The integration of Low-Earth Orbit (LEO) satellites with hypersonic aircraft presents a critical frontier for next-generation non-terrestrial networks (NTN). However, the ultra-high dynamics inherent in these scenarios introduce severe Doppler frequency shifts and time-varying delays, significantly degrading link reliability. To address these challenges, this article proposes a robust Orthogonal Time Frequency Space (OTFS) communication framework tailored for hypersonic-LEO links. First, we establish a three-dimensional (3D) kinematic model based on the Earth-Centered Inertial (ECI) coordinate system. This model accurately characterizes the dynamic delay-Doppler (DD) channel features across the aircraft’s ascent, cruise, and descent phases. Guided by these channel insights, we develop a phase-adaptive dynamic pilot design scheme that jointly optimizes pilot position, pilot power, pilot quantity, and guard intervals to maximize spectral efficiency and estimation accuracy. Furthermore, leveraging the temporal correlation of the OTFS channel in the DD domain, we propose a Multi-Frame Joint Cross-Correlation Matching (MF-JCM) channel estimation algorithm. This algorithm aggregates multi-frame channel responses to suppress noise and enhance detection probability. Simulation results demonstrate that the proposed scheme achieves a Bit Error Rate (BER) of 10−4at an signal-to-noise ratio (SNR) of 7.5 dB in the ascent phase and 11 dB in the descent phase. Hardware experiments based on Software-Defined Radio (SDR) further validate the feasibility and superiority of the proposed approach in practical ultra-high dynamic scenarios.

Satellite-based Internet of Things (S-IoT) faces a fundamental trilemma: propagation delay, dynamic fading, and bandwidth scarcity. While Layer-coded Hybrid ARQ (L-HARQ) enhances reliability, its backtracking decoding introduces age ambiguity, undermining the standard Age of Information (AoI) metric and obscuring the critical trade-off between data freshness and transmission efficiency. To bridge this gap, we propose a novel cross-layer optimization framework centered on a new metric, the Cross-layer Age of Error Information (C-AoEI). We derive a closed-form expression for C-AoEI, explicitly linking freshness to system parameters, establishing an explicit analytical connection between freshness degradation and channel dynamics. Building on this, we develop a packet-level encoded L-HARQ scheme for multi-GBS scenarios and an adaptive algorithm that jointly optimizes coding and decision thresholds. Extensive simulations demonstrate the effectiveness of our proposed framework: it achieves 31.8% higher transmission efficiency and 17.2% lower C-AoEI than conventional schemes. The framework also proves robust against inter-cell interference and varying channel conditions, providing a foundation for designing efficient, latency-aware next-generation S-IoT protocols.

Future sixth-generation (6G) low Earth orbit (LEO) satellite systems will utilize large-scale multi-beam phased arrays operating at millimeter-wave (mmWave) frequencies, with embedded power amplifiers (PAs) at each antenna element. However, the severe path loss drives PAs near saturation, and dynamic beamforming leads to nonuniform, time-varying PA conditions, jointly resulting in complex memory nonlinearities that degrade system performance. Such in-band distortions require mitigation by PA linearization, which remains highly constrained in LEO satellite systems. Therefore, accurate modeling and quantification of these nonlinearities are critical for realistic performance evaluation and to guide the development of effective linearization strategies. Current analytical methods focus on single-PA or memoryless-array models, without addressing the frequency-dependent statistical characteristics and spatially varying radiation patterns of nonlinear distortions in multi-beam phased arrays. In this paper, we develop statistical models for the PA output and the received signals in multi-beam phased arrays, explicitly decomposing the distortion structure and deriving closed-form expressions for each distortion component’s statistical properties. Furthermore, we analyze the spatial beam-pattern distortions and derive a closed-form expression for the signal-to-distortion-plus-interference-and-noise ratio (SDINR) under nonlinear distortion. Extensive simulations, conducted measurements, and over-the-air (OTA) experiments validate that the proposed model reduces optimal input back-off estimation error by 2 dB and improves SDINR prediction accuracy by approximately 3 dB. This research establishes a robust analytical foundation for future LEO satellite system design and linearization technology development.

Satellite systems have emerged as a key component of future 6G networks, where beam hopping is widely adopted to improve resource utilization under limited onboard power and bandwidth. However, due to the inherent timescale mismatch between beam scheduling and resource allocation, as well as the increased system complexity caused by wide-area coverage and flexible resource demands, efficient resource scheduling requires coordinated decision-making across multiple timescales while ensuring user-level QoS provisioning. This paper aims to address the three-dimensional coupling of resources in the beam, power, and frequency domains while satisfying heterogeneous user QoS requirements. The key idea is to establish a multitime-scale framework that decouples the original optimization problem in the temporal domain. Specifically, beam scheduling is performed over beam hopping time slots(BHTSs), while resource allocation are conducted over finer-grained user scheduling time slots. A verification approach is developed to provide theoretical justification for the proposed timescale decomposition and to ensure the consistency. Based on this framework, a weight-based search strategy is employed for beam hopping pattern (BHP) selection, and a block coordinate descent (BCD) method is adopted to iteratively optimize user-level resource allocation. The simulation results demonstrate that the proposed scheme significantly improves system throughput and service satisfaction quality compared to existing methods, while maintaining robust performance under heterogeneous traffic and delay-sensitive scenarios.

International audience

Current 3D model Level of Detail (LOD) methods require multiple models with varying detail levels to reduce client computational load, transmitting different models based on the user's distance to the object. However, this process consumes excessive network bandwidth and strains the client's memory and storage. To address this, we propose BS3 (Bézier Slicing for 3Ds), a middleware-enabled method that slices 3D meshes and fits the contours using Bézier curves. Acting as an intermediate layer, the BS3 middleware handles slicing, vectorization, sampling and reconstruction, allowing .bs3 files to be streamed only once and adjusted dynamically at different sampling rates. Our experiments demonstrate the efficiency and performance analysis of BS3, which shows that it can reduce network and storage burdens while keeping the display effect. We believe that BS3 will enhance 3D multimedia in the game, exhibition, digital museum, cultural heritage, metaverse, etc.

Non-terrestrial networks (NTNs), connecting components such as low-earth orbit (LEO) satellites, exhibit highly dynamic behaviors in both their topological configurations and radio channels. Traditional pilot-aided channel estimation (PACE) methods are susceptible to significant Doppler shifts and rapid channel variations, limiting further enhancements in data transmission rates or incurring unaffordable pilot overheads. Data-aided channel estimation (DACE), as a potential improvement path, leverages data vectors to supplement pilot signals. Nevertheless, conventional DACE faces difficulties in effectively selecting data vectors under frequency offsets and rapid channel variations. To address this, this paper introduces a dynamic threshold-based scheme for selecting reliable data vectors, which leverages an adaptive threshold to precisely identify reliable data vectors within a frame. Furthermore, we propose a data vector deviation estimation method as a preprocessing step to correct the overall offset of data vectors, thereby ensuring the efficacy of data selection even amidst significant channel variations. Our approach significantly elevates the performance of LEO links within current protocols and is compatible with higher modulation orders and frequency bands. Simulation results validate the effectiveness of the proposed scheme.

The 3rd Generation Partnership Project (3GPP) 5G Non-Terrestrial Networks (NTN) adopt Orthogonal Frequency Division Multiplexing (OFDM) to enable integrated space-ground networks via Low Earth Orbit (LEO) satellite global connectivity. However, the rapid movement of LEO satellites induces a significant non-uniform Doppler shift across subcarriers, resulting in the signal bandwidth changes that leads to sampling point offsets and severely impacting demodulation performance. Traditional Doppler compensation algorithms focus mainly on addressing the uniform Carrier Frequency Offset (CFO) caused by crystal oscillation. In LEO satellite broadband communication systems, the Sampling Frequency Offset (SFO), caused by the non-uniform Doppler frequency shift, can be tens of times greater than the CFO, leading to non-negligible phase rotation, inter-carrier interference (ICI), and inter-symbol interference (ISI). Consequently, a low-complexity algorithm is required to address the fast time-varying SFO — one of the core challenges in these systems. In this paper, we derive a closed-form expression for the signal distortion and propose a model-driven compensation method that leverages the predictability of satellite trajectories. The proposed method effectively removes phase rotation and mitigates ICI and ISI through fast Fourier transform (FFT) window adjustment and phase rotation compensation. The accuracy of the model is validated through extensive simulations and a real satellite communication trial. Results demonstrate that the proposed method supports higher-order modulations, leading to an average spectral efficiency improvement of approximately 50%. This pioneering research promises to ensure robust performance in dynamic LEO satellite environments.

Due to the constraints of satellite payloads, traditional multi beam approaches often lead to the inefficient allocation of limited resources when addressing uneven traffic demands. One major trend noticed in satellite networks is the implementation of beam-hopping and full frequency multiplexing. It gives us flexibility in adjusting the time and power domains, enabling us to fulfil the uneven traffic demands and user latency performance requirements through appropriate beam-hopping pattern (BHP) scheduling and power allocation. This paper proposes a joint beam-hopping pattern scheduling and power allocation scheme for low earth orbit (LEO) satellite networks to address the uneven traffic demands while ensuring the latency performance requirements of users. The problem is formulated as maximizing the minimum traffic satisfaction rate by optimizing the BHPs and power allocation across time slots. To solve this mixed-integer non-convex problem, a two-step approach is adopted. For areas with high levels of traffic demand, the proposed joint beam-hopping and power allocation strategy delivers over 90% traffic satisfaction. When compared to optimization without beam-hopping, it increases total system throughput by 15%.

Satellite Internet, as an important component of the integrated space-ground information network, is a hot research hotspot nowadays. Many scholars have undertaken research in the areas of constellation networking design, network protocol design, and communication performance assessment, and their main research tool is software simulation. Traditional stand-alone network simulation simulators based on OPNET or NS3 are constrained in the simulation efficiency of mega satellite networks because of the limitations of computer hardware conditions and software performance. Based on the above characteristics, we propose a parallel network simulation architecture based on low correlation between different areas of the global satellite network, and in order to improve the parallel network simulation performance, the network topology needs to be divided effectively. Therefore, firstly we consider CPU and memory resource consumption as a measure of topology partitioning performance indicators, propose a resource assessment algorithm and use the result of this assessment as the topology partitioning optimization objective; secondly, we propose a load balancing based intelligent topology partitioning algorithm (LBTP); thirdly, we propose a time slice algorithm (TSA) for parallel simulation in each time cycle. To demonstrate the algorithm proposed in this paper, we built a simulation platform based on the combination of STK (Satellite Tool Kit), OPNET and Proxmox VE, and experimentally verified that the proposed architecture and algorithm significantly improve the simulation efficiency.

As the demand for high data capacity steadily increases, high-throughput satellites based on frequency reuse are becoming the focus of future research. Under the circumstance, it is attractive that multiple multi-beam satellites with wide coverage serving the same area simultaneously can improve the system throughput by transmitting more data streams to covered terminals. However, the increased inter-beam interference become the major obstacle for increasing the overall system throughput. Considering that beamforming techniques will increase satellite complexity and linear precoding techniques will significantly amplifies noise, this paper focuses on the Tomlinson-Harashima precoding (THP) with a balanced performance and complexity in nonlinear precoding to mitigate inter-beam interference. Utilizing the feature that the performance of THP related to the ordering of data streams, this paper proposes a novel iterative optimization THP (IO-THP) based on iterative structure without modifying the existing THP structure. Based on the existing multi-branch THP (MB-THP), IO-THP configures the optimal transmission mode for each terminal through sorting and scheme selection strategies and iterative structure to obtain the optimal ordering of data streams. In addition, this paper develops IO-cTHP and IO-dTHP for two basic THP structures. The first one places the diagonal weighted filters at the terminals to reduce the amplification for noise and the second places the diagonal weighted filter at the base station to simplify terminals. Simulation results show IO-THP possess 4dB gain relative to MB-THP and more than 10 dB gain relative to conventional ZF, MMSE and THP.

As an important complement to sixth-generation (6G) systems, low earth orbit (LEO) satellite will increase system capacity and improve service coverage. A critical question in LEO satellite systems is how to increase spectral efficiency. The existing primary method is the adaptive modulation and coding (AMC) based on accurate channel state information (CSI). However, the long-time delay of LEO satellite will lead to outdated CSI. Existing works usually predicates the future CSI based on time series, which is usually influenced by the constriction on satellite payload, such as nonlinear distortion caused by a high-power amplifier (HPA). In this work, we investigate the effects of predicting CSI using an improved time series prediction model in order to resolve the above problems. The simulation results verify the accuracy of the improved model. Compared to the commonly-used model, the performance of the improved model has a significant increase in spectral efficiency.

As one of the next-generation mobile network visions, the Space-Air-Ground integrated network is an inevitable trend of the future network, and research on heterogeneous network switching algorithms under the Space-Air-Ground integrated network becomes more important. Existing heterogeneous network switching algorithms usually use fixed weights of attribute to make decisions, but when the single or multiple attributes of multiple networks are too different, users will be connected to the same network. In the Space-Air-Ground integrated network, large differences in networks between heterogeneous networks, such as delay, will result in excessive load on a single network. In this paper, we proposed the Monte Carlo-Markov decision process (MC-MDP) algorithm to balance the network load of multiple networks. It can dynamically adjust the access networks of users in the system while considering the user's service requirements and network differences. Monte Carlo method is used to improve the convergence speed of the Markov decision process (MDP) algorithm. Numerical results confirm the MCMDP can improve the bandwidth resource utilization efficiency of the heterogeneous network and the convergence speed of the MDP algorithm.

With centralized baseband unit (BBU) pool, Cloud-RAN (C-RAN) has powerful computation capability to support jointly processing signals for several remote radio heads (RRHs) simultaneously. It raises a challenge to fully utilize the computation resources and meet the hard deadline constraint posed by multi-media traffics. To overcome this challenge, in this work a real-time partitioned scheduling algorithm called Wi-EDF has been proposed. In order to improve the efficiency of Wi-EDF, a cross layer scheduling framework is firstly presented. Based on that, Wi-EDF is carrying out in a two level scheduling manner. In the first level, a beamforming based scheduling problem is formulated, which aims to minimize the system power consumption with the constraint of fronthual capacity. The problem can be iterative solved by the equivalent second order cone program (SOCP) problems. In the second level, a preemptive earliest deadline first (EDF) based scheduling algorithm with admission control has been designed. The processing time deduced by the baseband signaling has been taken into account in this level. The simulation results show that Wi-EDF has lower power consumption, higher CPU utilization and higher overall throughput comparing with LTE-A.

With the explosive growth of smart devices and mobile data traffic, limited fronthaul capacity has become a notable bottleneck of green communication access networks, such as cloud radio access networks(C-RANs). In this paper, we proposed a joint user association and downlink beamforming scheme for green C-RANs to minimize the network power consumption with the limited fronthaul links. We first formulate the design problem as a mixed integer nonlinear programming (MINLP), and then transformed the MINLP problem into a mixed integer second-order cone programming (MI-SOCP) which is a convex programming when the integer variables are fixed. By relaxing the integer variables to continuous ones, an inflation algorithm, which can be finished within polynomial time, was proposed to solve the problem. The simulation results are presented to validate the effectiveness of our proposed algorithm compared with the the scheme adopted by LTE-A.

Nowadays, in Massive MIMO Relay Networks (MM-RNs), delay jitter decrease the video quality of users when they enjoy the Instant Video Communication (IVC) service. Cache is a promising technology to deal with the problem. In this paper, we study a high video quality guarantee problem. To mitigate the jitter, caching is applied to adjust the rate of transmitting side to adapt the rate of receiving side. The objective of this problem is to guarantee high video quality for IVC users. We derive a novel expressions for video quality with fairness in consideration. We also deduce the expression of delay and obtain a limit for the video frame queuing in cache. Since the problem is convex, we solve it by employing the convex optimization methods. Based on bisection searching, a Video Quality Guarantee (VQG) algorithm is designed to achieve the methods. Various simulations are shown to demonstrate the effectiveness of our proposed algorithm.

Ultra-dense network (UDN) has been considered as one of the advanced technologies in 5G. With the increasing requirements on quality of service (QoS), large numbers of connections and very high area throughput density, various access points (APs) are deployed densely to provide desired services to user terminals. Then how to choose the optimal AP among neighbor candidate APs for accessing to the network is an interesting problem. Meanwhile, some research work has pointed out that the backhaul capability may become the bottleneck for UDN. Therefore, the backhaul links should be considered when making the accessing decision. However, there is few well recognized solution so far. In this paper, we investigate the energy-efficient access scheme in UDN, with joint consideration on backhauling capability. On the basis of the proposed UDN architecture, an access evaluation model is set up with flexible weight adjustment on delay and power consumption of the candidate AP's backhaul links. Then, the resource allocation problem is formulated with joint scheduling on time slot, subchannel and power to optimize system energy efficiency. Simulation results have proven that compared with traditional access algorithm, our proposed scheme has obvious improvement in the performance.

Recent studies have investigated a novel software paradigm that enables program decomposition and dynamic partitioning for cloud gaming system. Among the challenges for the proposed software architecture, to design and implement the decomposed software, which follows the platform's specification, is most critical in practice. In this work, we investigate a fundamental application in gaming scenario, the 3D skeletal game engine, which is often used to animate avatars. A graphics demo program is developed to demonstrate the feasibility of proposed system, while experiments have been conducted to show the efficiency of cognitive resource allocation. We also made contributions to seeking the appropriate design pattern in developing programs on our platform. In addition, we provide future visions on alternative implementations of some specific parts, which dedicates on improving the platform's compatibility.

To enjoy rich Internet services, contemporary mobile devices generate heavy loads of computation tasks, which cannot be suffered locally due to the limited computing and energy capacities of each device. Cloud radio access network (Cloud RNA) based heterogeneous structure is widely accepted as a promising way to make up devices' shortage and computation need. To determine whether the computation task should be migrated from the device to the Cloud RAN, traditional work usually aims to improve resource utilization efficiency and constrain the latency of computation task. However, less concerned on the energy efficiency problem for the device. Through maintaining higher energy efficiency, device can utilize the limited energy in a reasonable way. In this paper, we formulate an energy efficient resource allocation problem for the computation tasks in Cloud-RAN based heterogeneous Network (Cloud-RAN HetNet). Within this problem, two subproblems are proposed. The first is determining whether computation task on the device should be offloaded to the the Cloud RAN. The second is energy efficiency based resource allocation while considering latency limitation for the computation tasks. To solve the proposed problem, a computation location selection and resource allocation (CCSRA) method is put forward. Finally, we utilize Various simulation results show the property of the strategy.

Wireless Sensor Networks (WSNs) have been widely deployed in monitoring and surveillance areas. With the improvement of sensor nodes' processing capability, multi-media transmission in WSNs has become a new trend. Since multi-media has strict Quality of Service (QoS) requirements, which calls for efficient scheduling algorithm that could guarantee the end-to-end delay and reduce the energy consumption. Therefore, in this paper we propose a utility-based scheduling algorithm that could enhance the energy efficiency and meet the QoS requirements of multimedia. Our proposed scheduling algorithm includes two steps: firstly, a semi-Markov model is deployed to predict the arrival rate of multi-media; secondly, a utility function is designed which based on the consideration of energy consumption and the QoS constraints. Simulations show that, compared to conventional algorithms, our proposed scheduling algorithm performs better in terms of packet loss rate due to buffer overflow and end-to-end delay.

This paper discusses the problem of minimizing the energy used to transmit packets over a memoryless channel, with a constraint on the delay suffered by packets and a constraint on peak transmitter power. The problem is studied within discontinuous reception (DRX) supported system. Specifically, we seek a DRX-compatible transmission policy that can solve the problem without the knowledge of fading distribution and packet arrival process. To achieve this goal, we formulate the problem as a variation of finite horizon classical secretary problem (CSP) with arbitrary monotonic utility. We present key structure property of optimal solution, and utilize it to obtain the optimal policy which can be described as a threshold rule. Moreover, we also propose an off-line calculation method to obtain the thresholds with low computing complexity. With the help of the thresholds, the optimal policy can make transmission decision only based on the relative rank of channel state over present time slot and the inter-packet deadlines. The performance of the optimal transmission policy is studied via simulation. The results show that the optimal transmission policy can work efficiently in DRX-supported system with low packet drop rate and high energy efficiency.

This paper concentrates on the problem of how to schedule a great quantity of heterogeneous traffic for one mobile node in LTE. To solve such problem in High Speed Rail (HSR), we present a decoupled time/frequency domain packet scheduler based on the analysis of HSR MIMO channel properties. In time domain, a novel single-user adaptive EXP/PF algorithm is adopted. In frequency domain, a fast exhaustive search-based frequency resource allocation algorithm (FRAA) named SU-OP is proposed, which can utilize the scarce frequency resources with constraint of LTE practical limits. Furthermore, we also introduce an alternative FRAA named SU-Greedy which has lower complexity and could obtain similar performance while the SNR is not high. By our original calculation rule the complexity of both algorithms is reduced further and thus more practical. Based on the system simulation, we find that the proposed scheduler has better delay and throughput performance than the well-known joint time domain and frequency domain proportional fair (TD-FD-PF) scheduler under HSR scenario.