Implementation of a space communications cognitive engine

Hackett, Timothy M.; Bilén, Sven G.; Ferreira, Paulo Victor R.; Wyglinski, Alexander M.; Reinhart, Richard C.

doi:10.1109/ccaaw.2017.8001607

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…The aforementioned works mostly focus on simulations and numerical comparisons. With one step further, the authors in [146] implement a multi-objective DQL framework as the radio-resource-allocation controller for space communications. The implementation uses modular software architecture to encourage re-use and easy modification for different algorithms, which is integrated into the real spaceground system developed by NASA Glenn Research Center.…”

Section: B Resource Sharing and Schedulingmentioning

confidence: 99%

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,487

481

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This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

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Section: B Resource Sharing and Schedulingmentioning

confidence: 99%

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,487

481

View full text Add to dashboard Cite

show abstract

“…Optimal resource allocation is key network capacity, system enhancement and coverage. Resource allocation for radio access network and space communication by using deep Qlearning architecture are proposed in [382] and [383] respectively. Applications of RL on the use of network slicing for resource management are reviewed in [384].…”

Section: H Scheduling Management and Configuration Of Resourcesmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

146

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Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

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Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks

Huang

Gao

et al. 2018

Mobile Netw Appl

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Implementation of a space communications cognitive engine

Cited by 5 publications

References 10 publications

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks

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