A Dynamic Channel Reservation Strategy Based on Priorities of Multi-Traffic and Multi-User in LEO Satellite Networks

2019 ◽  
Vol 29 (05) ◽  
pp. 2050082
Author(s):  
Juan Wang ◽  
Lijuan Sun ◽  
Jian Zhou ◽  
Chong Han
Keyword(s):  
Channel Allocation ◽  
Low Earth Orbit ◽  
Satellite Networks ◽  
Limited Resources ◽  
Allocation Model ◽  
Channel Reservation ◽  
Leo Satellite Networks ◽  
Dynamic Channel ◽  
Leo Satellite

In Low Earth Orbit (LEO) satellite networks, it is a challenge to allocate the limited resources to meet the needs of different calls. In this paper, a dynamic channel reservation strategy based on priorities of multi-traffic and multi-user in LEO satellite networks is proposed. The dynamic admission threshold reserved for different calls is the key of this strategy. Firstly, the traffic prediction model based on LEO satellite mobility is established. Then the channel allocation model is built on the Markov process. Finally, the reserved admission thresholds are dynamically changed according to the predicted traffic. And the calculation of the admission thresholds is solved by the genetic algorithm. The simulation results show that the proposed strategy not only meets the needs of calls of different type traffic and different level users, but also improves the overall quality of service in LEO satellite networks.

scholarly journals LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Fei Zheng ◽  
Zhao Pi ◽  
Zou Zhou ◽  
Kaixuan Wang
Keyword(s):  
Reinforcement Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Channel Allocation ◽  
Low Earth Orbit ◽  
Allocation Scheme ◽  
Q Learning ◽  
Channel Scheduling ◽  
Dynamic Channel ◽  
Leo Satellite

Delay, cost, and loss are low in Low Earth Orbit (LEO) satellite networks, which play a pivotal role in channel allocation in global mobile communication system. Due to nonuniform distribution of users, the existing channel allocation schemes cannot adapt to load differences between beams. On the basis of the satellite resource pool, this paper proposes a network architecture of LEO satellite that utilizes a centralized resource pool and designs a combination allocation of fixed channel preallocation and dynamic channel scheduling. The dynamic channel scheduling can allocate or recycle free channels according to service requirements. The Q-Learning algorithm in reinforcement learning meets channel requirements between beams. Furthermore, the exponential gradient descent and information intensity updating accelerate the convergence speed of the Q-Learning algorithm. The simulation results show that the proposed scheme improves the system supply-demand ratio by 14%, compared with the fixed channel allocation (FCA) scheme and by 18%, compared with the Lagrange algorithm channel allocation (LACA) scheme. The results also demonstrate that our allocation scheme can exploit channel resources effectively.

scholarly journals A Two-Hops State-Aware Routing Strategy Based on Deep Reinforcement Learning for LEO Satellite Networks

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 920 ◽  
Author(s):  
Cheng Wang ◽  
Huiwen Wang ◽  
Weidong Wang
Keyword(s):  
Reinforcement Learning ◽  
Low Earth Orbit ◽  
Satellite Networks ◽  
Traffic Load ◽  
Network Simulator ◽  
Link State ◽  
Routing Strategy ◽  
Leo Satellite Networks ◽  
Network Status ◽  
Leo Satellite

Low Earth Orbit (LEO) satellite networks can provide complete connectivity and worldwide data transmission capability for the internet of things. However, arbitrary flow arrival and uneven traffic load among areas bring about unbalanced traffic distribution over the LEO constellation. Therefore, the routing strategy in LEO networks should have the ability to adjust routing paths based on changes in network status adaptively. In this paper, we propose a Two-Hops State-Aware Routing Strategy Based on Deep Reinforcement Learning (DRL-THSA) for LEO satellite networks. In this strategy, each node only needs to obtain the link state within the range of two-hop neighbors, and the optimal next-hop node can be output. The link state is divided into three levels, and the traffic forwarding strategy for each level is proposed, which allows DRL-THSA to cope with link outage or congestion. The Double-Deep Q Network (DDQN) is proposed in DRL-THSA to figure out the optional next hop by inputting the two-hops link states. The DDQN is analyzed from three aspects: model setting, training process and running process. The effectiveness of DRL-THSA, in terms of end-to-end delay, throughput, and packet drop rate, is verified via a set of simulations using the Network Simulator 3 (NS3).

scholarly journals Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2230
Author(s):  
Tao Leng ◽  
Yuanyuan Xu ◽  
Gaofeng Cui ◽  
Weidong Wang
Keyword(s):  
Failure Rate ◽  
High Mobility ◽  
Low Earth Orbit ◽  
Satellite Networks ◽  
Call Blocking ◽  
Communication Services ◽  
Satellite Systems ◽  
Leo Satellite Networks ◽  
Scenarios Simulation ◽  
Leo Satellite

Recently, many Low Earth Orbit (LEO) satellite networks are being implemented to provide seamless communication services for global users. Since the high mobility of LEO satellites, handover strategy has become one of the most important topics for LEO satellite systems. However, the limited on-board caching resource of satellites make it difficult to guarantee the handover performance. In this paper, we propose a multiple attributes decision handover strategy jointly considering three factors, which are caching capacity, remaining service time and the remaining idle channels of the satellites. Furthermore, a caching-aware intelligent handover strategy is given based on the deep reinforcement learning (DRL) to maximize the long-term benefits of the system. Compared with the traditional strategies, the proposed strategy reduces the handover failure rate by up to nearly 81% when the system caching occupancy reaches 90%, and it has a lower call blocking rate in high user arrival scenarios. Simulation results show that this strategy can effectively mitigate handover failure rate due to caching resource occupation, as well as flexibly allocate channel resources to reduce call blocking.

scholarly journals Otimização de Energia em uma Rede de Satélites LEO em Cenários de Alta Vazão

2021 ◽  
Author(s):  
Renata Do Nascimento Mota Macambira ◽  
Celso Barbosa Carvalho ◽  
José Ferreira de Rezende
Keyword(s):  
Residual Energy ◽  
Low Earth Orbit ◽  
Satellite Networks ◽  
Depth Of Discharge ◽  
Leo Satellite Networks ◽  
Average Residual ◽  
Useful Life ◽  
Routing Methods ◽  
Leo Satellite ◽  
Energy Routing

Low Earth Orbit (LEO) satellites, when exposed to the sun, use solar energy for operation, processing, and communication, and with excess energy they charge their batteries. However, when satellites are in an area with no sunlight, called eclipse areas, they operate using only their battery power. The batteries have limitations on the amount of recharges/discharges, also known as the depth of discharge (DOD) cycle. Therefore, this restricts the useful life of the batteries themselves and also of the satellites.In this paper, we propose two different efficient routing methods for LEO satellite networks, which optimize traffic in order to reduce the DOD of satellites. We improved the Energy and Capacity Aware Routing (ECARS) metric, existing in the literature, by adding the Energy Routing prUning ( DOD and Energy Routing penAlty ( DOD methods. These proposed methods prune and penalize, respectively, the links whose satellites have reached a certain minimum battery charge threshold. With this procedure, we avoid over discharging the satellites’ battery, and thus, the lifetime is extended.Simulations results show that ERU DOD and ERA DOD can increase the satellites’ batteries lifetime by more than 54% and 10%, respectively. Moreover, the average residual energy obtained when comparing our ERU DOD and ERA DOD proposals with the ECARS proposal, resulted in gains greater than 113% and 29%, respectively.

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