Probability-Based Centralized Device for Spectrum Handoff in Cognitive Radio Networks

Aggarwal, Manav; Velmurugan, T.; Karuppiah, Marimuthu; Hassan, Mohammad Mehedi; Almogren, Ahmad; Ismail, Walaa N.

doi:10.1109/access.2019.2901237

Cited by 27 publications

(11 citation statements)

References 30 publications

(31 reference statements)

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“…The handoff delay occurs at the maximum level when the PU arrived, hence the handoff judgment is completed. In [51], author presented a framework comprises of probabilistic algorithms, and some other novel approaches including CRU clustering and PRP M/G/1 queuing to attain better competence in spectrum handoff in a CRN. Authors claim that the proposed strategy performs better than existing schemes in terms of accuracy in sensing the right channel, handoff latency, and energy consumption.…”

Section: Reactive 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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Section: Reactive Handoffmentioning

confidence: 99%

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

et al. 2020

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“…However, this work considers centralized network architecture as well. In [21], a probability based proactive spectrum handoff mechanism is proposed where a centralized device computes the probability of idleness for each primary channel and then based on QoS requirements of SUs allocates appropriate predicted channel during handoff. Handoff delay and transmission delay are improved by sensing the right channel for handoff based on prediction probabilities.…”

Section: Related Workmentioning

confidence: 99%

“…Imperfect channel prediction mechanism has been considered for improving only the spectrum sensing phase in a cognitive cycle [16,17,18,19]. In [20,21], prediction accuracy for improving the spectrum sensing delay as a part of spectrum handoff has been considered; however, this is applicable only in a centralized CRN. The work in this paper differs from the existing work in that the imperfect channel prediction method is being considered for analyzing the spectrum handoff in a multi-user cognitive radio ad hoc network (CRAHN).…”

Section: Introductionmentioning

confidence: 99%

Spectrum Handoff based on Imperfect Channel State Prediction Probabilities with Collision Reduction in Cognitive Radio Ad Hoc Networks

Shakeel

Hussain

Iqbal

et al. 2019

Sensors

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The spectrum handoff is highly critical as well as challenging in a cognitive radio ad hoc network (CRAHN) due to lack of coordination among secondary users (SUs), which leads to collisions among the SUs and consequently affects the performance of the network in terms of spectrum utilization and throughput. The target channel selection mechanism as part of handoff process can play an enormously significant role in minimizing the collisions among the SUs and improving the performance of a cognitive radio network (CRN). In this paper, an enhanced target channel selection scheme based on imperfect channel state prediction is proposed for the spectrum handoff among the SUs in a CRAHN. The proposed scheme includes an improved frame structure that increases coordination among the SUs in the ad hoc environment and helps in organizing the SUs according to the shortest job first principle during channel access. Unlike the existing prediction-based spectrum handoff techniques, the proposed scheme takes into account the accuracy of channel state prediction; the SUs affected due to false prediction are compensated by allowing them to contend for channel access within the same transmission cycle and thus enabling them to achieve higher throughput. The proposed scheme has been compared with the contemporary spectrum handoff schemes and the results have demonstrated substantial improvement in throughput and extended data delivery time by virtue of the reduced number of collisions.

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“…In the spectrum handoff, when the PUs reappear, the SUs leave the channel and look for another channel, if available, to complete its unfinished transmission. The selection of a new channel depends on the channel availability and capacity when a handoff decision is made [6][7][8]. Spectrum handoff in a CRN may also be required when the SU experiences the degradation of the quality of service [9,10].…”

Section: Introductionmentioning

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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Probability-Based Centralized Device for Spectrum Handoff in Cognitive Radio Networks

Cited by 27 publications

References 30 publications

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

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

Spectrum Handoff based on Imperfect Channel State Prediction Probabilities with Collision Reduction in Cognitive Radio Ad Hoc Networks

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

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