Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc Networks
In this paper, we propose an integrated model of the cell-based and pool-based spectrum handoff (SH) process of a non-stationary cognitive user (CU) under a heterogeneous spectrum environment (HetSE) in a 6G-enabled cognitive radio cellular network (CRCN). We model the Link Maintenance Probability (...
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2025-01-01
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author | Shanidul Hoque Hemanta Kumar Sahu Mohammed Nazibul Hasan Rajesh Saha |
author_facet | Shanidul Hoque Hemanta Kumar Sahu Mohammed Nazibul Hasan Rajesh Saha |
author_sort | Shanidul Hoque |
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description | In this paper, we propose an integrated model of the cell-based and pool-based spectrum handoff (SH) process of a non-stationary cognitive user (CU) under a heterogeneous spectrum environment (HetSE) in a 6G-enabled cognitive radio cellular network (CRCN). We model the Link Maintenance Probability (LMP) and Link Failure Probability (LFP) of the CUs under a Heterogeneous Spectrum Environment (HSE) with licenced and unlicensed spectrum pools. These performance measuring metrics are derived for various SH schemes: intracell/intrapool SH, intracell/interpool SH, intercell/intrapool SH, and intercell/interpool SH, considering the primary user (PU) activity model and CU mobility. Considering <inline-formula> <tex-math notation="LaTeX">$D_{th}$ </tex-math></inline-formula> as the waiting threshold period for CU, we analyze the effect PU’s arrival rate and <inline-formula> <tex-math notation="LaTeX">$D_{th}$ </tex-math></inline-formula> on the performance measuring metrics in two different network architectures: Cognitive Radio-Vehicular Ad-Hoc Network (CR-VANET) and Cognitive Radio-Vehicular Centralized Network (CR-VCNET). In addition, we derive the LMP and LFP of a non-stationary CU in <inline-formula> <tex-math notation="LaTeX">$k^{th}$ </tex-math></inline-formula> cell and compare the results among various cells. Further, we realize the effect of various service time distributions of PUs and CUs on the performance metrics, and lognormal service time distribution offers better SH performance as compared to the exponential and Erlangen distribution models. We also perform Monte-Carlo simulation for the performance measuring metrics to validate the proposed integrated SH model in CRCNs. |
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spelling | doaj-art-43c7b88da3114a98b2210db8c4404b802025-02-07T00:01:24ZengIEEEIEEE Access2169-35362025-01-0113228002281110.1109/ACCESS.2025.353709610859155Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc NetworksShanidul Hoque0https://orcid.org/0000-0001-6711-5716Hemanta Kumar Sahu1https://orcid.org/0000-0002-0530-9061Mohammed Nazibul Hasan2https://orcid.org/0000-0001-9953-7446Rajesh Saha3https://orcid.org/0000-0003-3108-6081School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Electrical Engineering, Uppsala University, Uppsala, SwedenDepartment of ECE, NIT Silchar, Silchar, IndiaIn this paper, we propose an integrated model of the cell-based and pool-based spectrum handoff (SH) process of a non-stationary cognitive user (CU) under a heterogeneous spectrum environment (HetSE) in a 6G-enabled cognitive radio cellular network (CRCN). We model the Link Maintenance Probability (LMP) and Link Failure Probability (LFP) of the CUs under a Heterogeneous Spectrum Environment (HSE) with licenced and unlicensed spectrum pools. These performance measuring metrics are derived for various SH schemes: intracell/intrapool SH, intracell/interpool SH, intercell/intrapool SH, and intercell/interpool SH, considering the primary user (PU) activity model and CU mobility. Considering <inline-formula> <tex-math notation="LaTeX">$D_{th}$ </tex-math></inline-formula> as the waiting threshold period for CU, we analyze the effect PU’s arrival rate and <inline-formula> <tex-math notation="LaTeX">$D_{th}$ </tex-math></inline-formula> on the performance measuring metrics in two different network architectures: Cognitive Radio-Vehicular Ad-Hoc Network (CR-VANET) and Cognitive Radio-Vehicular Centralized Network (CR-VCNET). In addition, we derive the LMP and LFP of a non-stationary CU in <inline-formula> <tex-math notation="LaTeX">$k^{th}$ </tex-math></inline-formula> cell and compare the results among various cells. Further, we realize the effect of various service time distributions of PUs and CUs on the performance metrics, and lognormal service time distribution offers better SH performance as compared to the exponential and Erlangen distribution models. We also perform Monte-Carlo simulation for the performance measuring metrics to validate the proposed integrated SH model in CRCNs.https://ieeexplore.ieee.org/document/10859155/Vehicular cognitive radio cellular networksmobility of cognitive usersservice time distribution modelsperformance analysisspectrum handoff models |
spellingShingle | Shanidul Hoque Hemanta Kumar Sahu Mohammed Nazibul Hasan Rajesh Saha Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc Networks IEEE Access Vehicular cognitive radio cellular networks mobility of cognitive users service time distribution models performance analysis spectrum handoff models |
title | Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc Networks |
title_full | Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc Networks |
title_fullStr | Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc Networks |
title_full_unstemmed | Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc Networks |
title_short | Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc Networks |
title_sort | mobility aware spectrum handoff in 6g enabled cognitive radio vehicular centralized and ad hoc networks |
topic | Vehicular cognitive radio cellular networks mobility of cognitive users service time distribution models performance analysis spectrum handoff models |
url | https://ieeexplore.ieee.org/document/10859155/ |
work_keys_str_mv | AT shanidulhoque mobilityawarespectrumhandoffin6genabledcognitiveradiovehicularcentralizedandadhocnetworks AT hemantakumarsahu mobilityawarespectrumhandoffin6genabledcognitiveradiovehicularcentralizedandadhocnetworks AT mohammednazibulhasan mobilityawarespectrumhandoffin6genabledcognitiveradiovehicularcentralizedandadhocnetworks AT rajeshsaha mobilityawarespectrumhandoffin6genabledcognitiveradiovehicularcentralizedandadhocnetworks |