Throughput-Efficient Channel Allocation Algorithms in Multi-Channel Cognitive Vehicular Networks

Han, You; Ekici, Eylem; Kremo, Haris; Altintas, Onur

doi:10.1109/twc.2016.2629484

Cited by 30 publications

(12 citation statements)

References 37 publications

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“…The optimization problem was formulated as a joint game in the form of partitions to obtain a suboptimal solution. To maximize the throughput, the authors in [10] proposed a throughput-efficient channel allocation framework for multi-channel cognitive vehicle networks and pointed out that the problem was an NP-hard nonlinear integer programming problem. An NP algorithm was designed to solve the channel allocation problem.…”

Section: Related Workmentioning

confidence: 99%

Spectrum Allocation Strategies Based on QoS in Cognitive Vehicle Networks

Zhu

2020

IEEE Access

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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.

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Section: Related Workmentioning

confidence: 99%

Spectrum Allocation Strategies Based on QoS in Cognitive Vehicle Networks

Zhu

2020

IEEE Access

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“…Maghsudi and Stanczak applied the graph theory for the channel allocation in a device-to-device (D2D) environment and considered fairness by equalizing the interference for cellular users [12]. Han et al studied channel allocation methods for maximizing the overall system performance in vehicular networks by using the submodular set function-based algorithm [13]. Li et al investigated channel allocation methods that maximize the overall system reward using a semi-Markov decision process (SMDP) in a vehicular ad-hoc network (VANET) [14].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Jang

Han

Lee

et al. 2019

J Wireless Com Network

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In cognitive radio (CR) ad-hoc network, the characteristics of the frequency resources that vary with the time and geographical location need to be considered in order to efficiently use them. Environmental statistics, such as an available transmission opportunity and data rate for each channel, and the system requirements, specifically the desired data rate, can also change with the time and location. In multi-band operation, the primary time activity characteristics and the usable frequency bandwidth are different for each band. In this paper, we propose a Qlearning-based dynamic optimal band and channel selection by considering the surrounding wireless environments and system demands in order to maximize the available transmission time and capacity at the given time and geographic area. Through experiments, we can confirm that the system dynamically chooses a band and channel suitable for the required data rate and operates properly according to the desired system performance.

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“…4 In CR-VANETs, some researches on the physical or media access control (MAC) layer have been proposed. [5][6][7] They have demonstrated that their methods can guarantee the correctness and timeliness of CR channel detection in CR-VANETs and improve the spectrum efficiency. However, routing is a crucial issue that bottlenecks the performance of the entire network and has not been widely studied in CR-VANETs.…”

Section: Introductionmentioning

confidence: 99%

Social-aware routing for cognitive radio–based vehicular ad hoc networks

Wang

Zhang

Tang

et al. 2019

International Journal of Distributed Sensor Networks

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Cognitive radio–based vehicular ad hoc networks can solve the problem of limited spectrum resource and growing vehicular communication service demands in intelligent transportation systems, and thus, it receives much concern recently. In cognitive radio–based vehicular ad hoc networks, the high mobility of vehicles and the dynamic spectrum activity of cognitive radio make routing in such networks a great challenge. Some routing researches have been proposed in cognitive radio–based vehicular ad hoc networks with single-objective optimization and neglecting the nodes’ social behaviors which can improve the network performance. From this perspective, we propose a social-aware routing scheme for cognitive radio–based vehicular ad hoc networks, with the purpose of increasing the packet delivery ratio and decreasing the overhead ratio. First, we analyze the social centrality of primary users to offer an accuracy spectrum hole measurement. Moreover, we develop a social community partition algorithm to divide secondary users into intra-community and inter-community groups. Furthermore, considering the tradeoff between the packet delivery ratio and the overhead ratio, we adopt different replication policies and forwarding ranks in different community communication processes. In the intra-community communication process, we employ the single-copy policy and the contact duration rank. In the inter-community communication process, we utilize the optimized-binary-tree replication policy and the bridge coefficient rank. Simulation results show that our social-aware routing scheme achieves the higher package delivery ratio and the lower overhead ratio when compared with the existing cognitive radio–based vehicular ad hoc networks routing schemes and other standard routing schemes.

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Throughput-Efficient Channel Allocation Algorithms in Multi-Channel Cognitive Vehicular Networks

Cited by 30 publications

References 37 publications

Spectrum Allocation Strategies Based on QoS in Cognitive Vehicle Networks

Spectrum Allocation Strategies Based on QoS in Cognitive Vehicle Networks

Reinforcement learning-based dynamic band and channel selection in cognitive radio ad-hoc networks

Social-aware routing for cognitive radio–based vehicular ad hoc networks

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