Dual-DNN Assisted Optimization for Efficient Resource Scheduling in NOMA-Enabled Satellite Systems

Wang, Anyue; Lei, Lei; Lagunas, Eva; Chatzinotas, Symeon; Ottersten, Björn

doi:10.1109/globecom46510.2021.9685660

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…The authors analyze different ML techniques applied to systems with power, bandwidth, and/or beamwidth flexibility and systems with beam hopping capabilities. Furthermore, reference [14] proposes a combined learning and optimization approach to address a Mixed-Integer Convex Programming (MICP) problem in satellite RRM. The problem is decomposed into classification-like tasks and power control optimization, respectively solved by dual Deep Neural Networks (DNNs) and convex optimization.…”

Section: Related Work 1) Machine Learning For Rrm In Satcommentioning

confidence: 99%

Energy-Efficient on-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing

Ortiz,

Skatchkovsky,

Lagunas

et al. 2024

Trans. Mach. Learn. Comm. Netw.

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The latest Satellite Communication (SatCom) missions are characterized by a fully reconfigurable on-board software-defined payload, capable of adapting radio resources to the temporal and spatial variations of the system traffic. As pure optimization-based solutions have shown to be computationally tedious and to lack flexibility, Machine Learning (ML)-based methods have emerged as promising alternatives. We investigate the application of energy-efficient brain-inspired ML models for on-board radio resource management. Apart from software simulation, we report extensive experimental results leveraging the recently released Intel Loihi 2 chip. To benchmark the performance of the proposed model, we implement conventional Convolutional Neural Networks (CNN) on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy, precision, recall, and energy efficiency for different traffic demands. Most notably, for relevant workloads, Spiking Neural Networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100× as compared to the CNNbased reference platform. Our findings point to the significant potential of neuromorphic computing and SNNs in supporting on-board SatCom operations, paving the way for enhanced efficiency and sustainability in future SatCom systems.

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Section: Related Work 1) Machine Learning For Rrm In Satcommentioning

confidence: 99%

Energy-Efficient on-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing

Ortiz,

Skatchkovsky,

Lagunas

et al. 2024

Trans. Mach. Learn. Comm. Netw.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Regarding resource management at SatComs, the authors in [24] proposed a combined learning and optimization approach to address a mixed-integer convex programming problem (MICP) in satellite RRM. The complex MICP problem is decomposed into two classification-like tasks and a remaining power control problem.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems

et al. 2022

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Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and changes in traffic demand diurnal. This problem is addressed by using flexible payload architectures, which allow payload resources to be flexibly allocated to meet the traffic demand of each beam. While optimization-based radio resource management (RRM) has shown significant performance gains, its intense computational complexity limits its practical implementation in real systems. In this paper, we discuss the architecture, implementation and applications of Machine Learning (ML) for resource management in multibeam GEO satellite systems. We mainly focus on two systems, one with power, bandwidth, and/or beamwidth flexibility, and the second with time flexibility, i.e., beam hopping. We analyze and compare different ML techniques that have been proposed for these architectures, emphasizing the use of Supervised Learning (SL) and Reinforcement Learning (RL). To this end, we define whether training should be conducted online or offline based on the characteristics and requirements of each proposed ML technique and discuss the most appropriate system architecture and the advantages and disadvantages of each approach.

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“…There are more significant contributions regarding radio resource management (RRM) in SatCom. For example, in [11], the authors propose a combined learning and optimization approach to address a mixed-integer convex programming (MICP) problem in satellite RRM. Deng et al [12] suggest an innovative RRM framework for next-generation heterogeneous satellite networks (HSNs), which can cooperate between independent satellite systems and maximize resource utilization.…”

mentioning

confidence: 99%

Onboard Processing in Satellite Communications Using AI Accelerators

et al. 2023

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Satellite communication (SatCom) systems operations centers currently require high human intervention, which leads to increased operational expenditure (OPEX) and implicit latency in human action that causes degradation in the quality of service (QoS). Consequently, new SatCom systems leverage artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomy and control. Onboard processing for advanced AI/ML algorithms, especially deep learning algorithms, requires an improvement of several magnitudes in computing power compared to what is available with legacy, radiation-tolerant, space-grade processors in space vehicles today. The next generation of onboard AI/ML space processors will likely include a diverse landscape of heterogeneous systems. This manuscript identifies the key requirements for onboard AI/ML processing, defines a reference architecture, evaluates different use case scenarios, and assesses the hardware landscape for current and next-generation space AI processors.

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Dual-DNN Assisted Optimization for Efficient Resource Scheduling in NOMA-Enabled Satellite Systems

Cited by 10 publications

References 21 publications

Energy-Efficient on-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing

Energy-Efficient on-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing

Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems

Onboard Processing in Satellite Communications Using AI Accelerators

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