Uplink NOMA-based long-term throughput maximization scheme for cognitive radio networks: an actor–critic reinforcement learning approach

Giang, Hoang Thi Huong; Hoan, Tran Nhut Khai; Koo, Insoo

doi:10.1007/s11276-020-02520-y

Cited by 4 publications

(4 citation statements)

References 55 publications

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“…In addition, 5G cognitive-radio-enabled spectrum access technology can optimize the spectrum and energy efficiency, together with the secrecy of networks, by progressively adjusting its transmission parameters. An actor-critic reinforcement learning strategy can enhance the cognitive network prolonged throughput [11]: by interacting and assimilating knowledge from the environment across various time slots, the cognitive base station can exemplarily assign the volume of transmission energy for each secondary user in conformity with the residual energy and primary channel availability. As increasing demands for significant data rates overload standard radio frequency technologies [7], millimeter waves and cognitive radios can surmount the spectrum scarcity and capacity constraints of the standard radio frequency systems.…”

Section: Methodsmentioning

confidence: 99%

“…A game-based fair resource allocation algorithm can enable stable cooperation between primary users and secondary users [ 91 ] across wireless powered cooperative cognitive radio networks through streamlined resource allocation. Nonorthogonal multiple access can bring about spectrum efficiency [ 11 ] across wireless networks. In an uplink nonorthogonal multiple-access cognitive system, secondary users can collectively transfer data, throughout the same spectrum resources, to the cognitive base station, and uninterrupted interference discontinuation is applied to retrieve secondary-user-transmitted signals.…”

Section: Energy-harvesting Technology For Cognitive-radio-based Iot N...mentioning

confidence: 99%

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Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Fernando,

Lăzăroiu

2023

Sensors

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The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

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Section: Methodsmentioning

confidence: 99%

Section: Energy-harvesting Technology For Cognitive-radio-based Iot N...mentioning

confidence: 99%

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Fernando,

Lăzăroiu

2023

Sensors

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“…Above the data layer is the system service layer, which provides background workflow management configuration, template configuration, Real Time Xpert (RTX) message service and data sharing services to support the better application of the whole system application layer. The application layer of the whole system includes the core business modules of the system, including plan management, pre-trial management, review management, quality management and engineering information base management [19]. The basic network layer is used for the operation network environment of the whole system, and the end user layer accesses each business module in the system through the power information network.…”

Section: Overall System Architecture Designmentioning

confidence: 99%

Design of power transmission and transformation engineering design review system based on spring struts hibernate framework

Zhang

Saren

Jiang

et al. 2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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For the current design of power transmission and transformation engineering design review system, there are some problems, such as poor functionality and processing capacity, and long system response time. The design review system of power transmission and Transformation Engineering Based on Spring + Struts + Hibernate (SSH) framework is designed. According to the definition and characteristics of SSH framework, this paper puts forward three classical frameworks: Struts, Spring and Hibernate, and integrates SSH framework. Through data cleaning, conversion and standardization, the evaluation data of transmission line engineering are simplified, and the evaluation data of transmission and transformation engineering are clustered by clustering algorithm. Analyze the overall requirements and functional requirements of the system, adopt SSH framework and design the overall system architecture in combination with specific business. Divide the system function structure from the user use type. Through the design of database structure and data table, the design of power transmission and transformation engineering design review system based on SSH framework is realized. The experimental results show that all functional modules of the system designed by the proposed method are in normal state, with an average throughput of 94.8mbps and a response time of only 17.2s. The proposed method has better functionality and processing capacity, and shorter system response time.

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“…Along with challenges in spectrum management, energyefficient utilization is one of the main concerns in wireless communications. Energy harvesting can ensure energy autonomy by renowned renewable energy, such as radio frequency power [27] and solar power [28], [29], to recharge the limited-capacity battery of the devices. Among the different types of existing renewable energy, solar power, which is harvested directly from sunlight, is considered the most effective energy resources, even though the density of solar energy is strongly dependent on the environmental conditions.…”

Section: Referencesmentioning

confidence: 99%

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

et al. 2021

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The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, the opportunistic spectrum access capability of cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast network, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the successful transmission probability of the packets among field devices by opportunistically allocating the primary channels to the devices for data transmissions. In this paper, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process by considering the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe to maximize the received packets at the GW. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.INDEX TERMS wirelessHART, cognitive radio, markov decision process, industrial scientific medical

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Uplink NOMA-based long-term throughput maximization scheme for cognitive radio networks: an actor–critic reinforcement learning approach

Cited by 4 publications

References 55 publications

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Design of power transmission and transformation engineering design review system based on spring struts hibernate framework

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

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