Time-varying on-body wireless channel model during walking

EURASIP Journal on Wireless Communications and Networking, Feb 2014

A novel dynamic channel model for on-body wireless communication during walking is proposed. The developed model utilizes a human walking model which provides detailed information on the movement of the human body parts. The diffraction of the signal around the body parts is used to describe the time-varying shadowing effects. Body part movements are also used to estimate the signal fading caused by angular variations of the transmitting and receiving antenna gains. A Rice distribution is used to represent the multipath fading effects caused by objects around the human body. Simulation results of the first- and second-order statistics of the received signal affected by moving body parts for 2.4 GHz signal are presented. To illustrate the capabilities of the developed model, time series were generated and used in system performance calculations. The obtained results give an insight into the potential advantages of link diversity technique in wireless body area networks (WBANs).

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Time-varying on-body wireless channel model during walking

Michael Cheffena 0 0 Gjvik University College , Teknologivn. 22, Gjvik N-2815, Norway A novel dynamic channel model for on-body wireless communication during walking is proposed. The developed model utilizes a human walking model which provides detailed information on the movement of the human body parts. The diffraction of the signal around the body parts is used to describe the time-varying shadowing effects. Body part movements are also used to estimate the signal fading caused by angular variations of the transmitting and receiving antenna gains. A Rice distribution is used to represent the multipath fading effects caused by objects around the human body. Simulation results of the first- and second-order statistics of the received signal affected by moving body parts for 2.4 GHz signal are presented. To illustrate the capabilities of the developed model, time series were generated and used in system performance calculations. The obtained results give an insight into the potential advantages of link diversity technique in wireless body area networks (WBANs). 1 Introduction In recent years, body area networks are gaining increasing attention because of their potential applications in various domains such as health, entertainment, and sports. The use of wireless communication in the immediate vicinity of the human body eliminates the need for wired interconnections and hence the concept of wireless body area network (WBAN). However, various propagation measurement results have shown that the on-body wireless channel is subject to fading caused by the movement of the human body [1-9]. In addition to the shadowing of the signal by moving body components, signal reflection/scattering from objects around the human body result in multipath fading effects [1,2,10]. Furthermore, the angular variations of the antenna gains during walking give rise to time-varying channel conditions [9,11,12]. Understanding the on-body propagation channel is thus important for successful design of WBAN systems. A number of studies on the signal fading caused by the movement of the human body are reported in the literature, most of them are based on radio frequency (RF) measurements [1-9] or using numerical simulations such as finite-difference time-domain (FDTD) [10,13-15]. A finite-state Markov model for dynamic on-body channels, where the model parameters are extracted from the RF measurements is reported in [5]. A two-state alternating Weibull renewal process for describing the dynamical properties of the on-body channel is proposed in [6]. In addition, by modeling the trunk, arms, and legs of a human body as infinite cylinders, Liu et al. [7] proposed a method for calculating the scattering signal by body components. A series of time-consecutive scenarios with different positions of the arms at the azimuth is included to describe the time-varying behavior of the channel. In [15], a phantom created by an animation software is used for simulating the time-varying on-body communication channel. Similar study is conducted in [16] to characterize the shadowing properties of an arm-waving human body. In this paper, a novel dynamic channel model for onbody wireless communication during walking is proposed by utilizing a human walking model. Using geometrical relations, the diffraction of the signal around body parts is calculated to describe the time-varying shadowing effects. The movements of the body parts are also used to estimate the signal fading caused by angular variations of the antenna gains. In addition, a Rice distribution is used to represent the multipath fading effects caused by objects around the human body. To show the potential of the proposed model, time series were generated and used in system performance calculations. The results give an insight into the advantages of link diversity technique in WBANs. The paper begins by discussing the human walking model in Section 2. The proposed dynamic channel model for on-body wireless communication is presented in Section 3. Numerical results and discussions are given in Section 4. Finally, the conclusions are given in Section 5. 2 The human model Human walking models have been developed for use in, e.g., virtual reality [17-19]. Such models may provide detailed informations on the movement of human body parts which are necessary in characterizing the time-varying on-body wireless channel. Figure 1 shows a human body model consisting of 12 body parts which are modeled as cylinders except the head which is modeled as a sphere. The body parts are connected to each other by translations and rotations (see Table 1 for description) and flex as the person walks. These time-dependent body part translations and rotations have to be known in order to characterize the time-varying on-body wireless channel. Figure 1 Human body model with translations and rotations (see Table 1) [19,20]. Based on experimental data, Boulic et al. [19] developed a model for calculating the tim (...truncated)


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Michael Cheffena. Time-varying on-body wireless channel model during walking, EURASIP Journal on Wireless Communications and Networking, 2014, pp. 29, Volume 2014, Issue 1, DOI: 10.1186/1687-1499-2014-29