A novel spectrum handoff switching decision scheme for improved performance of secondary users in cognitive radio network

Dey, Sudipta; Misra, Iti Saha

doi:10.1002/dac.5413

Cited by 4 publications

(2 citation statements)

References 43 publications

(62 reference statements)

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“…For example, Sivasundarapandian et al [ 27 ] present an approach based on a secondary user (SU) mobility model that considers SU movement and distribution. Dey and Saha propose a scheme that considers channel availability, interference, channel quality, and user distance to define the parameter MOS [ 28 ]. Al-Dulaimi et al [ 29 ] propose a congestion control scheme, and Zhang et al [ 30 ] propose an opportunistic programming scheme in the spectrum.…”

Section: Related Workmentioning

confidence: 99%

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Cervantes-Junco,

Rodriguez-Colina,

Palacios-Luengas

et al. 2024

Sensors

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The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability.

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Section: Related Workmentioning

confidence: 99%

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Cervantes-Junco,

Rodriguez-Colina,

Palacios-Luengas

et al. 2024

Sensors

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“…The model described in Section 3.1 of Scheme 1, which incorporates PU delay [4], is further improved by incorporating non-preemptive priority [22]. The PU packet experiences a delay equal to a specific number of time slots (PU delay, d = 1).…”

Section: Delay By Pusmentioning

confidence: 99%

Optimizing Priority Queuing Systems with Server Reservation and Temporal Blocking for Cognitive Radio Networks

Behera,

Imoize,

Singh

et al. 2024

Telecom

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In the domain of cognitive radio (CR), unlicensed users have the opportunity to efficiently use available spectrum bands without interfering with licensed primary users (PUs). Our study addresses the challenge of secondary user (SU) spectrum shortage due to high arrival rates of licensed users. We propose two models aimed at improving the average total waiting time for SUs in such scenarios. These models incorporate non-acquired and preemptive priority mechanisms within the M/D/1 model of a PU delay system. Through quantitative evaluations and Monte Carlo simulations, we evaluate the performance of these models. Our findings show significant improvements in average waiting time for both PUs and SUs, especially under the priority scheme. Furthermore, we explore these models in the context of real-time systems, considering the limited buffer capacity for both user types. This further improves the average waiting time for PUs and SUs in both priority schemes. Our contribution lies in providing effective solutions to mitigate SU shortages in CR networks, providing insight into priority-based approaches and real-time system considerations.

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