Spectrum Allocation Strategies Based on QoS in Cognitive Vehicle Networks
Ruifang Li, Pusheng Zhu
Abstract
The vehicular ad hoc network (VANET) is an important part of modern intelligent transportation systems (ITS), and its emergence has provided support for improving traffic safety and driving experience. The problem of spectrum scarcity has become evident due to the increasing demand for various VANET services. Using cognitive radio (CR) technology in VANET to solve the problem of spectrum scarcity has become a research focus in recent years. The existing spectrum allocation mechanism cannot effectively solve problems, such as high delay, uncertain quality of service (QoS), and low throughput. In this study, we investigate the spectrum allocation strategies in CR for VANET(CR-VANET). For different network optimization indicators under different load networks, we divide CR-VANET into two scenarios: high-load CR-VANET (HCR-VANET) and low-load CR-VANET (LCR-VANET). In LCR-VANET, we establish a model for maximizing throughput with two constraints and propose a channel allocation scheme based on a greedy algorithm (CASGA) to maximize the network throughput. In HCR-VANET, application services are divided into safe application services(SAS) and unsafe application services. To improve the acceptance probability of SAS, we also propose an SMDP-based channel allocation scheme (SMDP-CAS) to maximize the acceptance probability of SAS. Simulation results prove that CASGA and SMDP-CAS greatly improve the throughput of the network and the acceptance probability of SAS.