Utilization-Oriented Spectrum Allocation in an Underlay Cognitive Radio Network

Zhang, Xiu; Zhang, Xin; Han, Liang; Xing, Ruiqing

doi:10.1109/access.2018.2808473

Cited by 29 publications

(13 citation statements)

References 32 publications

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“…To unleash the potential of the M2M paradigm and address the aforementioned spectrum scarcity in M2M networks [8], Cognitive Radio (CR) [9] can be integrated with M2M communications resulting in the term cognitive M2M communications. CR technology can boost the spectrum by sharing resources between the primary and secondary users [10]. Spectrum sharing schemes include underlay, overlay and interweave, and among them, the underlay scheme is favorable in M2M networks due to its high spectral and power efficiency with low delay in transmission [11].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Cognitive Clustered M2M Random Networks With Joint User and Machine Device Selection

Abdullah

Chen

et al. 2019

IEEE Access

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In this paper, a machine-to-machine (M2M) communication system is proposed with joint M2M and cellular user equipment (CUE) device selection schemes to decrease the outage probability of the system. The machine devices and CUEs are positioned randomly according to a binomial point process (BPP), and two novel ordering metrics are proposed for the joint selection scheme: one based on the locations of the M2M devices and the other based on instantaneous channel gains. The simulation results confirm that the proposed selection scheme attains a significant reduction in the outage probability for M2M networks while limiting the interference to the base station (BS) by a delimited threshold. A hybridduplex BS is employed to switch between a half-duplex (HD) and a full-duplex (FD) to attain the best performance corresponding to various levels of residual self-interference. The closed-form formulas of the outage probability are derived for each of these ordering policies corresponding to different path loss exponents, and the analytical results are verified through Monte Carlo simulations. The proposed model and its related analysis is given in this paper lead the way for further work in the 5G Internet of Things (IoT) area.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Cognitive Clustered M2M Random Networks With Joint User and Machine Device Selection

Abdullah

Chen

et al. 2019

IEEE Access

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“…A spectrum allocation model that maximizes the spectrum utilization, based on the interference among primary and secondary users by providing the spectrum allocation solution for a cognitive radio network (CRN), is proposed in [23]. Authors have developed an enhanced artificial bee colony algorithm called the Modified Binary ABC (MBABC) algorithm to solve spectrum allocation problem.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Spectrum Utilization in Cooperative Spectrum Sensing

Ali

Nam

2019

Sensors

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This paper presents an analytical framework for the probability of spectrum hole utilization (PSHU) of a cognitive radio system with soft cooperative spectrum sensing (CSS) under a practical consideration of fixed frame structure. In practical systems, the length of a time-frame is generally fixed, where the time-frame consists of sensing, reporting, and transmission durations. Thus, increasing sensing and reporting time duration in cooperative spectrum sensing improves the probability of successful detection of the primary user’s (PU) presence or the absence but reduces transmission time duration, which results in a lower PSHU. A large reporting duration is required when more secondary users (SUs) report their sensed information to the fusion center (FC) and/or multiple bits are used by each SU in soft cooperative spectrum sensing. Thus, reporting time in terms of the number of SUs and reporting bits also have a similar effect on PSHU. Based on this interesting trade-off between PSHU and the sensing and reporting time duration, this paper analyzes the impact of an increasing number of SUs and reporting bits on PSHU.

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“…However, in case of WRANs, the probability of channel uncertainty is reduced as the SU base station is able to regularly estimate the channel conditions through feedback control channel for the each SU. Zhang et al considered underlay approach for SU transmission, and to reduce the interference between the PU and the SU, problem is formulated as an optimization problem which is solved using modified binary artificial bee colony algorithm. Ahmad et al considered a deep learning method and used a multilayer neural network for channel assignment and power allocation problem by training it using the network data generated by the genetic algorithm.…”

Section: Channel Assignment In Literaturementioning

confidence: 99%

Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

Gupta

Dhurandher

2019

Int J Communication

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Summary With the advent of various emerging wireless products, the usage of limited spectrum has grown exponentially in the recent years. In the next few years, that mobile data traffic globally is expected to grow up to 50 EB/month, which is nearly a five times increase over year 2018. Therefore, it will become extremely difficult to satisfy the ever increasing demand through the current fixed spectrum assignment policy in which spectrum band is exclusively used for the particular applications, and it has also led to underutilization of a significant portion of the spectrum (like TV bands). Cognitive radio networks has emerged as a possible solution for the problem which makes dynamic spectrum access possible for unlicensed user when licensed user is not active. Among various operations of cognitive radio, channel assignment to the unlicensed user is very important. Further, wireless regional area network is one of the most important application of cognitive radio, which provides wireless broadband to the rural area using vacant TV channels. This paper discusses channel assignment techniques considering various functionalities for cognitive radio networks in respect of wireless regional area network in the existing literature. Initially, a comprehensive introduction to both cognitive radio networks and wireless regional area networks is provided, and in the end, the paper summarizes the various issues and research challenges in the channel assignment for wireless regional area networks.

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Utilization-Oriented Spectrum Allocation in an Underlay Cognitive Radio Network

Cited by 29 publications

References 32 publications

Performance Analysis of Cognitive Clustered M2M Random Networks With Joint User and Machine Device Selection

Performance Analysis of Cognitive Clustered M2M Random Networks With Joint User and Machine Device Selection

Optimization of Spectrum Utilization in Cooperative Spectrum Sensing

Cross‐layer perspective for channel assignment in cognitive radio networks: A survey

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