Spectrum Aggregation Scheme in a Wireless Broadband Data Transceiver System

Guo, Da; Zhang, Yong; Guang-nian, XU; Hyeongchun, Park

doi:10.2316/journal.206.2018.5.206-0068

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…In China, the 230 MHz power wireless private networks uses authorized spectrum specially allocated to the power grid, hydraulic, geological, and other industries [14, 15]. Private networks users can only use their own authorized bands, which makes the power wireless private networks weak in transmission capacity and low in spectrum efficiency [16]. Cognitive radio technology can be applied to power wireless private networks to improve the spectrum utilization efficiency.…”

Section: Introductionmentioning

confidence: 99%

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

Yuan

Zhang

et al. 2022

IET Communications

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Nowadays, wireless communication system is facing the problems of spectrum resource shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized to primary users to improve the spectrum utilization. In this paper, a cognitive network model based on hybrid overlay-underlay spectrum access mode is established. To solve the resource allocation problem, a multi-agent resource allocation algorithm based on graph convolution reinforcement learning which combines deep Q network (DQN) and graph attention network is proposed. DQN is used for action selection and graph attention network is used to obtain the information about neighbours, so as to achieve local cooperation. The proposed algorithm can adaptively optimize cognitive network throughput, spectrum efficiency, or power efficiency by controlling the transmission power and channel selection of cognitive users. To improve the information interaction efficiency, the agent's states are divided into two categories, whether it needs to interact with neighbours or not, which shortens training time and improves convergence speed. Simulation results show that the proposed algorithm can effectively improve the power efficiency of cognitive networks. Compared with Q-learning, DQN and exiting graph convolutional reinforcement learning algorithm, the proposed algorithm has faster convergence speed and higher stability, and obtains higher network power efficiency.

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Section: Introductionmentioning

confidence: 99%

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

Yuan

Zhang

et al. 2022

IET Communications

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“…Many frequency bands in electric power wireless private networks are licensed frequency bands. Private network users cannot use the licensed frequency bands of other private networks, which makes the 230MHz frequency band have weak transmission capabilities and low spectrum utilization [2]. With the development of wireless communication technology, the current power wireless private network based on the LTE system has begun to evolve to 5G, and the application of multi-slice services needs to be carried out in the spectrum awareness environment.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Yuan

Zhang

Qie

et al. 2021

ComSIS

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With the development of wireless communication technology, the requirement for data rate is growing rapidly. Mobile communication system faces the problem of shortage of spectrum resources. Cognitive radio technology allows secondary users to use the frequencies authorized to the primary user with the permission of the primary user, which can effectively improve the utilization of spectrum resources. In this article, we establish a cognitive network model based on under1 lay model and propose a cognitive network resource allocation algorithm based on DDQN (Double Deep Q Network). The algorithm jointly optimizes the spectrum efficiency of the cognitive network and QoE (Quality of Experience) of cognitive users through channel selection and power control of the cognitive users. Simulation results show that proposed algorithm can effectively improve the spectral efficiency and QoE. Compared with Q-learning and DQN, this algorithm can converge faster and obtain higher spectral efficiency and QoE. The algorithm shows a more stable and efficient performance.

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“…Currently, the main standard for China's PWPNs is based on long term evolution (LTE) [6][7][8][9]. However, LTE networks are not absolutely secure and are still susceptible to a number of security threats.…”

Section: Introduction the Terminal Communication Access Network Ismentioning

confidence: 99%

Security Architecture of Wireless Private Networks for Smart Grid

2019

Electrical Engineering and Computer Science (EECS)

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Due to the open nature of wireless networks, power wireless private networks (PWPNs), as the carrier of smart grid service, must provide highly reliable and secure communication capabilities. The security risks of terminals, wireless channels, base stations, core networks, host systems and data applications are analyzed. Aiming at the multi-service characteristics of smart grid, a security architecture based on service isolation is proposed in PWPNs. The architecture implements end-to-end service security isolation and can effectively combat different security threats from outside.

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Spectrum Aggregation Scheme in a Wireless Broadband Data Transceiver System

Cited by 4 publications

References 16 publications

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Security Architecture of Wireless Private Networks for Smart Grid

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