Resource Allocation for Cognitive Satellite Communications Downlink

Zuo, Peiliang; Peng, Tao; Linghu, Wangdan; Wang, Wenbo

doi:10.1109/access.2018.2882795

Cited by 18 publications

(15 citation statements)

References 26 publications

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“…Zhu et al [23] used multidimensional knapsack theory to minimize energy consumption while considering user's delay constraint. To improve the spectrum utilization of satellites, Zuo et al [24] jointly optimized allocation of time, spectrum, power, and beam and solved it by heuristic algorithm. Mai et al [25] used Stackelberg game method to reduce the transmission delay of remote sensing data.…”

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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“…Reference [22] proposes a beam-hopping algorithm, which adjusts the beam size according to the business distribution. Reference [23] uses a heuristic algorithm to achieve frequency band selection and beam allocation and adopts Lagrangian dual algorithm and water-filling-assisted Lagrangian dual algorithm to achieve power allocation. Reference [24] proposes a channel allocation scheme of mixed random access and on-demand access, which reduces system delay within the throughput threshold.…”

Section: Leo Satellite Resource Allocation Schemementioning

confidence: 99%

LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning

Zheng

Zhou

et al. 2020

Mobile Information Systems

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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.

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“…In [12] and [13], the beam control and beamforming techniques were adopted respectively to maximize the signal to interference plus noise ratio (SINR) towards the desired secondary system and to minimize the interference towards the primary system. In the secondary category, spectrum sharing between broadcasting satellite service (BSS) feeder links and fixed satellite service (FSS) downlinks can be based on a simple coordination mechanism by defining cognitive (protection) zones around the BSS stations [14]- [16] or a beam-hopping (BH) scheme [17]. In [18], a BH scheme-based dual satellite coexistence scenario was proposed, in which the power control method and the exclusion zone method were applied to avoid producing harmful interference to the primary system.…”

Section: Introductionmentioning

confidence: 99%

“…Sharma et al [18] presented cognitive BH for spectral coexistence of two GEO multibeam satellites, and they did not consider the traffic demand distribution too. Zuo et al [17] presented two 4-D (time, frequency, power, and dedicated spot beam) RA schemes in the cognitive satellite system, but it was not in the GEO/LEO scenario.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for LEO Beam-Hopping Satellites in a Spectrum Sharing Scenario

Tang

Bian

et al. 2021

IEEE Access

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In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite's spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.

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Resource Allocation for Cognitive Satellite Communications Downlink

Cited by 18 publications

References 26 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

LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning

Resource Allocation for LEO Beam-Hopping Satellites in a Spectrum Sharing Scenario

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