The low Earth orbit (LEO) satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment, which can provide a broader range of communication services and has become an essential supplement to the terrestrial network. However, the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing. Even worse, the harsh space environment often leads to incomplete collection of network state data for routing decision-making, which further complicates this challenge. To address this problem, this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm (SIDMRA) to maximize network efficiency even with incomplete state data as input. Specifically, we model the multipath routing problem as a markov decision process (MDP) and then combine the deep deterministic policy gradient (DDPG) and the $K$ shortest paths (KSP) algorithm to solve the optimal multipath routing policy. We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network (MPNN) for data enhancement. Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.
Low Earth orbit (LEO) satellite networks have the advantages of low transmission delay and low deployment cost, playing an important role in providing reliable services to ground users. This paper studies an efficient inter-satellite cooperative computation offloading (ICCO) algorithm for LEO satellite networks. Specifically, an ICCO system model is constructed, which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals, effectively improving resource utilization efficiency. Additionally, the optimization objective of minimizing the system task computation offloading delay and energy consumption is established, which is decoupled into two sub-problems. In terms of computational resource allocation, the convexity of the problem is proved through theoretical derivation, and the Lagrange multiplier method is used to obtain the optimal solution of computational resources. To deal with the task offloading decision, a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration. Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.
Low-earth-orbit (LEO) satellite network has become a critical component of the satellite-terrestrial integrated network (STIN) due to its superior signal quality and minimal communication latency. However, the highly dynamic nature of LEO satellites leads to limited and rapidly varying contact time between them and Earth stations (ESs), making it difficult to timely download massive communication and remote sensing data within the limited time window. To address this challenge in heterogeneous satellite networks with coexisting geostationary-earth-orbit (GEO) and LEO satellites, this paper proposes a dynamic collaborative inter-satellite data download strategy to optimize the long-term weighted energy consumption and data downloads within the constraints of on-board power, backlog stability and time-varying contact. Specifically, the Lyapunov optimization theory is applied to transform the long-term stochastic optimization problem, subject to time-varying contact time and on-board power constraints, into multiple deterministic single time slot problems, based on which online distributed algorithms are developed to enable each satellite to independently obtain the transmit power allocation and data processing decisions in closed-form. Finally, the simulation results demonstrate the superiority of the proposed scheme over benchmarks, e.g., achieving asymptotic optimality of the weighted energy consumption and data downloads, while maintaining stability of the on-board backlog.
The increasing demand for radio-authorized applications in the 6G era necessitates enhanced monitoring and management of radio resources, particularly for precise control over the electromagnetic environment. The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment. However, most existing algorithms rely on sparse measurements of radio strength, disregarding the impact of building information. In this paper, we propose a spectrum cartography (SC) algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters. Our algorithm leverages Pix2Pix Generative Adversarial Network (GAN) to construct accurate radio maps using transmitter information within real geographical environments. Finally, simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.
To support ubiquitous communication and enhance other 6G applications, the Space-Air-Ground Integrated Network (SAGIN) has become a research hotspot. Traditionally, satellite-ground fusion technologies integrate network entities from space, aerial, and terrestrial domains. However, they face challenges such as spectrum scarcity and inefficient satellite handover. This paper explores the Channel-Aware Handover Management (CAHM) strategy in SAGIN for data allocation. Specifically, CAHM utilizes the data receiving capability of Low Earth Orbit (LEO) satellites, considering satellite-ground distance, free-space path loss, and channel gain. Furthermore, CAHM assesses LEO satellite data forwarding capability using signal-to-noise ratio, link duration and buffer queue length. Then, CAHM applies historical data on LEO satellite transmission successes and failures to effectively reduce overall interruption ratio. Simulation results show that CAHM outperforms baseline algorithms in terms of delivery ratio, latency, and interruption ratio.
The rapid development of mega low earth orbit (LEO) satellite networks is expected to have a significant impact on 6G networks. Unlike terrestrial networks, due to the high-speed movement of satellites, users will frequently hand over between satellites even if their positions remain unchanged. Furthermore, the extensive coverage characteristic of satellites leads to massive users executing handovers simultaneously. To address these challenges, we propose a novel double grouping-based group handover scheme (DGGH) specifically tailored for mega LEO satellite networks. First, we develop a user grouping strategy based on beam-limited hierarchical clustering to divide users into distinct groups. Next, we reframe the challenge of managing multiple users' simultaneous handovers as a single-objective optimization problem, solving it with a satellite grouping strategy that leverages the accuracy of greedy algorithms and the simplicity of dynamic programming. Additionally, we develop a group handover algorithm based on minimal handover waiting time to improve the satellite grouping process further. The detailed steps of the DGGH scheme's handover procedure are meticulously outlined. Comprehensive simulations show that the proposed DGGH scheme outperforms single-user handover schemes in terms of handover signaling overhead and handover success rate.
Frequent extreme disasters have led to frequent large-scale power outages in recent years. To quickly restore power, it is necessary to understand the damage information of the distribution network accurately. However, the public network communication system is easily damaged after disasters, causing the operation center to lose control of the distribution network. In this paper, we considered using satellites to transmit the distribution network data and focus on the resource scheduling problem of the satellite emergency communication system for the distribution network. Specifically, this paper first formulates the satellite beam-pointing problem and the access-channel joint resource allocation problem. Then, this paper proposes the Priority-based Beam-pointing and Access-Channel joint optimization algorithm (PBAC), which uses convex optimization theory to solve the satellite beam pointing problem, and adopts the block coordinate descent method, Lagrangian dual method, and a greedy algorithm to solve the access-channel joint resource allocation problem, thereby obtaining the optimal resource scheduling scheme for the satellite network. Finally, this paper conducts comparative experiments with existing methods to verify the effectiveness of the proposed methods. The results show that the total weighted transmitted data of the proposed algorithm is increased by about 19.29$\sim$26.29% compared with other algorithms.
Codebooks have been indispensable for wireless communication standard since the first release of the Long-Term Evolution in 2009. They offer an efficient way to acquire the channel state information (CSI) for multiple antenna systems. Nowadays, a codebook is not limited to a set of pre-defined precoders, it refers to a CSI feedback framework, which is more and more sophisticated. In this paper, we review the codebooks in 5G New Radio (NR) standards. The codebook timeline and the evolution trend are shown. Each codebook is elaborated with its motivation, the corresponding feedback mechanism, and the format of the precoding matrix indicator. Some insights are given to help grasp the underlying reasons and intuitions of these codebooks. Finally, we point out some unresolved challenges of the codebooks for future evolution of the standards. In general, this paper provides a comprehensive review of the codebooks in 5G NR and aims to help researchers understand the CSI feedback schemes from a standard and industrial perspective.
