Abstract-Generative models are created to be used in the design and performance assessment of high layer wireless communication protocols and some error control strategies. Generative models can replace real digital wireless channels to significantly reduce the time and complexity of system simulation. The errors occurring in digital wireless channels are not independent but form clusters or bursts. Generative models have to produce error sequences having similar burst error statistics to those of original error sequences obtained from real digital systems. In this paper, we propose a generative hidden Markov model (HMM) with three layers. It is shown that the proposed three layered HMM can generate error sequences that have statistics compatible with those of original error sequences derived from an enhanced general packet radio service (EGPRS) transmission system.
Abstract-Generative models hold the promise of reducing the computational load and cost caused by directly simulating a real system. They are vital to the design and performance evaluation of error control schemes and high layer wireless communication protocols. Therefore, designing an efficient and accurate generative model is highly desirable. Moreover, the errors encountered in digital wireless channels exhibit correlation among them. This stimulates us to construct a Markovian based generative model with two embedded processes. The first process is dedicated to assembling error bursts with error-free bursts, whereas the second one is devoted to creating individual error bursts employing the maximum gap norm within error bursts. This premise is utilized in this paper to show that the resulting generative model can generate error sequences with desired bit correlations and is capable of statistically matching a descriptive model, derived from an enhanced general packet radio service (EGPRS) transmission system, regardless of the configuration of its 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.
Error models that can characterize the statistical behavior of bursty error sequences in digital wireless channels are important for evaluating and designing error control strategies as well as high layer wireless protocols. Generative models have an immense impact on wireless communications industry as they can significantly reduce the computational time of simulating wireless communication links. By using a few reference error sequences obtained from a reference transmission system, adaptive generative models aim to generate many more error sequences, corresponding to various conditions of physical channels. Compared with traditional general models, this adaptive technique can further considerably reduce the computational load of generating new error sequences as there is no need to simulate the whole transmission system again. In this paper, reference error sequences are obtained by computer simulations of a long term evolution (LTE) system. Adaptive generative models are developed from several widely used generative models, namely, the simplified Fritchman model (SFM), the Baum-Welch based hidden Markov model (BWHMM), and the deterministic process based generative model (DPBGM). We produce new error sequences according to the developed adaptive generative models and compare their burst error statistics for specific channel conditions with those obtained from reference error sequences. It is demonstrated that the well-known burst error statistics of the new error sequences derived from adaptive generative models can closely match those of reference error sequences. Index TermsAdaptive generative models, error models, burst error statistics, digital wireless channels, Markov models.
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