Cognitive radio based spectrum sharing models for multicasting in 5G cellular networks: A survey

Bhattacharjee, Shankar; Acharya, Tamaghna; Bhattacharya, Uma

doi:10.1016/j.comnet.2022.108870

Cited by 26 publications

(11 citation statements)

References 111 publications

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“…Reconfigurable intelligent surfaces assist network operators in monitoring radio waves to remove the detrimental consequences of natural wireless propagation [ 9 ], and through the use of nonorthogonal multiple access, adequate transmissions can be provided. Radio resources’ limited availability together with interference elevated levels reduces dense network spectrum reuse [ 10 ], but cognitive radio technology can improve spectral efficiency through enabling low-priority unlicensed users to have the same spectrum as high-priority licensed users (the transmission parameters are adjusted according to the application requests, resulting in streamlined spectrum management and interference reduction across 5G cellular networks). Cognitive-radio-based spectrum sharing approaches can enable 5G cellular network services.…”

Section: Methodsmentioning

confidence: 99%

“…The massive increase in smart devices articulating content-centric data traffic amplifies cellular-network-based group communication services [ 10 ]: multicasting can handle resources adequately and enable simultaneous common data sharing to a significant volume of users. The bandwidth large-scale communication demands of multimedia multicast applications necessitate exemplary spectral resource use, while the connectivity, immense capacity, and ultra-low-latency demands of the content-centric applications have led to closer content to the users and short-range communication deployment, resulting in cellular network compaction.…”

Section: Energy-harvesting Technology For Cognitive-radio-based Iot N...mentioning

confidence: 99%

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Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Fernando,

Lăzăroiu

2023

Sensors

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The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

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Section: Methodsmentioning

confidence: 99%

Section: Energy-harvesting Technology For Cognitive-radio-based Iot N...mentioning

confidence: 99%

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Fernando,

Lăzăroiu

2023

Sensors

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“…CR arises as a technique to increase the overall spectrum utilization by the opportunistic utilization of the available spectrum [11]. A typical CR network environment consists of M various types of primary radio (PR) networks and one or more CR networks that coexist geographically within the same area.…”

Section: B Cognitive Radio Networkmentioning

confidence: 99%

“…On the other hand, Cognitive radio (CR) technology has been highlighted as a possible spectrum-management technique that successfully tackles the bandwidth dilemma in legacy licensed wireless communication networks by offering opportunistic, on-demand connection [11]. To be specific, in the CR-based networks, there are two types of users, namely the primary (licensed) users (PUs) and the unlicensed users, namely the secondary users (SUs).…”

mentioning

confidence: 99%

Integration of Cognitive Radio Technology in NOMA-Based B5G Networks: State of the Art, Challenges, and Enabling Technologies

Abdel-Razeq

Al‐Obiedollah

2023

IEEE Access

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The integration of cognitive radio (CR) technology and non-orthogonal multiple access (NOMA) techniques, referred to as CR-based NOMA system, has been recently configured as a promising solution to meet the requirements of beyond fifth generation (B5G) networks, especially those related to Internet-of-Things (IoT) applications. With such integration, power domain NOMA allows multiple users to share the same orthogonal resource blocks, while CR technology enables opportunistic bandwidth utilization by permitting secondary users (SUs) to access the licensed spectrum frequency without interrupting the primary users' (PUs) activities. To support the massive connectivity requirements of IoT-based networks, several multiple-access techniques have been recently combined with CR-based NOMA systems, including orthogonal multiple access (OMA) and multiple-antenna techniques. For example, in CR-based OMA-NOMA systems, the licensed frequency band is split into several channels, and a set of SUs is served on each channel using the NOMA technique. This paper provides an overview and analysis of the stateof-the-art CR-based NOMA network architecture. It summarizes the main design challenges related to the practical implementation of such systems. Furthermore, this paper presents the advances of combining CR-based NOMA with recent multiple-access techniques, where the potential capabilities and the design challenges of such integrated systems are also investigated and discussed. On the other hand, this paper demonstrates the potential capabilities of deploying recent technologies in CR-based NOMA networks. The technologies include intelligent reflecting surfaces, terahertz communications, machine learning, unmanned aerial vehicles, and hybrid NOMA systems. Finally, future research directions and open issues are provided and discussed. INDEX TERMS Cognitive Radio (CR), NOMA, Internet-of-Things (IoT), 5G, B5G.

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“…Next generation 4G and 5G networks are promising examples and consequently opportunistic networks are born [25], [28]. Several scheduling techniques with different levels of complexity are present in the literature [26], [29], [33], [34], [36], [37], [38], [39], [40], [41], [42], [43], [44].…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of opportunistic schedulers based on fairness and throughput in new-generation mobile networks

et al. 2023

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This paper is focused on the study of the allocation of radio resources using the scheduling and opportunistic techniques. This allows us to compare the quality of service afforded to each user when the types of traffic transmitted are highly heterogeneous and efficiently evaluate the performance of resource allocation algorithms and schedulers. Many scheduling algorithms are used for resource allocation and scheduling: Round Robin, MaxSNR and PF. The main objective of this research work is to analyze and develop the main performances relating to schedulers deployed by telecommunication operators. It should be noted that scheduling is a primodridial phase preceding the phase of allocation and allocation of radio resources in a mobile radio telecommunications system. We will focus in this paper on the major advantages and the contribution of opportunistic

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Cognitive radio based spectrum sharing models for multicasting in 5G cellular networks: A survey

Cited by 26 publications

References 111 publications

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

Integration of Cognitive Radio Technology in NOMA-Based B5G Networks: State of the Art, Challenges, and Enabling Technologies

Performance evaluation of opportunistic schedulers based on fairness and throughput in new-generation mobile networks

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