Deterministic process-based generative models for characterizing packet-level bursty error sequences

Errors encountered in digital wireless channels are not independent but rather form bursts or clusters. Error models aim to investigate the statistical properties of bursty error sequences at either packet level or bit level. Packet-level error models are crucial to the design and performance evaluation of high-layer wireless communication protocols. This paper proposes a general design procedure for a packet-level generative model based on a sampled deterministic process with a threshold detector and two parallel mappers. In order to assess the proposed method, target packet error sequences are derived by computer simulations of a coded enhanced general packet radio service system. The target error sequences are compared with the generated error sequences from the deterministic process-based generative model using some widely used burst error statistics, such as error-free run distribution, error-free burst distribution, error burst distribution, error cluster distribution, gap distribution, block error probability distribution, block burst probability distribution, packet error correlation function, normalized covariance function, gap correlation function, and multigap distribution. The deterministic process-based generative model is observed to outperform the widely used Markov models.