针对现如今无线电频谱资源短缺,频谱利用率较低并且车辆用户有时参与频谱资源共享意愿较低的问题,在认知车联网(Cognitive Internet of Vehicles,CIoV)的环境中提出了一中基于双重拍卖算法(Double Auction,DA)来提高车辆与车辆(Vehicle to Vehicle,V2V)之间的频谱效率.具体利用双重拍卖算法建立一个频谱共享的频谱分配激励机制,并为授权车辆(PV)和认知车辆(SV)根据资源定价和资源需求建立排序队列,最后达到PV和SV双方满意的效果,提高车辆用户之间的频谱匹配效率与认知车联网系统的吞吐量.通过Python实验仿真结果表明,本文所提出的算法可以满足不同车辆条件下的满意度,并验证提出的双重拍卖算法比传统的先到先得拍卖算法的吞吐量有一定的提升.
Research on Efficient Spectrum Sharing Based on Double Auction Algorithm
In response to the current problems of shortage of spectrum resources,low spectrum utilization rate and sometimes the lower willingness of the users to participate in spectrum resource sharing,this paper proposes a spectrum sharing method based on Double Auction algorithm(Double Auction,DA)in the cognitive internet of vehicles to improve efficient sharing of spectrum resources between Vehicle to Vehicle(V2V).The DA algorithm is used to establish a spectrum allocation incentive mechanism for spectrum sharing and establish a sorting queue for Priority Vehicles(PV)and Secondary Vehicles(SV)based on resource pric-ing and resource requirements.Finally,we can achieve satisfactory results for both parties and improve the throughput of the cog-nitive vehicle networking system.Through the Python experimental simulation results,the algorithm's satisfaction under different vehicle conditions is analyzed,and it is verified that the throughput of this algorithm is improved compared to the traditional first-come-first-served auction algorithm.
cognitive internet of vehiclesspectrum sharingdouble auctionincentive mechanism
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