Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network

Jaishanthi, B.; Ganesh, E. N.; Sheela, D.

doi:10.1080/00051144.2019.1674512

Cited by 10 publications

(8 citation statements)

References 14 publications

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“…ML is the scienti c investigation of algorithms and statistical models used by computer systems to carry out speci c tasks without explicit programming. The research of CR network aims to maximize spectrum utilization by providing access to increased users with the help of dynamic SA strategy [13]. By using ML algorithms, once the algorithm has learned how to process data, it can automatically complete the work.…”

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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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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“…Comparative studies of the partially observable Markov decision process (POMDP) and the Markov decision process (MDP) were also conducted. Reinforcement learning and policy-based multi-agent systems were suggested by Jaishanthi et al [12]. Because the system is powered by AI, the nodes are able to make autonomous choices about which channel to use and how to move between multiple channels because they have all of the necessary information stored in the repository.…”

Section: Survey On Learning-based Allocationmentioning

confidence: 99%

“…where a, s-available state sets, γ-Factor for discount The ideal policy can be identified using the following criteria [12]:…”

Section: Elite Mapping-swotting Algorithm For Parameter Mapping Repos...mentioning

confidence: 99%

Elite-CAM: An Elite Channel Allocation and Mapping for Policy Engine over Cognitive Radio Technology in 5G

Babu

Balakrishnan

Ramana

et al. 2022

Sensors

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The spectrum allocation in any auctioned wireless service primarily depends upon the necessity and the usage of licensed primary users (PUs) of a certain band of frequencies. These frequencies are utilized by the PUs as per their needs and requirements. When the allocated spectrum is not being utilized in the full efficient manner, the unused spectrum is treated by the PUs as white space without believing much in the concept of spectrum scarcity. There are techniques invented and incorporated by many researchers, such as cognitive radio technology, which involves software-defined radio with reconfigurable antennas tuned to particular frequencies at different times. Cognitive radio (CR) technology realizes the logic of the utility factor of the PUs and the requirements of the secondary users (SU) who are in queue to utilize the unused spectrum, which is the white space. The CR technology is enriched with different frequency allocation engines and with different strategies in different parts of the world, complying with the regulatory standards of the FCC and ITU. Based on the frequency allocation made globally, the existing CR technology understands the nuances of static and dynamic spectrum allocation and also embraces the intelligence in time allocation by scheduling the SUs whenever the PUs are not using the spectrum, and when the PUs pitch in the SUs have to leave the band without time. This paper identifies a few of the research gaps existing in the earlier literature. The behavioral aspects of the PUs and SUs have been analyzed for a period of 90 days with some specific spectrum ranges of usage in India. The communal habits of utilizing the spectrum, not utilizing the spectrum as white space, different time zones, the requisites of the SUs, the necessity of the applications, and the improvement of the utility factor of the entire spectrum have been considered along with static and dynamic spectrum usage, the development of the spectrum policy engine aligned with cooperative and opportunistic spectrum sensing, and access techniques indulging in artificial intelligence (AI). This will lead to fine-tuning the PU and SU channel mapping without being hindered by predefined policies. We identify the cognitive radio transmitter and receiver parameters, and resort to the same in a proposed channel adaption algorithm. We also analyze the white spaces offered by spectrum ranges of VHF, GSM-900, and GSM-1800 by a real-time survey with a spectrum analyzer. The identified parameters and white spaces are mapped with the help of a swotting algorithm. A sample policy has been stated for ISM band 2.4 GHz where such policies can be excited in a policy server. The policy engine is suggested to be configured over the 5G CORE spectrum management function.

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“…The results obtained are close to the optimal value provided by the brute-force algorithm, and showed superiority to the other schemes. A priority-based multi-agent system for spectrum allocation using a reserved allocation method was proposed for spectrum allocation in [86]. Here, the proposed scheme gathers environmental information to be used in spectrum sharing decision making.…”

Section: ) Cognitive Radio Networkmentioning

confidence: 99%

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Hlophe

Maharaj

2021

IEEE Access

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The spectrum low utilization and high demand conundrum created a bottleneck towards fulfilling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the first step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of artificial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at filling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an indepth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to specific mobile and wireless networking problems. Therefore, this review addresses that problem by detailing how deep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.

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Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network

Cited by 10 publications

References 14 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

Elite-CAM: An Elite Channel Allocation and Mapping for Policy Engine over Cognitive Radio Technology in 5G

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

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