Delay Jitter Modeling for Low-Latency Wireless Communications in Mobility Scenarios

Understanding the delay jitter of mobile communications becomes important because of widely spreading delay-sensitive applications such as remote control of mobile robots with high-frequency communications via wireless networks. Prior studies on delay jitter modeling have proposed using a single probability distribution (e.g., Gamma and Laplace distributions). However, mobility-induced wireless quality fluctuations form a mixture of probability patterns, e.g., several peaks and a heavy tail. This paper proposes a method to estimate delay jitter accurately in high-frequency and mobile communications. Our method has two features. The first is to model the delay jitter by a mixture of multiple Laplace distributions by taking into account the probability patterns. For quick convergence of model training, the model is trained with access manner-aware initialization in each Wi-Fi and mobile network. The second is to construct a likelihood-based observation segmentation for estimating model parameters accurately against mobility. Performance evaluation through experiments in indoor Wi-Fi and outdoor 5G scenarios shows that our proposed method improves modeling accuracy by 28.7% compared with the case of the prior studies.