Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

Sun, Yunyu; Wang, Ye; Jiao, Jian; Wu, Shaohua; Zhang, Qinyu

doi:10.1109/access.2019.2926426

Cited by 46 publications

(19 citation statements)

References 28 publications

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“…Joint beamforming design and resource allocation for terrestrial-satellite cooperative systems is investigated in [15]. Deep learning based long-term power allocation is analyzed in [16] for NOMA downlink in SAT-IoT networks. However, these literatures mainly focus on the management of communication resource, whilst the joint computing and communication resource management, which will affect the latency and QoS in SAT-IoT networks, is not taken into consideration.…”

Section: A Related Workmentioning

confidence: 99%

Latency and Energy Optimization for MEC Enhanced SAT-IoT Networks

Cui

et al. 2020

IEEE Access

119

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Mobile edge computing (MEC) enhanced satellite based internet of things (SAT-IoT) is an important complement for terrestrial networks based IoT, especially for the remote and depopulated areas. For MEC enhanced SAT-IoT networks with multiple satellites and multiple satellite gateways, the coupled user association, offloading decision, computing and communication resource allocation should be jointly optimized to minimize the latency and energy cost. In this paper, the latency and energy optimization for MEC enhanced SAT-IoT networks are formulated as a dynamic mixed-integer programming problem, which is hard to obtain the optimal solutions. To tackle this problem, we decompose the complex problem into two sub-problems. The first one is computing and communication resource allocation with fixed user association and offloading decision, and the second one is joint user association and offloading with optimal resource allocation. For the sub-problem of resource allocation, the optimal solution is proven to be obtained based on Lagrange multiplier method. And then, the second sub-problem is further formulated as a Markov decision process (MDP), and a joint user association and offloading decision with optimal resource allocation (JUAOD-ORA) is proposed based on deep reinforcement learning (DRL). Simulation results show that the proposed approach can achieve better long-term reward in terms of latency and energy cost. INDEX TERMS Latency and energy optimization, MEC, SAT-IoT, deep reinforcement learning.

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Section: A Related Workmentioning

confidence: 99%

Latency and Energy Optimization for MEC Enhanced SAT-IoT Networks

Cui

et al. 2020

IEEE Access

119

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“…To enable massive access in S-IoT, a NOMA downlink system was considered in [103] involving a satellite source node and multiple ground nodes, where the nodes in the same spot beam coverage synchronously shared the same frequency. For this system, a DL-based adaptive moment estimation (Adam) algorithm was developed to jointly optimize the decoding order of SIC and the long-term power allocation.…”

Section: Spaceborne-based Intelligent Iiotmentioning

confidence: 99%

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

Michailidis

Potirakis

Kanatas

2020

IoT

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During the last few years, various Industrial Internet of Things (IIoT) applications have emerged with numerous network elements interconnected using wired and wireless communication technologies and equipped with strategically placed sensors and actuators. This paper justifies why non-terrestrial networks (NTNs) will bring the IIoT vision closer to reality by providing improved data acquisition and massive connectivity to sensor fields in large and remote areas. NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services. Additionally, this paper describes indicative delay-tolerant massive IIoT and delay-sensitive mission-critical IIoT applications spanning a large number of vertical markets with diverse and stringent requirements. As the heterogeneous nature of NTNs and the complex and dynamic communications scenarios lead to uncertainty and a high degree of variability, conventional wireless communication technologies cannot sufficiently support ultra-reliable and low-latency communications (URLLC) and offer ubiquitous and uninterrupted interconnectivity. In this regard, this paper sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in the provision of challenging NTN-based IIoT services and provides a thorough review of the relevant research works. By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics with significantly lower complexity compared to typical optimization methods.

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“…The framework of DL-PA scheme. 49 DL-PA: deep-learning-based long-term power allocation. signal (SCS), ACS has better auto-correlation and better cross-relation in time domain and frequency domain.…”

Section: Large Dynamic Channelmentioning

confidence: 99%

A review on non-terrestrial wireless technologies for Smart City Internet of Things

Wang

Miao

et al. 2020

International Journal of Distributed Sensor Networks

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Smart City Internet of Things will become a fundamental infrastructure to support massive machine-type communications between the widely deployed sensors serving big cities. Since there exists many location constraints for the existing terrestrial Internet of Things, the non-terrestrial Internet of Things sheds light on breaking these limits. Therefore, this article conducts a comprehensive survey on non-terrestrial Internet of Things technologies for Smart City, which is an important complement to terrestrial Internet of Things. We first present the application scenarios of Internet of Things and point out where the existing terrestrial Internet of Things cannot work perfectly. Two non-terrestrial Internet of Things technical proposals are then introduced, namely satellite Internet of Things and unmanned aerial vehicle Internet of Things. However, the focuses of these non-terrestrial Internet of Things are distinct, that is, the major problems of satellite and unmanned aerial vehicle Internet of Things are the high dynamic nature of channel and high maneuverability of unmanned aerial vehicles, respectively. The key technologies for satellite and unmanned aerial vehicle Internet of Things are then reviewed separately. Both physical and non-physical layer technologies are surveyed for satellite Internet of Things, and the route planning is mainly investigated for the unmanned aerial vehicle Internet of Things. Finally, we draw a conclusion and give some potential research directions of non-terrestrial Internet of Things.

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Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

Cited by 46 publications

References 28 publications

Latency and Energy Optimization for MEC Enhanced SAT-IoT Networks

Latency and Energy Optimization for MEC Enhanced SAT-IoT Networks

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

A review on non-terrestrial wireless technologies for Smart City Internet of Things

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