The Low-Altitude Wireless Networks (LAWNs) has emerged as a cornerstone of next-generation mobile due to their flexibility and adaptability in providing on-demand connectivity. However, ensuring reliable and high-throughput aerial drone communication remains a major challenge, mainly due to the dynamic mobility of aerial drones and the complexity of the wireless propagation environment. Traditional LAWNs rely heavily on channel sampling and real-time feedback, which introduce latency and communication overhead. In this work, we proposeRadioRS, a novel sampling-free aerial drone communication framework that combines Radio Map (RM) prediction with Rate-Splitting Multiple Access (RSMA) to enable robust and efficient communication without requiring explicit channel estimation during flight. RadioRS leverages a RM that provides location-aware predictions of channel state. To enhance the accuracy and generalization of these predictions under complex propagation conditions, we develop a generative model based on the Mamba architecture, which efficiently captures fine-grained correlations in the radio environment. Building on the RM, RSMA is employed to flexibly manage interference and improve spectral efficiency. In addition, we design a Mamba-powered controller that adapts beamforming strategies from the RM directly, further improving link reliability and throughput. Comprehensive simulation results demonstrate that the proposed RadioRS framework significantly outperforms conventional channel-sampling-based approaches in terms of both communication reliability and throughput.

Emerging mega-constellations with numerous Low Earth Orbit (LEO) satellites actively provide pervasive Internet services worldwide, which are usually considered crucial components of Non-Terrestrial Networks (NTNs). However, the high mobility and limited coverage of LEO satellites introduce frequent handovers, causing network interruptions and degrading Quality of Service (QoS). While many efforts have been made to alleviate the impact of handovers on service provisioning from NTNs, they usually assume channel conditions are pre-determined and remain unchanged as satellites move, which is different from real situations and thus may experience significant performance degradation compared to theoretical analysis. In this paper, we proposeOracleto promise QoS-aware service provisioning in NTNs under dynamic channel conditions. Specifically, we mathematically formulate a channel model to characterize dynamic channel conditions in NTNs and develop a QoS maximization problem considering handover frequency and transmission capacity. To accommodate the dynamic nature of NTNs, we introduce a Model Predictive Control (MPC)-based controller to predict future network status and generate control strategies correspondingly, and leverage Digital Twin (DT) for real-time network status consideration. For higher efficiency, we further employ Generative Artificial Intelligence (GAI) with a safe transfer learning-based framework to enhance model adaptivity to environmental uncertainties and ensure feasible control decisions in real-world NTNs. Extensive simulation results under the real-world constellation demonstrate thatOraclecan enhance up to$3\times $QoS during online service provisioning.

The rise of heterogeneous aerial and space platforms within Space-Air-Ground Integrated Networks (SAGINs) introduces significant challenges, as the limited spectrum resources force these platforms to operate within shared frequency bands, resulting in co-existing systems. Effective interference management in such networks requires both the design of communication channels and the dynamic mitigation of interference between them. Prior research has largely focused on interference mitigation with fixed communication links, often overlooking adaptive channel selection, which can result in performance degradation. In this study, we address this limitation by introducing MetaRS, an innovative, self-intelligent rate-splitting solution designed for more flexible interference management in co-existing SAGINs. MetaRS enables adaptive channel and communication scheme selection, by leveraging a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA)-based framework enhanced with a one-pass diffusion model. Specifically, the FD-RSMA-based framework allows MetaRS to dynamically shift its interference management strategy according to the current network status. The integration of the diffusion model further enhances MetaRS by allowing it to recognize and adapt to real-time channel conditions and user deployment, thereby enabling self-intelligent interference mitigation. Simulation results demonstrate that MetaRS significantly outperforms conventional SDMA, RSMA, and FD-RSMA approaches. This improvement stems from MetaRS’s joint optimization of channel selection and its adaptive, intelligent interference management capabilities, which effectively balance channel utilization and mitigate interference in complex, multi-platform environments.

The challenge of mitigating interference in Space-Air-Ground Integrated Networks (SAGINs) is exacerbated by the inherent channel uncertainty, which arises due to dynamic weather conditions, heterogeneous user deployment, and different altitude of transmitters. To tackle this problem, Rate-Splitting Multiple Access (RSMA) has been seen as a promising solution due to its robustness. However, conventional beamforming designs for RSMA often suffer from two major limitations: high processing delays and overfitting to specific channel conditions. When the channel conditions change, the performance of these predictors degrades significantly, limiting their effectiveness in dynamic environments. To address these challenges, we propose a novel Fast-Adaptive Predictive Beamforming (FA-PB) framework for RSMA in SAGINs. Unlike traditional predictive beamforming approaches that rely on fixed predictive models, FA-PB integrates a transfer-learning-based online learning mechanism. This innovative approach allows the predictor to dynamically adapt to new channel conditions with minimal computational overhead. FA-PB achieves this by leveraging few-shot Channel State Information at the Transmitter (CSIT) samples, enabling real-time updates and adjustments to the predictor. Consequently, FA-PB ensures that the beamforming process can rapidly adapt to fluctuating channel conditions, maintaining high levels of performance even in highly dynamic SAGIN environments. Extensive simulation results validate the superiority of the FA-PB framework, demonstrating its enhanced adaptability and improved beamforming performance in SAGINs.

Integrating non-terrestrial networks (NTN) with terrestrial networks (TN) is vital to support scalable multicast/broadcast services (MBS) in 6G, particularly for low-altitude UAV swarms requiring seamless and reliable coverage. Low Earth orbit (LEO) constellation in integrated TN-NTN can effectively take over multicast to UAVs when flying over TN underserved regions. However, distinct differences in signal variation and mobility between TN and NTN make it difficult to optimally exploit MBS cooperation and maintain superior delivery. To address these challenges, this paper proposes a mobility-aware TN-NTN MBS orchestration framework for low-altitude UAVs. We fist cognize signal variations of TN and NTN in low-altitude layer with UAV mobility characteristics from cell center to edge, and use an Adaboost-based machine learning classifier to dynamically group UAVs into two segments for optimal system multicast delivery. A joint file multicast scheduling strategy is also proposed to align with UAV and NTN mobility-driven grouping dynamics to globally enhance multicast time efficiency. System-level case studies with a practical LEO constellation confirm our approach significantly outperforms existing methods, especially when more UAVs near cell edges. Our method also demonstrates strong adaptability to network dynamics and superior time efficiency, enabling robust and efficient MBS delivery in integrated 6G TN-NTN systems.

Extremely Large-scale Antenna Array (ELAA) is increasingly recognized as a promising solution for enhancing spectral efficiency and spatial resolution in the 6G mobile system. However, realizing these benefits necessitates the development of sophisticated interference management strategies, which typically rely on perfect Channel State Information at the Transmitter (CSIT) and involve computationally intensive operations. In real-world scenarios, perfect CSIT is typically infeasible due to inherent channel estimation errors and hardware impairments, which also lead to imperfect Successive Interference Cancellation (SIC). Additionally, the computational complexity associated with precoding schemes poses a formidable challenge. To address these issues, this study proposes a Deep Learning (DL)-assisted Rate-Splitting Multiple Access (RSMA) scheme for ELAA systems. The primary objective is to maximize the geometric mean of ergodic user-rates under imperfect CSIT and SIC, thereby optimizing both fairness and system throughput. Given the prohibitively high computational complexity of conventional optimization approaches to address this optimization problem, we introduce a DL model, named GruCN, to optimize precoder design. Simulation results demonstrate that the proposed RSMA-enabled ELAA system achieves better performance in terms of fairness and robustness under imperfect CSIT. Moreover, the GruCN model exhibits remarkable efficiency and effectiveness in precoder optimization.

As one of the most promising elements in Intelligent Transportation Systems (ITSs), connected electric vehicles (CEVs) can be collectively utilized to improve the quality of essential transportation services. However, involving CEVs to provide vehicle-to-grid (V2G) services becomes a crucial problem since they are selfish and belong to different parties. To solve this problem, we propose an efficient federated CEV scheduling framework that implements noncooperative online incentive approach. In particular, the proposed system is designed for providing privacy-preserving power grid signals to each CEV aggregator (CEVA) within the citywide region. To motivate the CEVs to participate in V2G services, a noncooperative interaction scheme is designed between the selfish CEVs and each CEVA. The purpose of the game is to let the CEVA to determine the real-time electricity trading prices, while the CEVs decide their own real-time service schedules. Case studies assess the feasibility and effectiveness of proposed noncooperative incentive approach, in which the efficient motivation on the CEVs contribute to a high quality V2G services. Additionally, the use of sufficient online parking allocation method can further increase the quality of V2G services.

Emerging Low-Earth Orbit (LEO) mega-constellations face challenges such as limited bandwidth and highly variable user demand, which can degrade network performance and lead to inefficient satellite resource utilization. One promising solution is to enable Content Delivery Networks (CDNs) within LEO satellites by deploying cache-equipped satellites. However, many existing approaches rely on inter-satellite links, which are not widely used in practice and are typically activated only when terrestrial ground station coverage is insufficient. Furthermore, the dynamic coverage patterns of satellites and diverse regional content preferences add to the complexity of efficient CDN deployment in space. To address these challenges, we proposeSpaceCache+, a satellite-based CDN framework. We introduce a new metric,user benefit, that jointly captures user coverage and latency reduction to assess the effectiveness of cache satellite deployment. Recognizing that deployment typically occurs incrementally, we formulate theUser Benefit-centric Cache Satellite Deploymentproblem and design an efficient heuristic solution. To enhance content placement, we also propose a cache replacement policy based on zero-shot meta-learning, which adapts to both regional content popularity and satellite mobility. We evaluate the performance ofSpaceCache+using real-world constellation settings with CDN traces. Compared with benchmark strategies,SpaceCache+improves user benefit and cache hit ratio by up to 66.29% and 77.12%, respectively.

Despite the allure of ubiquitous, high-speed, and low-latency connectivity offered by Space-Air-Ground Integrated Networks (SAGINs), the co-existence of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) within the same frequency band poses significant challenges in interference management. Traditional optimization approaches, requiring seconds or even minutes for beamforming design, simply cannot keep pace with this dynamic environment. This work addresses these challenges by proposing a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA), which enables efficient cross-system interference management in SAGINs with statistical Channel State Information (CSI) at the Transmitter (CSIT). Building upon FD-RSMA, we study the precoder design of LEO satellites and UAVs along with common rate allocations of RSMA to maximize Weighted Ergodic Sum Rate (WESR). To handle channel randomness, we employ a Sample Average Approximation (SAA) approach. Furthermore, a Deep Learning (DL)-based precoder design algorithm, called GruCN, which marries the advantages of Gate Recurrent Unit (GRU) and Convolutional Neural Network (CNN), is proposed to efficiently tackle the non-convex optimization problem. Numerical results demonstrate the effectiveness and efficiency of our proposed DL-assisted FD-RSMA. Compared to conventional RSMA approaches, FD-RSMA improves up to 20% of WESR performance, while the GruCN achieves around 50% higher WESR performance and up to four orders of magnitude lower processing time than the conventional optimization approaches.

In-orbit computation offloading plays a crucial role in enhancing the performance of resource-constrained mobile devices by conserving energy and reducing application latency. However, the inherent channel uncertainty in uplink communications poses a significant challenge, often degrading the Quality of Service (QoS) provided by Satellite Edge Networks (SENs). This uncertainty cannot be effectively captured by static parametric modeling, limiting their applicability in dynamic environments. To address this limitation, we propose an environment-aware computational offloading strategy for SENs. Unlike previous studies that neglect the impact of uplink channel uncertainty, we focus on this key issue by formulating a stochastic optimization problem aimed at minimizing offloading latency. Our approach integrates channel state variability into the decision-making process, ensuring a more realistic and robust model for SEN applications. In particular, we design a novel Spatio-Temporal Mixing (STM) methodology to extract relevant features from both environmental data and historical Channel State Information (CSI). These features are then used to jointly optimize the task scheduling, satellite selection, and beamforming vector design. Extensive simulations demonstrate that the proposed STM approach significantly reduces latency compared to traditional methods. The results highlight the effectiveness of our strategy in addressing the challenges posed by uplink channel uncertainty, ultimately leading to more efficient and reliable SEN operations.

With the increasing deployment scale of Low Earth Orbit (LEO) mega-constellations, more satellites are expected to become visible to users simultaneously, bringing a new opportunity to optimize the network performance by properly assigning users to satellites. In this paper, we consider LEO mega-constellations without Inter-Satellite Links (ISLs) while assuming there are enough ground relays for inter-satellite communications. To establish a path from a user terminal to the nearest ground station (which serves as a gateway to the Internet), shortest path routing is usually adopted. To focus on the problem of user-satellite assignment, as well as to make routing more scalable, we divide the routing process into two parts: assigning the user terminal to a visible satellite, and finding a path from the satellite to the nearest ground station. For simplicity, shortest path routing is assumed in the second part. Aiming at minimizing the Maximum Satellite Utilization (MSU), a Mixed Integer Linear Programming (MILP), called Optimal User-Satellite Assignment (OUSA), is formulated. Performance evaluations are conducted based on Starlink Phase I mega-constellation and AWS ground station locations. As compared with the existing solutions, we show that the average load balancing performance can be improved by up to 33.29%.

Autonomous cooperative planning (ACP) is a promising technique to improve the efficiency and safety of multi-vehicle interactions for future intelligent transportation systems. However, realizing robust ACP is a challenge due to the aggregation of perception, motion, and communication uncertainties. This paper proposes a novel multi-uncertainty aware ACP (MUACP) framework that simultaneously accounts for multiple types of uncertainties via regularized cooperative model predictive control (RC-MPC). The regularizers and constraints for perception, motion, and communication are constructed according to the confidence levels, weather conditions, and outage probabilities, respectively. The effectiveness of the proposed method is evaluated in the Car Learning to Act (CARLA) simulation platform. Results demonstrate that the proposed MUACP efficiently performs cooperative formation in real time and outperforms other benchmark approaches in various scenarios under imperfect knowledge of the environment.

Non-terrestrial networks (NTN) provide a revolutionary solution to bridge the digital divide in areas underserved by terrestrial network (TN). Particularly, low Earth orbit (LEO) constellations can substitute for offering data services to mobile devices like UAVs when flying into TN service-deficient areas. In this paper, viewing TN and NTN as both competitors and collaborators, we present a novel approach to optimize UAV mobility management in integrated TN and NTN, thereby improving network service continuity. Specifically, we enable UAVs to opportunistically handover (HO) between TN and NTN during flight to maintain reliable data reception while minimizing HO overhead. The decision to switch from TN to NTN involves comparative assessments of service capabilities and HO rates between two segments over time, considering their link quality variations during UAV flight, TN coverage distributions, and orbital dynamics of LEO satellites. Our system-level case studies, based on a practical LEO constellation, demonstrate the significant advantages of UAV HO planning in integrated TN and NTN over standalone TN or NTN for HO numbers and service rates. We also demonstrate that in various scenarios, our UAV mobility management solution consistently outperforms existing heterogeneous HO methods that underrate the dynamic differences in service capabilities between TN and NTN.

The growth of vehicular applications will inevitably require Base Stations (BSs) to simultaneously serve more Connected Vehicles (CVs) within limited bandwidth resources, which imposes a great challenge in interference management. Effective management of this interference is crucial for reliable Vehicle-to-Everything (V2X) communication, and necessitates accurate Channel State Information at the Transmitter (CSIT). In practice, the dynamic and unpredictable nature of CV movements prevents BS from obtaining perfect CSIT, leading to outdated information and threatening communication performance. In this study, we propose a Rate-Splitting Multiple Access (RSMA)-enabled V2X communication system to efficiently manage interference channels. We leverage a 1-layer RSMA scheme to relax the stringent requirement for perfect CSIT and enhance robustness to outdated information. Furthermore, we introduce Gruformer, a transformer-based model for improved CSIT prediction utilizing historical data. While longer forecasting horizons decrease accuracy, we present a game theory-based approach that significantly reduces processing time for power allocation, enabling timely decisions before CSIT becomes outdated. Simulation results reveal that Gruformer allows for more accurate predictions during rapid changes in channel conditions. Leveraging this high-quality CSIT, the proposed V2X system achieves a 20% increase in Weighted Ergodic Sum-Rate (WESR). Furthermore, the game theory-based approach delivers a 60% reduction in processing time while maintaining near-optimal performance.

Existing Rate-Splitting Multiple Access (RSMA) techniques offer a promise for Non-Terrestrial Networks (NTNs) by managing interference and ensuring reliable data transmission. However, precoder design remains a crucial bottleneck, demanding accurate Channel State Information (CSI) feedback and complex optimization, which are challenging in practical deployment. Motivated by this, this paper proposes a novel Deep Learning (DL)-based method to predict the precoder design from the historical CSI directly. In particular, we first establish a predictive beamforming protocol for precoder design using historical CSI, bypassing the need for constant feedback and reducing complexity. Subsequently, we formulate a general problem for precoder design, with the Weighted Ergodic Sum Rate (WESR) serving as the objective function. Solving this problem is particularly challenging due to the dynamic nature of wireless channels in NTNs. To address this, we designed a fusion model, named TranCN, which harnesses the strengths of Transformers and Convolutional Neural Networks (CNNs) to extract spatial-temporal features from historical CSI, thereby enhancing precoder performance. Simulation results demonstrate that our predictive beamforming scheme enables RSMA to adapt to dynamic channel conditions using historical CSI, surpassing baseline methods and improving data transmission resilience.

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms both the popular Multi-User Linear Precoding (MU–LP) and the conventional Non-Orthogonal Multiple Access (NOMA) system. Finally, the BCD framework is shown to generate near-optimal solutions at reduced complexity.

We propose a novel energy efficient virtual optical network embedding (VONE) scheme over sliceable-transponder-equipped elastic optical networks (EONs). In the scheme, two energy-saving methods for data center (DC) and transponder (TP) are designed for node mapping and link mapping, respectively. During dynamic VONE, two schemes are developed: i) DC-EA scheme which only considers DC energy-saving in the node mapping; and ii) DC&TP-EA scheme which considers the energy saving for both the DCs and TPs, in the node mapping and link mapping, respectively. In order to evaluate the energy-saving performance of our proposed scheme, a benchmark algorithm is also realized which tries to maintain traffic-load balancing (TB) without any energy-saving consideration. The simulation results demonstrated that our proposed DC&TP-EA scheme achieves a maximum power saving, compared with the DC-EA and TB schemes. Also, the effect of the DC-EA gets very remarkable in terms of energy saving especially when the traffic load is smaller. With the increase of traffic load, the DC&TP-EA has higher energy-saving efficiency, due to the fact that the TP energy-saving plays a more and more important role.