Wang Junfeng

Bio: Wang Junfeng is an academic researcher. The author has contributed to research in topics: Frequency modulation & Demodulation. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

An efficient coherent demodulation algorithm for TETRA communication system

Abstract: This paper describes an efficient coherent demodulation algorithm aiming at the receiver in Terrestrial Trunked Radio (TETRA) communication system. The algorithm proposes frequency offset error twice-estimation and sectioned phase offset compensation to achieve the high precision in carrier synchronization. Odd-even decision is then proposed in the algorithm to avoid signal noise ratio (SNR) loss in differential demodulation. The simulation has confirmed that the precision of frequency offset error twice-estimation is better than that of data-aided (DA) estimation and that compared with differential demodulation, the bit error rate (BER) performance of coherent demodulation is much better.

Design and implementation of TETRA call analysis software

Abstract: TETRA communication standard developed by ETSI has become the leader in the Professional Mobile Radio market, the demand of call analysis software is increasing in the areas of testing, development and network maintenance. But the proprietary signaling of TETRA cannot be processed by most commercial call analysis software, so that we develop TETRA call analysis software to overcome this problem. In this paper, we proposed protocol process architecture based on open source software Wireshark. Some key issues of the development are discussed, including the extraction of call information, the generation of bi-directional navigable Message Sequence Chart (MSC) and network performance measurement. Finally, a test result carried by our call analysis software in an experimental TETRA network is also presented.

Multiple evidence fusion based information diffusion model for social network

Abstract: Study on diffusion behaviors, such as contagion of virus, adoption of production, and information propagation, is one of the hottest topics of social network research. The conventional models usually focus on a particular factor that influences the contagion process. A novel model of diffusion based on multiple behavior evidence fusion is proposed on the analysis of prevalent social network data set, which can simulate the information propagation process with the fitting goodness of 0.79. In this model, impact factors, i.e. the influence between neighborhood, and the activity of user, are taken into account together and fused into combined evidence mass function, which can be used to infer users' forwarding behavior by D-S theory reasoning. Series of experiments tested on the published Enron email data set show that the multiple evidence fusion based diffusion model (MEFDM) is reasonable and feasible in demonstrating the cascading of information in the social network.

The mergence of group calls in TETRA networks

Abstract: Group call is a major service in TETRA (Terrestrial Trunked Radio) networks, and there are some cases where group call conflict occurs. This paper presents a method to resolve group call conflict by merging the users requesting to initiate an existing group call into the ongoing call. In addition, this paper also comes up with a method to shorten the call set-up time by setting a time delay between call clear and channel release when the same group call is initiated frequently, and by these means user experience and system performance are improved. The state machine design and process design are described in details. The experimental test result proven the effectiveness of our method.