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A Network Selection Strategy Based on Joint Optimization of User Satisfaction and Transmission Efficiency in Internet of Vehicle
Mathematical Problems in Engineering ( IF 1.430 ) Pub Date : 2020-11-21 , DOI: 10.1155/2020/1593530 Xinyi Liu 1, 2 , Jilong Pang 1, 2 , Wei Wang 1, 2 , Yun Meng 1, 2 , Jun Hou 1, 2
Mathematical Problems in Engineering ( IF 1.430 ) Pub Date : 2020-11-21 , DOI: 10.1155/2020/1593530 Xinyi Liu 1, 2 , Jilong Pang 1, 2 , Wei Wang 1, 2 , Yun Meng 1, 2 , Jun Hou 1, 2
Affiliation
Network selection in the Internet of Vehicles has become a popular topic of research. Unlike existing algorithms for heterogeneous network environments that rarely consider user satisfaction, in this paper, we propose a network selection strategy that takes into account both user satisfaction and transmission efficiency. We employ the effective capacity concept, which describes the maximum throughput a system can achieve under a specific statistical Quality-of-Service (QoS) delay violation probability constraint. This strategy first analyzes the influence of different utility function weight coefficients, transmission power, and time delay on each network utility satisfaction function. It is evident that the weight coefficient is proportional to the value of the utility function. Within a constrained transmission power range, the rate of increase of the function gradually slows down until it approaches a fixed value. When the delay factor value is larger, the function value is smaller, which indicates that the pursuit of lower delay will sacrifice other network performance aspects. In order to determine the maximum value of each network utility satisfaction function, a convex optimization theory is introduced for the joint optimization of user satisfaction and transmission efficiency. Finally, simulation experiments carried out under three representative network environments show that the proposed strategy is efficient and reliable.
中文翻译:
基于用户满意度和传输效率联合优化的车联网网络选择策略
车辆互联网中的网络选择已成为研究的热门话题。与针对异构网络环境的现有算法很少考虑用户满意度不同,在本文中,我们提出了一种同时考虑用户满意度和传输效率的网络选择策略。我们采用有效容量概念,该概念描述了系统在特定的统计服务质量(QoS)延迟违约概率约束下可以实现的最大吞吐量。该策略首先分析不同效用函数权重系数,传输功率和时间延迟对每个网络效用满意度函数的影响。显然,权重系数与效用函数的值成正比。在受限的传输功率范围内,函数的增长率逐渐减慢,直到接近固定值。当延迟因子值较大时,函数值较小,这表明追求较低延迟将牺牲其他网络性能方面。为了确定每个网络效用满意度函数的最大值,引入了凸优化理论对用户满意度和传输效率进行联合优化。最后,在三个有代表性的网络环境下进行的仿真实验表明,该策略是有效且可靠的。为了确定每个网络效用满意度函数的最大值,引入了凸优化理论对用户满意度和传输效率进行联合优化。最后,在三个有代表性的网络环境下进行的仿真实验表明,该策略是有效且可靠的。为了确定每个网络效用满意度函数的最大值,引入了凸优化理论对用户满意度和传输效率进行联合优化。最后,在三个有代表性的网络环境下进行的仿真实验表明,该策略是有效且可靠的。
更新日期:2020-11-22
中文翻译:
基于用户满意度和传输效率联合优化的车联网网络选择策略
车辆互联网中的网络选择已成为研究的热门话题。与针对异构网络环境的现有算法很少考虑用户满意度不同,在本文中,我们提出了一种同时考虑用户满意度和传输效率的网络选择策略。我们采用有效容量概念,该概念描述了系统在特定的统计服务质量(QoS)延迟违约概率约束下可以实现的最大吞吐量。该策略首先分析不同效用函数权重系数,传输功率和时间延迟对每个网络效用满意度函数的影响。显然,权重系数与效用函数的值成正比。在受限的传输功率范围内,函数的增长率逐渐减慢,直到接近固定值。当延迟因子值较大时,函数值较小,这表明追求较低延迟将牺牲其他网络性能方面。为了确定每个网络效用满意度函数的最大值,引入了凸优化理论对用户满意度和传输效率进行联合优化。最后,在三个有代表性的网络环境下进行的仿真实验表明,该策略是有效且可靠的。为了确定每个网络效用满意度函数的最大值,引入了凸优化理论对用户满意度和传输效率进行联合优化。最后,在三个有代表性的网络环境下进行的仿真实验表明,该策略是有效且可靠的。为了确定每个网络效用满意度函数的最大值,引入了凸优化理论对用户满意度和传输效率进行联合优化。最后,在三个有代表性的网络环境下进行的仿真实验表明,该策略是有效且可靠的。