Abstract
Channel models are widely applied in research to develop Vehicle-to-Vehicle (V2V) applications. Such models lay a foundation for gaining knowledge about application performance and help to evaluate algorithms. While deterministic channel models aim to represent the environment as realistically as possible to compute the channel impulse response, stochastic channel models based on measurement campaigns offer a computational inexpensive and generic way to randomly create realistic channel impulse responses. This paper compares the ray optical deterministic channel model MobPred optimized for V2V applications with the open source stochastic channel model Quadriga that is applicable to V2V communication. channel impulse responses were created for three different scenarios and evaluated inside a link level simulator to examine the packet error rate which is an important key performance indicator for many V2V applications. The results can be used to weigh which channel model type is suitable for which certain simulation scenario.
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