From Dynamic Spectrum Management to Smart Spectrum Management

Zhang, Jianzhao; Chen, Yong; Liu, Yongxiang; Liu, Liu

doi:10.1109/iccc47050.2019.9064314

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The edge computing and blockchain were suggested in [31] and [32] respectively to provide more sufficient and reliable assistance to users. In [33], we have probed into the possibility and initiative model of the SSM.…”

Section: B Dsm and The Transitions To Ssmmentioning

confidence: 99%

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

et al. 2020

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Spectrum is the indispensable resource for 5G wireless systems and beyond. The dynamic spectrum management (DSM) framework with spectrum sharing among different kinds of users is essential to satisfy the sustainable rapid increase of the bandwidth requirement. Based on the analysis of the DSM as well as the latent applications of the machine learning and artificial intelligence (AI) in the spectrum access, this paper will address the problem of incorporating intelligence into the spectrum management. Firstly, the spectrum opportunity (SOP) related information is layered into the spectrum data, the spectrum information and the spectrum knowledge based on the processing and abstractive level. The spectrum knowledge is formally defined as the extendible and scalable information to reason and predict the SOP usability as well as the outcome of the SOP occupancy. Then the smart spectrum model (SSM) is proposed with the spectrum knowledge as the key enabler and the coupled SOP exploration and exploitation as the core feature. Under the SSM framework, the spectrum knowledge and real-time observing (SKRO) enabled SOP exploration scheme is designed, which can make use of both the historical information and the real-time sensing information for smart SOP exploration. Extensive simulations are provided which demonstrate that the SKRO enabled SSM can achieve much better SOP utilization while satisfying the required legacy assurance. Specifically, in the low signal to noise environment, at least 16.55% gains on the SOP utilization ratio can be obtained compared with the DSM.

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Section: B Dsm and The Transitions To Ssmmentioning

confidence: 99%

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

et al. 2020

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“…With the increasing demand for wireless services and the advancements brought about by 5G technology, the need for more innovative approaches to effectively manage the radio frequency (RF) spectrum is becoming more apparent. Approaches to spectrum management that leverage both data analysis and interpretation (data-driven) are emerging as promising solutions to address these challenges, as they can enhance processes, improve decision-making and enable alternative methods for spectrum sharing [1], [2]. This is exemplified by database-assisted spectrum sharing regimes that are being implemented globally [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Evolution Toward Data-Driven Spectrum Sharing: Opportunities and Challenges

Brown,

Ghasemi

2023

IEEE Access

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As the demand for wireless services continues to grow, the need for efficient and effective management of the radio frequency (RF) spectrum becomes increasingly important. A spectrum management approach based on data analysis and interpretation i.e. data-driven, offers a solution as it provides valuable insights into spectrum usage patterns, demand, and the potential for harmful interference in shared spectrum scenarios. This paper focuses on the opportunities and challenges of incorporating diverse data sources into the management of spectrum, with a specific emphasis on spectrum sharing, particularly within database-assisted spectrum sharing systems. The benefits of adopting a data-driven approach to these systems are demonstrated through simulation of specific case studies. These studies indicate the potential for achieving up to a 60% greater density of spectrum use compared to conventional approaches, while also effectively managing harmful interference.INDEX TERMS Data-driven spectrum management, database-assisted spectrum sharing, dynamic spectrum accessThis article has been accepted for publication in IEEE Access.

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Threshold Optimization in Maximum–Minimum Eigenvalue-Based Detection in Cognitive Radio Using Ant Colony Optimization

Vishwakarma

Kulkarni

2022

Mobile Computing and Sustainable Informatics

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From Dynamic Spectrum Management to Smart Spectrum Management

Cited by 5 publications

References 26 publications

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

Evolution Toward Data-Driven Spectrum Sharing: Opportunities and Challenges

Threshold Optimization in Maximum–Minimum Eigenvalue-Based Detection in Cognitive Radio Using Ant Colony Optimization

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