AG‐DPA: Assignment game–based dynamic power allocation in multibeam satellite systems

Liu, Shuaijun; Fan, Yuanyuan; Hu, Yuemei; Wang, Dapeng; Liu, Lixiang; Gao, Lei

doi:10.1002/sat.1310

Cited by 12 publications

(19 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, the numerical results obtained in a reference scenario are presented, comparing the performance of the proposed CNN approach with the PPA, AG-DPA, and classification algorithms [13], [23]. The selected reference scenario corresponds to a multibeam coverage for Europe and the Mediterranean basin with 82 beams, similar to the coverage currently provided by KA-SAT [36].…”

Section: Numerical Results and Analysismentioning

confidence: 99%

“…From (13), the offered capacity in the bth beam at time t, C b (t) is a function f 3 (•) on the power [3], beamwidth, and the bandwidth assigned to the bth beam at time t (P b (t), θ b (t), and BW b c (t), respectively). On the other hand, EIRP of the bth beam at instant t, EIRP b (t) is a function f 4 (•) on the power and beamwidth assigned to the bth beam at time t (14) [3].…”

Section: Drm Cost Functionmentioning

confidence: 99%

“…The normalization parameter allows the comparison of the different DRM algorithms performance when used in different traffic demand scenarios. The second KPI is the power saving [13], defined as…”

Section: Performance Evaluationmentioning

confidence: 99%

“…While it may seem feasible to achieve a solution to this problem through optimization techniques, on a larger scale, the number of resources to be managed, the constraints coming from the system and the infinite number of traffic demand situations may lead to a problem that cannot be solved by conventional techniques [12]. To solve this problem, Liu et al [13] suggested an assignment game-based dynamic power allocation (AG-DPA) to achieve suboptimal low complexity in multibeam satellite systems. The authors compare the results obtained with a proportional power allocation (PPA) algorithm, obtaining a remarkable advantage in terms of power saving; however, the management of resources is still insufficient for the required demand.…”

Section: Introductionmentioning

confidence: 99%

“…4) In this article, a comparison is made between different flexible payloads according to the three previously identified resources. The CNN algorithm performance is compared with three other algorithms: PPA [13], AG-DPA [13], and classification algorithm [23].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Ortíz

Tarchi

Martínez

et al. 2021

IEEE Systems Journal

View full text Add to dashboard Cite

Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the nonuniform user distribution, and the changing traffic demand during the day. This problem is addressed using flexible payload architectures, enabling the allocation of the payload resources in a flexible manner to meet traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to the VHTS systems, which is why in this article, we are analyzing the use of convolutional neural networks (CNNs) to manage the resources available in flexible payload architectures for DRM. The VHTS system model is first outlined, for introducing the DRM problem statement and the CNN-based solution. A comparison between different payload architectures is performed in terms of DRM response, and the CNN algorithm performance is compared by three other algorithms, previously suggested in the literature to demonstrate the effectiveness of the suggested approach and to examine all the challenges involved.

show abstract

Section: Numerical Results and Analysismentioning

confidence: 99%

Section: Drm Cost Functionmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Ortíz

Tarchi

Martínez

et al. 2021

IEEE Systems Journal

View full text Add to dashboard Cite

show abstract

An online power allocation algorithm based on deep reinforcement learning in multibeam satellite systems

Zhang

Wang

et al. 2020

Satell Commun Network

View full text Add to dashboard Cite

SummaryDynamic power allocation (DPA) is the key technique to improve the system throughput by matching the offered capacity with that required among distributed beams in multibeam satellite systems. Existing power allocation studies tend to adopt the metaheuristic optimization algorithms such as the genetic algorithm. The achieved DPA cannot adapt to the dynamic environments due to the varying traffic demands and the channel conditions. To solve this problem, an online algorithm named deep reinforcement learning‐based dynamic power allocation (DRL‐DPA) algorithm is proposed in this paper. The key idea of the proposed DRL‐DPA lies in the online power allocation decision making other than the offline way of the traditional metaheuristic methods. Simulation results show that the proposed DRL‐DPA algorithm can improve the system performance in terms of system throughput and power consumption in multibeam satellite systems.

show abstract

Supervised machine learning for power and bandwidth management in very high throughput satellite systems

Ortíz

Tarchi

Martínez

et al. 2021

Satell Commun Network

View full text Add to dashboard Cite

In the near future, Very High Throughput Satellite (VHTS) systems are expected to have a high increase in traffic demand. However, this increase will not be uniform over the service area and will be also dynamic. A solution to this problem is given by flexible payload architectures; however, they require that resource management is performed autonomously and with low latency. In this paper we propose the use of Supervised Machine Learning, in particular a Classification algorithm using a Neural Network, to manage the resources available in flexible payload architectures. Use cases are presented to demonstrate the effectiveness of the proposed approach and a discussion is made on all the challenges that are presented.

show abstract

AG‐DPA: Assignment game–based dynamic power allocation in multibeam satellite systems

Cited by 12 publications

References 12 publications

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

An online power allocation algorithm based on deep reinforcement learning in multibeam satellite systems

Supervised machine learning for power and bandwidth management in very high throughput satellite systems

Contact Info

Product

Resources

About