Large-scale low-orbit (LEO) Satellite Networks have the characteristics of wide coverage and low delay, and have attracted a lot of attention. However, due to the fast moving speed of LEO satellites, the topology of LEO networks changes frequently. In order to improve the utilization of network resources and the speed of routing calculation, this paper proposes a dynamic routing method for large-scale low-orbit satellite networks based on multi-agent DQN location guided networks. With the training of a large amount of prior data, the proposed method can enable the network nodes to make routing decisions based only on the surrounding environment. In addition, the transmission domain partition scheme is proposed, which can accelerate DQN convergence by reducing the routing scope and decreasing the satellite nodes during training. As the traffic distribution of satellite networks is not uniform in reality, a queuing model based on population density distribution is established. The simulation results demonstrate that this method has better performance than the existing methods in terms of packet loss rate, and model convergence speed and can decrease the end-to-end latency.
The development of low-orbit (LEO) satellites has attracted wide attention from industry and academia. Due to the scarcity of spectrum resources, LEO satellites in spectrum use respect have to share spectrum with other systems in space. In order to alleviate the shortage of space spectrum resources, spectrum sharing has been widely paid attention. Focusing on the sharing of spectrum between low-orbit (LEO) satellites and legacy geostationary orbit (GEO) satellites, a cognitive collaboration optimization method for sharing spectrum among LEO satellite groups and GEO satellite is proposed. At the expense of assisting in relaying information from GEO satellite, LEO satellite groups are granted the right to use the authorized spectrum of GEO satellite. With the minimum transmit rate of GEO satellite guaranteed and the transmit power threshold of each LEO satellite as the constraints, the optimization problem by optimizing forward matrix and precoding vector at each LEO satellite is established to maximize the minimum transmission rate in LEO satellite group. Considering established non-convex optimization problem, precoding vector and forward matrix optimization solving algorithm is proposed by jointly adopting bisection method, primal-contrapositive transform and primal-dual method. The proposed scheme is validated through numerical simulation. This paper provides a potential spectrum sharing method among GEO and LEO satellites to support theoretical basis and experimental data for parameter design, such as the number of LEO satellites participating in the cooperation. The cooperation performance and cooperative node selection in space time-dependent networks will be discussed in future research.
Recently, a series of researches have been emphasized on developing advanced satellite networks, mostly because of its advantage in providing spaced-based global communication service. But most of these work prefer to focus on the timevarying topologies, large delays and intermittent connections of satellite networks. However, there is another issue worthy of attentions, i.e., the scarcity and preciousness of satellite resources, owing to the shortage of orbit resources and the high cost of launching a satellite. Therefore, it is significantly important to consider the efficient utilization of resources during designing routing strategies for satellite networks. In this paper, we propose two routing algorithms to optimize the number of used inter-satellite links, which will directly improve the bandwidth utilization and save resources for LEO satellite networks. The basic idea is to reduce the number of links used by lower-priority traffic through scheduling them to links used by highest-priority services, and simultaneously introduce the load balancing strategies to control the aggregation of network flow. Simulation results show that with the price of little longer latency and load unbalancing, our algorithms can effectively decrease the total number of used links, and thus improve the resource utilization and save energy for satellite networks.
The continuously bandwidth-tunable pulse generation in the SWNT mode-locked fiber laser is achieved by only tuning the intracavity polarization state. By introducing the in-line polarizer with 2-meter-long polarization maintaining fiber pigtails in a typical ring fiber laser, a bandwidth-tunable SWNT mode-locked fiber laser is constructed. The mode locker is the single-wall carbon nanotube saturable absorber, which is fabricated by optical deposition in the ~0.27 w.t % ultrasonic carbon nanotube alcohol solution. By only tuning the intracavity polarization controllers, the spectral bandwidth is continuously tuned in the range of 0.94 to 3.04 nm. We attribute the upper limit of the spectral bandwidth to the limit of the free spectral range determined by Lyot filter, which consists of polarization controllers and in-linepolarizer in the cavity. These results provide a simple way to achieve bandwidth-tunable subpicosecond pulse, which should be attractive to the applications requiring ultrafast sources with tunable bandwidth or pulsewidth.
We designed and implemented a dynamic simulation platform for software defined optical satellite networking. It can simulate all nodes in satellite network on one computer. It can refresh the network in real time in a highly dynamic and complex environment, including changes in the connection between nodes caused by the dynamic periodic motion of the satellite and changes in link quality, etc. In addition, the platform can apply other algorithms in the simulation network so that the platform is practical and scalable. We also conducted the test of the small satellite constellation on this platform. The experimental results obtained are in line with expectations and reflects the practical capabilities of the platform.
In this paper, the cognitive multi-beam satellite system, i.e., two satellite networks coexist through underlay spectrum sharing, is studied, and the power and spectrum allocation method is employed for interference control and throughput maximization. Specifically, the multi-beam satellite with flexible payload reuses the authorized spectrum of the primary satellite, adjusting its transmission band as well as power for each beam to limit its interference on the primary satellite below the prescribed threshold and maximize its own achievable rate. This power and spectrum allocation problem is formulated as a mixed nonconvex programming. For effective solving, we first introduce the concept of signal to leakage plus noise ratio (SLNR) to decouple multiple transmit power variables in the both objective and constraint, and then propose a heuristic algorithm to assign spectrum sub-bands. After that, a stepwise plus slice-wise algorithm is proposed to implement the discrete power allocation. Finally, simulation results show that adopting cognitive technology can improve spectrum efficiency of the satellite communication.
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