Energy-Efficient Data Offloading for Multi-Cell Satellite-Terrestrial Networks

Ji, Zhe; Wu, Sheng; Jiang, Chunxiao; Hu, Dongwei; Wang, Wenbo

doi:10.1109/lcomm.2020.3003671

Cited by 27 publications

(9 citation statements)

References 12 publications

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“…Wireless Communications and Mobile Computing proposed a load balancing-based resource management method for improving STINs' performance. Ji et al [28] presented a data offloading method for solving the energy overhead issue in multicell STINs. Deng et al [29] designed a satellite constellation deployment solution with minimum number of satellites while meeting backhaul requirements of users in STINs.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Joint Satellite Scheduling and Resource Allocation in Satellite-Terrestrial Integrated Networks

Yin

Huang

et al. 2022

Wireless Communications and Mobile Computing

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Satellite-terrestrial integrated networks (STINs) are considered to be a new paradigm for the next generation of global communication because of its distinctive merits, such as wide coverage, high reliability, and flexibility. When the satellite associates with different base stations (BSs) and adopts different channels for communication, the utility of offloading data to BSs is different. In our work, we study how to jointly associate satellites with appropriate BSs and allocate channels to satellites. Our purpose is to maximize the utility of the data offloaded from satellites to BSs while considering the load balance of BSs. However, some satellites are often unable to connect to BSs because of their periodic flight characteristic, which makes the joint satellite-BS association and channel allocation more challenging. To solve the problem that satellites sometimes cannot connect to BSs, we abstract the communication model between satellites and BSs into a bipartite graph and add a virtual BS to ensure that all satellites can connect to at least one BS. Then, in the constructed joint optimization problem, we solve the assignment of satellites and channels simultaneously. Considering that the joint optimization problem is nonconvex, we use double deep Q-Network (DDQN) for achieving the optimal strategy of satellite association and channel allocation. Furthermore, the reward value in most state transition information generated by satellites is 0, which leads to the low learning efficiency of DDQN. Aiming at enhancing the learning efficiency of DDQN, the priority sampling-based DDQN (PSDDQN) algorithm is proposed. Experimental results demonstrate that PSDDQN gets better utility and achieves the load balance of BSs compared with other algorithms.

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Joint Satellite Scheduling and Resource Allocation in Satellite-Terrestrial Integrated Networks

Yin

Huang

et al. 2022

Wireless Communications and Mobile Computing

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“…Several research efforts focused on the integrated satellite-terrestrial network (ISTN) and studied different aspects related to the traffic offloading problem [1,14,15,22,24,33,36,37]. ISTN utilizes the traffic offloading to maximize the number of accommodated users, and their rate under a dynamic backhaul capacity constraint [15].…”

Section: Ia Literature Reviewmentioning

confidence: 99%

“…In this regard, it is essential to explore different offloading locations in satellite mobile edge computing (SMEC), such as the terrestrial station, the low earth orbit (LEO) satellite, and the terrestrial gateway [37]. Moreover, it is also important to consider users' locations in different regions such as suburban and mountainous regions and choose the suitable performance metric such as sum rate, and energy efficiency [22]. Also, investigating the cost matching of traffic offloading to edge servers placed in the satellite network and the terrestrial network is crucial, as studied while considering the energy consumption and the offloading delay in [33].…”

Section: Ia Literature Reviewmentioning

confidence: 99%

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Proactive Traffic Offloading in Dynamic Integrated Multi-Satellite Terrestrial Networks

Abderrahim¹,

Amin²,

Alouini³

et al. 2022

Preprint

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The integration between the satellite network and the terrestrial network will play a key role in the upcoming sixth-generation (6G) of mobile cellular networks thanks to the wide coverage and bandwidth offered by satellite networks. To leverage this integration, we propose a proactive traffic offloading scheme in an integrated multi-satellite terrestrial network (IMSTN) that considers the future networks' heterogeneity and predicts their variability. Our proposed offloading scheme hinges on traffic prediction to answer the stringent requirements of data-rate, latency and reliability imposed by heterogeneous and coexisting services and traffic namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable low latency communication (URLLC). However, the fulfilment of these requirements during offloading in dynamic IMSTN comes at the expense of significant energy consumption and introduces inherently supplementary latency. Therefore, our offloading scheme aims to balance the fundamental trade-offs first between energy consumption and the achievable data-rate and second between energy consumption and latency while meeting the respective needs of the present traffic. Our findings prove the importance of the cooperation between the multi-satellite network and the terrestrial network conditioned by traffic prediction to enhance the performance of IMTSN in terms of latency and energy consumption.

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“…As a reliable communication paradigm, much work has been done on STINs. In order to improve the power utilization of satellites, the authors in [32] proposed a data offloading scheme in STINs to jointly allocate the power and resources of satellites. Zhang et al [33] used edge computing techniques to improve the QoS of STINs.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Reinforcement Learning‐Based Routing Algorithm in Satellite‐Terrestrial Integrated Networks

Yin

Huang

et al. 2021

Wireless Communications and Mobile Computing

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Satellite-terrestrial integrated network (STIN) is an indispensable component of the Next Generation Internet (NGI) due to its wide coverage, high flexibility, and seamless communication services. It uses the part of satellite network to provide communication services to the users who cannot communicate directly in terrestrial network. However, existing satellite routing algorithms ignore the users’ request resources and the states of the satellite network. Therefore, these algorithms cannot effectively manage network resources in routing, leading to the congestion of satellite network in advance. To solve this problem, we model the routing problem in satellite network as a finite-state Markov decision process and formulate it as a combinatorial optimization problem. Then, we put forth a Q-learning-based routing algorithm (QLRA). By maximizing users’ utility, our proposed QLRA algorithm is able to select the optimal paths according to the dynamic characteristics of satellite network. Considering that the convergence speed of QLRA is slow due to the routing loop or ping-pong effect in the process of routing, we propose a split-based speed-up convergence strategy and also design a speed-up Q-learning-based routing algorithm, termed SQLRA. In addition, we update the Q value of each node from back to front in the learning process, which further accelerate the convergence speed of SQLRA. Experimental results show that our improved routing algorithm SQLRA greatly enhances the performance of satellite network in terms of throughput, delay, and bit error rate compared with other routing algorithms.

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Energy-Efficient Data Offloading for Multi-Cell Satellite-Terrestrial Networks

Cited by 27 publications

References 12 publications

Deep Reinforcement Learning-Based Joint Satellite Scheduling and Resource Allocation in Satellite-Terrestrial Integrated Networks

Deep Reinforcement Learning-Based Joint Satellite Scheduling and Resource Allocation in Satellite-Terrestrial Integrated Networks

Proactive Traffic Offloading in Dynamic Integrated Multi-Satellite Terrestrial Networks

Reinforcement Learning‐Based Routing Algorithm in Satellite‐Terrestrial Integrated Networks

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