Abstract

The Channel estimation in Vehicle-to-Vehicle (V2V) communications is critical due to the time-varying characteristics of the V2V channel. Hence, the authors propose a hybrid approach towards channel estimation by combining Pilot Symbol Aided Channel Estimation (PSACE), Improved Crow Search Algorithm (ICSA), and Decision Directed Channel Estimation (DDCE) by employing Weight Regularized Convolutional Neural Network (WCNN). Hybrid channel estimation by combining the properties of PSACE and DDCE. PSACE suffers from pilot overhead issue due to the demand of huge number of pilots for channel estimation. On the other hand, DDCE suffers from error propagation in fast fading environments. The proposed system employs an ICSA and WCNN to overcome the issues faced by channel estimation methods. Further, the proposed strategy replaces the analytical modeling of the channel with the help of the neural network. Optimized pilots obtained from ICSA along with data symbols are fed to WCNN to estimate the channel in a non-stationary environment. Finally, the performance of the proposed method is evaluated in terms of Bit Error Rate, Mean Square Error, and Packet Error Rate and the simulations are carried out under various V2V scenarios for testing its sturdiness.

中文翻译：

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OFDM系统中一种高效的导频符号辅助和判决定向混合信道估计技术

摘要

由于V2V通道的时变特性，车对车（V2V）通信中的通道估计至关重要。因此，作者通过采用加权正则卷积神经网络（WCNN）结合了飞行员符号辅助信道估计（PSACE），改进的Crow Search算法（ICSA）和决策定向信道估计（DDCE），提出了一种信道估计的混合方法。通过组合PSACE和DDCE的属性进行混合信道估计。由于对信道估计的大量导频的需求，PSACE遭受导频开销问题。另一方面，DDCE在快速衰落环境中会遭受错误传播。提出的系统采用ICSA和WCNN来克服信道估计方法所面临的问题。进一步，所提出的策略借助神经网络取代了通道的分析模型。从ICSA获得的优化导频以及数据符号被馈送到WCNN，以估计非平稳环境中的信道。最后，根据误码率，均方误差和分组误码率评估了该方法的性能，并在各种V2V场景下进行了仿真，以测试其坚固性。