Real Communication Data Research Articles

Symbol-Level Synchronization Channel Modeling With Real-World Application: From Davey-MacKay, Fritchman to Markov

Errors in realistic channels contain not only substitution errors, but synchronization errors as well. Moreover, these errors are rarely statistically independent in nature. By extending on the idea of the Fritchman channel model, a novel error category-based methodology for determining channel characteristics is described for memory channels that contain insertion, deletion, and substitution errors. The practicality of such a methodology is reinforced by making use of real communication data from a visible light communication system. Simulation results show that the error-free and error runs using this new method of defining the channel clearly deviates from the Davey-MacKay synchronization model which is memoryless in nature. This further emphasizes the inherent memory in these synchronization channels which we are now able to characterize. Additionally, a new method to determine the parameters of a synchronization memory channel using the Levenshtein distance metric is detailed. This method of channel modeling allows for more realistic communication models to be simulated and can easily extend to other areas of research such as DNA barcoding in the medical domain.

An Improvement Method to Mining Outliers Based on Social and Spatial Information

In this paper, a method for finding outliers is proposed for communication data. By extracting the social and spatial information as the eigenvalues from the user’s call list. We use clustering method to aggregate users with the same attributes, and then analyze the clusters to find outliers. We use the real communication data of a communication company as the data set, and the experimental results show that our method can quickly and accurately find the abnormal communication user.

Tracking random walks.

In empirical studies, trajectories of animals or individuals are sampled in space and time. Yet, it is unclear how sampling procedures bias the recorded data. Here, we consider the important case of movements that consist of alternating rests and moves of random durations and study how the estimate of their statistical properties is affected by the way we measure them. We first discuss the ideal case of a constant sampling interval and short-tailed distributions of rest and move durations, and provide an exact analytical calculation of the fraction of correctly sampled trajectories. Further insights are obtained with simulations using more realistic long-tailed rest duration distributions showing that this fraction is dramatically reduced for real cases. We test our results for real human mobility with high-resolution GPS trajectories, where a constant sampling interval allows one to recover at best 18% of the movements, while over-evaluating the average trip length by a factor of 2. Using a sampling interval extracted from real communication data, we recover only 11% of the moves, a value that cannot be increased above 16% even with ideal algorithms. These figures call for a more cautious use of data in quantitative studies of individuals' movements.