基于网络效用最大化的车联网功率控制算法

发布时间：2018-11-10 08:21

【摘要】：针对车联网(Io V)中车流密度增加到一定程度时,即使无线信道中只有信标消息,信道拥塞也会发生的问题,提出一种分布式加权公平功率控制(D-WFPC)算法。首先,考虑车联网的实际信道特性,采用Nakagami-m衰落信道模型建立随机信道模型;然后,考虑车联网中节点的移动性,基于网络效用最大化(NUM)模型建立功率控制优化问题,控制本地信道负载在阈值之下,从而避免拥塞;最后,通过对偶分解和迭代法解决该问题,设计分布式算法,每辆车根据周围环境的邻居车辆的信标消息,动态调整发射功率。仿真实验中,与固定发射功率方案相比,随着车流密度增大,D-WFPC算法能有效降低时延和丢包率,最高降幅分别达到24%和44%;与公平分布式发射功率拥塞控制(FCCP)算法相比,D-WFPC算法全程性能占优,时延和丢包率的最高降幅分别达到10%和4%。仿真结果表明,D-WFPC算法能快速收敛,保证车联网中消息的低时延、高可靠传输。
[Abstract]:A distributed weighted fair power control (D-WFPC) algorithm is proposed to solve the problem of congestion even if the traffic density in (Io V) is increased to a certain extent even if there are only beacon messages in the wireless channel. Firstly, considering the actual channel characteristics of vehicle networking, the Nakagami-m fading channel model is used to establish the stochastic channel model. Then, considering the mobility of nodes in the vehicle network, the power control optimization problem is established based on the network utility maximization (NUM) model to control the local channel load below the threshold so as to avoid congestion. Finally, the problem is solved by dual decomposition and iteration, and a distributed algorithm is designed to dynamically adjust the transmission power of each vehicle according to the beacons of the neighboring vehicles in the surrounding environment. In the simulation experiment, compared with the fixed transmission power scheme, with the increase of the traffic density, the D-WFPC algorithm can effectively reduce the delay and packet loss rate, the highest reduction is up to 24% and 44% respectively. Compared with the fair distributed transmit power congestion control (FCCP) algorithm, the overall performance of the D-WFPC algorithm is superior, the maximum drop of delay and packet loss rate is 10% and 4% respectively. The simulation results show that the D-WFPC algorithm can converge rapidly and ensure the low delay and high reliability of the message transmission in the vehicle network.
【作者单位】：同济大学电子与信息工程学院;
【基金】：国家自然科学基金重点项目(61331009)~~
【分类号】：TN929.5

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