Orthogonal codes-based dynamic spectrum access in cognitive radio networks

Badran, Ehab F.; Bashir, Amr A.; Zaki, Amira I.; Badawi, Waleed K.

doi:10.23919/jcc.2019.12.002

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Dynamic spectrum access based on CR technology is an effective way to solve the problem of "spectrum scarcity" [14]. ANN is a ML algorithm.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Research on Cognitive Radio Spectrum Allocation Strategy Based on Machine Learning Algorithm and Data Science Technology

Huang

Xiao

et al. 2023

Preprint

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With the rapid development of wireless communication technology, radio technology has gradually come into people’s sight. The arrival of the information age also makes people more urgent to need radio technology. However, the allocation of traditional static wireless spectrum resources is extremely uneven, which leads to extreme scarcity of spectrum resources. Many authorized spectrum is idle and spectrum utilization is low. In order to improve the utilization of spectrum resources and make secondary use of idle spectrum, this paper studied the cognitive infinite agent Spectrum Allocation (SA) strategy based on Machine Learning (ML) algorithm and data science technology. This paper compared Cognitive Radio (CR) SA based on ML algorithm and data science technology with traditional methods. The experimental results showed that the average practicability of the two CR SA in the public mobile communication frequency band was 80% and 74% respectively; the average practicability in the terrestrial TV frequency band was 75% and 59.5% respectively. Therefore, CR SA based on ML algorithms and data science and technology could improve the practicability and resource utilization of spectrum.

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“…Dynamic spectrum access based on CR technology is an effective way to solve the problem of "spectrum scarcity" [14]. ANN is a ML algorithm.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Research on Cognitive Radio Spectrum Allocation Strategy Based on Machine Learning Algorithm and Data Science Technology

Huang

Xiao

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors removed the assumption that all nodes in DWN are in the saturated mode, which is closer to the practical scenarios. In [22][23][24][25], the authors concentrated on the DRL-based DSA strategies in cognitive radio network (CRN). However, all the network scenarios considered in these works are homogeneous.…”

Section: Introductionmentioning

confidence: 99%

“…However, all the network scenarios considered in these works are homogeneous. Unlike [19][20][21][22][23][24][25], in this paper, we pay more attention to the coexistence of nodes adopting various MAC protocols in the shared multichannel heterogeneous network. The spectrum environment is more complicated than these works.…”

Section: Introductionmentioning

confidence: 99%

A Dueling Deep Recurrent $Q$ -Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks

Chen

Zhao

Zhou

et al. 2022

Wireless Communications and Mobile Computing

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This paper investigates a deep reinforcement learning algorithm based on dueling deep recurrent Q -network (Dueling DRQN) for dynamic multichannel access in heterogeneous wireless networks. Specifically, we consider the scenario that multiple heterogeneous users with different MAC protocols share multiple independent channels. The goal of the intelligent node is to learn a channel access strategy that achieves high throughput by making full use of the underutilized channels. Two key challenges for the intelligent node are (i) there is no prior knowledge of spectrum environment or the other nodes’ behaviors; (ii) the spectrum environment is partially observable, and the spectrum states have complex temporal dynamics. In order to overcome the aforementioned challenges, we first embed the long short-term memory layer (LSTM) into the deep Q -network (DQN) to aggregate historical observations and capture the underlying temporal feature in the heterogeneous networks. And second, we employ the dueling architecture to overcome the observability problem of dynamic environment in neural networks. Simulation results show that our approach can learn the optimal access policy in various heterogeneous networks and outperforms the state-of-the-art policies.

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Carrier frequency offset estimation for MIMO single-carrier FDMA system in wireless communication

Badran

Samara

Ismail

2022

Alexandria Engineering Journal

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Orthogonal codes-based dynamic spectrum access in cognitive radio networks

Cited by 3 publications

References 7 publications

Research on Cognitive Radio Spectrum Allocation Strategy Based on Machine Learning Algorithm and Data Science Technology

Research on Cognitive Radio Spectrum Allocation Strategy Based on Machine Learning Algorithm and Data Science Technology

A Dueling Deep Recurrent $Q$ -Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks

Carrier frequency offset estimation for MIMO single-carrier FDMA system in wireless communication

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Orthogonal codes-based dynamic spectrum access in cognitive radio networks

Cited by 3 publications

References 7 publications

Research on Cognitive Radio Spectrum Allocation Strategy Based on Machine Learning Algorithm and Data Science Technology

Research on Cognitive Radio Spectrum Allocation Strategy Based on Machine Learning Algorithm and Data Science Technology

A Dueling Deep Recurrent Q -Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks

Carrier frequency offset estimation for MIMO single-carrier FDMA system in wireless communication

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Product

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A Dueling Deep Recurrent $Q$ -Network Framework for Dynamic Multichannel Access in Heterogeneous Wireless Networks