Intelligent Process of Spectrum Handoff/Mobility in Cognitive Radio Networks

Yawada, Prince Semba; Dong, Mai Trung

doi:10.1155/2019/7692630

Cited by 36 publications

(24 citation statements)

References 26 publications

(37 reference statements)

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“…This algorithm, on the other hand, lacks global search, while the PSO algorithm lacks local search. Yawada(2019)[8] Cognitive radio is a cutting-edge wireless communication system technology that attempts to drastically enhance radio spectrum usage while allowing secondary users to access the spectral band as-needed. The spectrum management system, which controls the e ciency of operation between the primary and secondary networks, ensures data transport.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Spectrum Handoff scheme in Cognitive Radio with Multiple Attributes

Prasad

Jaya

2022

Preprint

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Cognitive radio is a technology that can be used to detect accessible data transmission channels in the wireless spectrum. To avoid user interference and congestion, the cognitive radio is employed. Multiple parameters make up the network, which are used to choose the best network for transmission without delay. The goal of this paper is to investigate and develop a spectrum handoff scheme using MADM (Multiple Attributes Decision Making) methods like simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis (GRA), cost function-based method (CFB), and Enhanced technique for order preference (ETOPSIS). In two different circumstances, the research work has been implemented. In the first scenario, MADM methods were used to implement all four network selection strategies, as well as Enhanced TOPSIS (ETOPSIS). Voice, video, and data services, sometimes called as triple play services, are depending on CR preferences. In cognitive radio networks, the proposed MADM spectrum handoff system will be efficiently implemented to select the best network in line with triple play services. Spectrum management concerns such as spectrum sensing and sharing make CRN networks difficult to manage. As a result, ARODN (Adaptive Rider Optimization with Different Noise) is created in this study to increase energy efficiency for various spectrum sensing circumstances. As a result, in the second example, various noises such as AWGN, Rayleigh, and Nagakami noises are introduced to the receiver signal, and spectrum sensing and sharing are evaluated on the basis of which the bit error rate is depicted (BER). As a result, the suggested approach is used to calculate the bit error rate for three different noise scenarios. MATLAB is used to implement the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

Novel Spectrum Handoff scheme in Cognitive Radio with Multiple Attributes

Prasad

Jaya

2022

Preprint

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“…Spectrum handoff in CRN is mainly classified into two main categories: 1) non-channel switching, also known as non-handoff and 2) channel switching spectrum handoff. In non-channel switching, when a PU reappears, an SU stays on the same channel and waits for the PU to vacate the channel to resume its unfinished transmission [11,12]. For short PU data transmission, the non-handoff mechanism is a better choice, because PU interference is very low [13].…”

Section: Background and Related Workmentioning

confidence: 99%

Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks

Rahim

Alfakeeh

Hussain

et al. 2020

Sensors

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The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user’s perspective as well as from the channel’s perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte–Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user’s satisfaction and channel utilization was also analyzed.

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“…It depends upon an algorithm of probability estimation however maintaining a backup reserve channel. In [48], authors anticipated a proactive handoff scheme based on a probabilistic and predictive approach. It is slightly mandatory due to the indefinite behavior of PUs.…”

Section: Proactive Handoffmentioning

confidence: 99%

Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks

et al. 2020

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Through the expeditious expansion of the wireless network, the unlicensed bandwidth-based devices are growing substantially as compared to the present vacant bandwidth. Cognitive radio networks present a proficient solution to the spectrum shortage diminution hitch by allowing the usage of the vacant part of the spectrum that is not currently in use of the Primary User licensed bandwidth to the secondary user or cognitive radio user. Spectrum management procedure in cognitive radio network comprises of spectrum sharing, sensing and handoff. Spectrum handoff plays a vital role in spectrum management and primarily focuses on single handoff strategies. This paper presents a primary user traffic pattern-based opportunistic spectrum handoff (PUTPOSH) approach to use in the cognitive radio networks. PUTPOSH permits a secondary user to sense the arrival of a primary user and use an opportunistic handoff scheme. The opportunistic handoff scheme firstly detects the arrival of the primary users by energy detection sensing and secondly, it allows a cognitive radio user to decide whether to do handoff or not contingent upon the overall service time to reduce the unused handoffs. The handoffs can either be reactive or proactive based on the arrival rate of the primary user. The simulation results show that the presented PUTPOSH approach (a) minimizes the number of handoffs and the overall service time, and (b) maintains the channel utilization and throughput of the system at a maximal point.

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Intelligent Process of Spectrum Handoff/Mobility in Cognitive Radio Networks

Cited by 36 publications

References 26 publications

Novel Spectrum Handoff scheme in Cognitive Radio with Multiple Attributes

Novel Spectrum Handoff scheme in Cognitive Radio with Multiple Attributes

Efficient Channel Allocation using Matching Theory for QoS Provisioning in Cognitive Radio Networks

Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks

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