Licensed Shared Access Evolution to Provide Exclusive and Dynamic Shared Spectrum Access for Novel 5G Use Cases

Kalliovaara, Juha; Jokela, Tero; Kokkinen, Heikki; Paavola, Jarkko

doi:10.5772/intechopen.79553

Cited by 10 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the wireless network becomes more distributed, enormous amounts of measurements will need to be conducted by the learning module of the CR device, and the decisions that need to be made have already presented themselves as major challenges [24]. Subsequently, the decision module of CR devices will be faced with the vexing problem of creating sufficient capacity and energy-efficient operations, combined with low latency for heterogeneous spectrum use cases.…”

Section: B Enormous Amount Of Measurementsmentioning

confidence: 99%

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

Hlophe

Maharaj

2021

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: B Enormous Amount Of Measurementsmentioning

confidence: 99%

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

Hlophe

Maharaj

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…0 in other cases (10) In general, three ways of removing an edge from the graph may be considered: either the transmit power of the interfering nodes will be reduced, or the locations of these nodes can be changed, or the available spectrum band can be split orthogonally between these two nodes. In our case we assume that the location of the device is fixed (i.e., even for nomadic networks, the coexistence manager can provide a suggestion about the available spectrum band for a certain location provided by the device in the query).…”

Section: A Graph Coloring and Finding The Chromatic Numbermentioning

confidence: 99%

“…It can be assumed that the distance between nodes d i,j and requirements on the sensitivity level P SEN and interference level P INT are fixed. However, the potential interference depends additionally on the allocated band (carrier frequency changes α) and allocated power P i and P j as visible in (10). A worst-case scenario is assumed here with the minimum center frequency out of the available band, i.e., f = 3652.5 MHz, guaranteeing the maximum range of interference.…”

Section: A Graph Coloring and Finding The Chromatic Numbermentioning

confidence: 99%

See 1 more Smart Citation

Coordinated Spectrum Allocation and Coexistence Management in CBRS-SAS Wireless Networks

Kułacz¹,

Kryszkiewicz²,

Kliks³

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Since the introduction of the idea of cognitive radio, various approaches towards spectrum sharing have been considered, for example, the Licensed Shared Access (LSA), which is considered in Europe, or Citizens Broadband Radio Service (CBRS) with Spectrum Access System (SAS) regulated by the US. This paper deals with the problem of coordinated resource allocation among a set of available base stations. A detailed definition of the problem is provided, followed by a discussion on a set of heuristics proposed for solving the problem. Four solutions are presented that are based on existing standards as well as on the approaches described in the literature. Next, new multi-selection (multi-choice) algorithm is proposed and discussed in detail. The main problem is divided in two subproblems, which are solved by using graph theorem and analytical description. The performance of the proposed solutions is analyzed in various scenarios. Finally, a trade-off between power allocation and frequency use is provided. All challenges identified during the investigation of the problem are presented.

show abstract

“…In this evolving landscape, new business models, market structures, and network architectures are beginning to take shape. For instance, the emergence of alternatives focused on the development of dynamic spectrum access models is observable, and this, in turn, has revitalized the role of market players, generating a new competitive landscape [2]. Consequently, the limitation of the radio spectrum and the lack of control over how available resources are allocated to users remain persistent challenges when contemplating the requirements of next-generation wireless systems.…”

Section: Introductionmentioning

confidence: 99%

A Techno-Economic Analysis of New Market Models for 5G+ Spectrum Management

Fletscher,

Zuleta,

Galvis

et al. 2024

Information

View full text Add to dashboard Cite

While 5G has become a reality in several places around the world, some countries are still in the process of assigning frequency bands and deploying networks. In this context, there is a significant opportunity to explore new market models for the management and utilization of the radio spectrum. Access to the radio spectrum results in diverse competition schemes, where market behavior varies based on the regulator-defined access scheme and the competitive strategies of different actors. To thoroughly analyze potential competition scenarios, this work introduces a model that enhances the comprehension of market variables, emphasizing behaviors influenced by relationships. The model’s development leverages the potential of artificial intelligence and historical data from Colombia’s mobile telecommunications market. Intelligent spectrum sensing, based on Software Defined Radio, augments the model’s construction, utilizing lightweight AI algorithms to acquire real data on spectrum occupancy. In this way, the model provides novel insights into market dynamics, enabling the formulation of informed decision-making policies for regulatory bodies. Additionally, the application of causal machine learning (CausalML) helps understand the underlying causes of market behaviors, facilitating the design of guiding policies to maximize spectrum usage and foster competition. This approach demonstrates how AI-driven approaches and a deeper understanding of market dynamics can lead to effective 5G spectrum management, fostering a more competitive and efficient wireless communication landscape.

show abstract

Licensed Shared Access Evolution to Provide Exclusive and Dynamic Shared Spectrum Access for Novel 5G Use Cases

Cited by 10 publications

References 21 publications

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

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

Coordinated Spectrum Allocation and Coexistence Management in CBRS-SAS Wireless Networks

A Techno-Economic Analysis of New Market Models for 5G+ Spectrum Management

Contact Info

Product

Resources

About