RDRL: A Recurrent Deep Reinforcement Learning Scheme for Dynamic Spectrum Access in Reconfigurable Wireless Networks

Chen, Miaojiang; Liu, Anfeng; Liu, Wei; Ota, Kaoru; Dong, Mianxiong; Xiong, Naixue

doi:10.1109/tnse.2021.3117565

Cited by 38 publications

(18 citation statements)

References 42 publications

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“…Based on the satellite time window characteristics, many researchers design algorithms to solve the scheduling problem of the visual time window constraints [12][13][14]. The resource scheduling process can generally be divided into task planning and resource allocation.…”

Section: Resource Scheduling Methods Based On Machine Learningmentioning

confidence: 99%

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Fan

Yang

et al. 2021

Mathematics

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To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.

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Section: Resource Scheduling Methods Based On Machine Learningmentioning

confidence: 99%

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Fan

Yang

et al. 2021

Mathematics

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“…With the help of spectrum sharing, the operational cost for the telecom operators will go down, and thereby users can experience better quality and high speeds at a reasonable cost. The advancement in deep reinforcement learning algorithms helps to ensure intelligent spectrum access for users [ 101 , 102 ]. Gateway placement: A judicious deployment of gateways helps to obtain the maximum data at CMCU from several motes in ISMs.…”

Section: Research Perspectivesmentioning

confidence: 99%

Communication Technologies for Interoperable Smart Microgrids in Urban Energy Community: A Broad Review of the State of the Art, Challenges, and Research Perspectives

Reddy

Kumar

Chakravarthi

2022

Sensors

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In modern urban energy communities, diverse natured loads (homes, schools, hospitals, malls, etc.) are situated in the same locality and have self-electricity generation/management facilities. The power systems of these individual buildings are called smart microgrids. Usually, their self-electricity generation is based on renewable energy sources, which are uncertain due to their environmental dependency. So, the consistency of self-energy generation throughout the day is not guaranteed; thus, the dependency on the central utility grid is continued. To solve this, researchers have recently started working on interoperable smart microgrids (ISMs) for urban communities. Here, a central monitoring and control station captures the energy generation/demand information of each microgrid and analyzes the availability/requirement, thereby executing the energy transactions among these ISMs. Such local energy exchanges among the ISMs reduce the issues with uncertain renewable energy and the dependency on the utility grid. To establish such useful ISMs, a well-established communication mechanism has to be adopted. In this view, this paper first reviews various state-of-the-art developments related to smart grids and then provides extensive insights into communication standards and technologies, issues/challenges, and future research perspectives for ISM implementation. Finally, a discussion is presented on advanced wireless technology, called LoRa (Long Range), and a modern architecture using the LoRa technology to establish a communication network for ISMs is proposed.

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“…[7] proposed a game-based deep reinforcement learning method, which is effective in optimizing the energy consumption problem of MEC systems. [8] proposed a recurrent deep reinforcement learning method for solving the control problem of spectrum access in wireless networks. Based on the inspiration from the above studies, we consider introducing reinforcement learning into our research.…”

Section: Introductionmentioning

confidence: 99%

Military Vehicle Object Detection Based on Hierarchical Feature Representation and Refined Localization

Ouyang

et al. 2022

IEEE Access

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Military vehicle object detection technology in complex environments is the basis for the implementation of reconnaissance and tracking tasks for weapons and equipment, and is of great significance for information and intelligent combat. In response to the poor performance of traditional detection algorithms in military vehicle detection, we propose a military vehicle detection method based on hierarchical feature representation and reinforcement learning refinement localization, referred to as MVODM. First, for the military vehicle detection task, we construct a reliable dataset MVD. Second, we design two strategies, hierarchical feature representation and reinforcement learning-based refinement localization, to improve the detector. The hierarchical feature representation strategy can help the detector select the feature representation layer suitable for the object scale, and the reinforcement learning-based refinement localization strategy can improve the accuracy of the object localization boxes. The combination of these two strategies can effectively improve the performance of the detector. Finally, the experimental results on the homemade dataset show that our proposed MVODM has excellent detection performance and can better accomplish the detection task of military vehicles.INDEX TERMS Military vehicle objects, object detection, reinforcement learning, hierarchical feature representation.

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RDRL: A Recurrent Deep Reinforcement Learning Scheme for Dynamic Spectrum Access in Reconfigurable Wireless Networks

Cited by 38 publications

References 42 publications

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Communication Technologies for Interoperable Smart Microgrids in Urban Energy Community: A Broad Review of the State of the Art, Challenges, and Research Perspectives

Military Vehicle Object Detection Based on Hierarchical Feature Representation and Refined Localization

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