With the escalating demand of users for broadband data and real-time interactive services, both the number of loworbit satellites launched into space and the number of terminals connected to satellite networks have increased significantly. However, due to the inherent time-varying uplink traffic fluctuations of satellite terminals, the traditional pre-allocation mechanism with fixed bandwidth mentioned in DVB-S2X standards for satellite uplink communication causes resource inefficiency and significant spectrum waste. To address this challenge, most existing works have proposed dynamic resource scheduling methods, but these studies ignore the long transmission delay between the Network Operation Control Center (NOCC) and satellite terminals, which implies that the traffic of satellite terminal is regarded as a constant variable rather than a random variable. In this article, a delay scheduling problem with random user traffic is studied, and the online algorithms for dynamic resource sharing mechanism based on Online Convex Optimization (OCO) theory with dynamic regret and constraint violations are proposed. In addition, the impact of user traffic fluctuations on the online algorithms is discussed. Simulation results show that the online algorithms with random user traffic can effectively decrease online decision errors and improve uplink spectrum utilization.

With the emergence of mobile edge applications and the demand for access-as-a-service, satellite mobile edge computing stands out as a disruptive technology for delivering low-latency edge service. In this article, we focus on service deployment to the edge satellites for terrestrial users, which is a key enabling technology in satellite mobile edge computing and will replace traditional centralized cloud computing. Most existing works on service deployment consider a centralized nonconvex optimization problem with high computational overhead. However, in practice, it is difficult for a single satellite to solve computationally expensive network optimization problems. To this end, we propose a distributed optimization model based on the alternating direction method of multipliers (ADMMs), which can relieve the computational burden by leveraging collaborative calculations among multiple satellites. Our proposed model minimizes the total delay of service deployment for terrestrial users by formulating a joint optimization problem that involves deployment decisions, CPU resource decisions, transmission decisions, and caching decisions. Furthermore, we propose a novel approximation method that transforms the nonconvex optimization problem to a convex one to make the joint optimization problem solvable in polynomial time. Finally, we conduct experiments using scaled global population data and show that the proposed distributed model outperforms the baselines.

The integrated satellite-terrestrial network with cascaded downlinks from satellites to wide-area mobile base stations and subsequently to terrestrial users enables global communication for terrestrial 4G/5G cellular users and is widely used in emergency rescue scenarios. However, in this network, satellites and wide-area mobile base stations are controlled by distinct resource scheduling systems with disparate packet queues, which means resources allocated by the satellite to the wide-area mobile base stations may not match the resources allocated by the wide-area mobile base stations to the terrestrial users, leading to coordination inefficiencies and resource wastage. To tackle this challenge, a resource collaborative scheduling mechanism based on cooperative game theory for cascaded downlinks is established, which effectively adapts to distinct resource scheduling systems with various QoS constraints. Then, the utility function of the Nash product is converted into a max-min problem, and a convex transformation method is proposed for the non-convex optimization problem. Simulation results demonstrate that the proposed collaborative scheduling mechanism effectively improves resource utilization and the transmission rate of cascaded downlinks.

With the development of space-based networks, the disadvantage of traditional centralized ground control including low invulnerability and high propagation delay cannot be ignored, and the capabilities of distributed resource management and control become more and more important. Although there are many distributed resource allocation methods, the computational complexity of them is too heavy with expensive communication costs and frequent parameter interaction causing a heavy burden to satellite systems. To solve such problems, we proposed a lightweight distributed satellite resource allocation method based on contract theory with one-time interaction. In our contract mechanism, a two-sided relationship is considered between resource requesters and resource providers. Satellites only need to sign a communication contract including a combination of the CPU resources that providers could offer and the payments that requesters could pay. Simulation results show that the proposed contract mechanism is feasible and the performance of our mechanism is better than the baseline mechanism under our scenario.

With the emergence of more and more latency-sensitive applications and mobile devices pumped to the edge of network, the burden on the backhaul network is getting heavier and heavier due to the limited transmission resources. Mobile edge computing (MEC) considered as a promising technology becomes more and more popular, which can provide services at the edge of the network. In this paper, we take into account the mobility of users and focus on the problem of service migration. Most of existing works modeled a Markov Decision Process (MDP) model with a high-dimensional state space, and have to solve it by deep reinforcement learning. To tackle this issue, we propose a distributed two-layer decomposition model and generate a series of new MDP problem with Low-dimensional in order to replace original High-dimensional MDP. In our model, the size of state space is reduced from M2Nto N × M2 by decomposing the original optimization problem. Simulation results show that the performance of the proposed two-layer decomposition model is better than the baseline models.