data collection. The simulation involves a single satellite, one UAV, and a ground-based access point. This setup includes four users within the simulation environment. Backhaul is performed using GNSS signals to ensure precise location data and synchronization. Resource allocation is managed by classifying the collected data using a GDM, which processes and categorizes the information to optimize network resources. The dataset utilized for this simulation is a classic GNSS type, comprising raw measurements from the f9p receiver. This dataset includes elements, such as GPS time, satellite code, code and phase measurements in L1 and L2 bands, Doppler measurements, signal strength, LOS/NLOS labels, pseudorange error, and the ground truth location (latitude, longitude, and height). For attention map, we use 100,000 images, of which approximately 75% are used for training and 25% for validation. In order for the ground receiver to more accurately extract relevant data such as the direction angle of the transmitter (i.e., UAVs or satellites), the key semantic information in the images is first extracted, as shown in part A of Fig. 3. For the left results, the attention map reflects the importance of map information through different brightness levels, where the more brightness indicates the greater weight of the corresponding map information. For instance, the information weight of the road area and roof area is greater because there is no obstruction between them and the link information is better. Moreover, the attention map depicted on the right uses a color scale from blue to green. The dark blue represents the areas with the least attention, while the bright green represents the areas with the most attention. By comparing the two results, it is evident that areas with more brightness in the original image usually correspond to areas with brighter green in the attention map. This demonstrates the system’s ability to assign higher weights to specific parts of the image and focus more on areas such as roads and rooftops, as these areas provide a clearer LOS.

After obtaining the channel data information, it is required to be classified. In this step, the GDM model is employed for classification, which utilizes the principles of information diffusion. The process of diffusion classification is shown in Part B of Fig. 3. Fig. 3 (B) shows the stage scatter plot as training time changes. Each point is a set of training data, and the root mean square error (RMSE) is used as the evaluation target. From the first to the last image, one can clearly see the RMSE gradually decreasing, from the starting value of 109.32 to only 0.58. This indicates that the accuracy of model’s predictions significantly improves as the diffusion process proceeds. Additionally, each point on the scatter plot represents the model prediction error. At the beginning of the diffusion, these error points are widely distributed and far away from the red baseline (i.e., 0 represents the LOS signal). However, as the diffusion steps proceed, these points gradually converge toward the red baseline, which further verifies the improvement in model accuracy.

Next, to verify the effectiveness of the proposed GDM classification, we compare it with several commonly used classification algorithms such as the K-nearest neighbors (KNN) method, extreme gradient boosting (XG-boost) method, and CNN method, with the results shown in part C of Fig. 3. The results show that the proposed GDM classification method is superior to the other benchmarks regarding accuracy, F1 score, and recall rate. This is because the GDM model can effectively capture the distribution and complex patterns of the underlying data for high-latitude or complex data. Unlike traditional classification methods, which may be susceptible to noise or data irregularities, GDM utilizes a diffusion process to gradually transform the data, ensuring a smoother, more robust representation. This inherent ability to extract complex data structures allows it to perform better when other algorithms struggle with overfitting or misclassification. It is worth noting the performance specifics of the three benchmark algorithms. The KNN method, although intuitive, sometimes has difficulty handling high-dimensional data and is sensitive to the choice of distance metric. The XG-boost method is a gradient enhancement method but is prone to overfitting. The CNN method works on the principle of local receptive fields and hierarchical feature extraction, which may not always be optimal for data sets that lack spatial structure. While each method has its merits and applications, its limitations in certain scenarios make the GDM’s inherent ability to extract complex data structures even more commendable. Moreover, using the GDM-based model for channel information mapping leads to faster updates in network configurations. Specifically, it shows that the GDM-based approach can achieve a reduced RMSE within 10 stages. In contrast, traditional methods such as CNN and K-means required over 100 iteration stages to reach similar accuracy levels. This significant reduction in the number of iterations needed for convergence demonstrates the efficiency of the GDM-based model, ensuring that the network can respond swiftly to dynamic environments, maintaining high performance and reliability.

Finally, based on the classification results, the channel map is constructed. To further verify the effectiveness of the constructed channel model, part D of Fig. 3 shows the cumulative reward value obtained with the increase of the number of iterations using the same PPO-based strategy for different types of channel models. Here, the corresponding curves are smoothed via a sliding window to provide a clearer overall trend of the raw results. The results show that regardless of the types of channel models, the RL-based strategy can converge with an increment of iterations. The drop in the reward after around 120 iterations can be attributed to the exploration mechanism inherent in RL algorithms, specifically the PPO-based method used in our study. During the training process, the RL agent occasionally explores less optimal actions to improve its policy and avoid local minima. This exploration phase can temporarily reduce the cumulative reward as the agent evaluates various actions. However, as training progresses, the agent learns from these explorations and adjusts its policy to achieve higher performance, leading to an increase in the reward again. Furthermore, the channel model constructed using our method can achieve higher performance gains. This is because the channel constructed by our method more accurately reflects the channel conditions under better classification conditions, allowing related resource optimization methods to find more reasonable and feasible solutions, i.e., the suitable transmit power.

SAGIN integrates various data sources, such as images, videos, audio, and radar scans, from satellites to ground sensors, providing information. Generative AI can seamlessly integrate this multi-modal data to view the SAGIN operating environment comprehensively. By merging data from different modalities, SAGIN can generate a detailed environmental model, enhancing the network’s ability to make informed decisions. For example, combining radar scans with satellite images can improve object detection and tracking accuracy, while integrating audio data can enhance situational awareness in emergency response scenarios. Technical approaches like deep learning-based data fusion and multi-modal neural networks can be employed to combine and analyze these diverse data sources effectively. This multi-modal integration enriches the contextual intelligence available to the network, enabling more precise and effective responses to dynamic conditions and improving overall connectivity and performance.

With growing concerns about energy consumption and environmental impact, it is crucial for SAGIN to operate efficiently and sustainably. Non-terrestrial networks, such as those involving satellites and high-altitude platforms, generally have lower energy consumption than terrestrial networks. Generative AI can enhance these efficiencies by optimizing trajectory planning and communication strategies. For example, generative AI can analyze historical data and current operational metrics to predict future network demands, recommending optimal satellite trajectories that minimize fuel consumption. Additionally, generative AI can optimize communication protocols to reduce energy usage during data transmission and processing. By continuously learning and adapting to environmental conditions and network demands, generative AI ensures that SAGIN operates sustainably while maintaining high performance.

Incorporating AI-generated content (AIGC) services into SAGIN emphasizes the potential for significant technological advancements. Generative AIGC services can change how data is generated, processed, and utilized across the network. For instance, AIGC services can create personalized content such as real-time traffic updates, emergency alerts, and customized multimedia content, enhancing user engagement and satisfaction. By leveraging AIGC, SAGIN can provide dynamic, responsive, and context-aware services that adapt to real-time user needs and environmental changes. However, integrating these services introduces challenges related to computational and communication complexities. Efficiently managing these complexities to minimize integration overhead while maximizing the benefits of AIGC services is critical. Approaches like model compression, edge computing, and efficient data transmission protocols can help address these challenges.