New Model Helps Connected Vehicles Stay Online During Network Handoffs
Researchers have developed a mathematical framework to predict when vehicles lose wireless connections during spectrum handoffs in next-generation 6G networks. The work addresses a critical reliability gap for autonomous vehicles and connected car services, offering telecom operators and automotive manufacturers a tool to minimize dropped connections in mixed licensed and unlicensed spectrum environments.
Originaltitel: Mobility Aware Spectrum Handoff in 6G-Enabled Cognitive Radio Vehicular Centralized and Ad-Hoc Networks
<p>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 D-th as the waiting threshold period for CU, we analyze the effect PU's arrival rate and D-th 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 k(th) 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.</p>