Measurement-Based Characterizations of On-Body Channel in the Human Walking Scenario

Abstract

In Wireless Body Area Networks (WBANs), on-body channels undergo temporal variations predominantly because of the body motion and shadowing of tissue. This paper focuses on a common human activity, walking, to explore the characteristics of the dynamic on-body propagation channel. A customized portable wireless sounder is built and utilized to collect the original on-body channel data both for the indoor and outdoor environments. We focus on exploring the second-order statistics of the channel gain variation during walking, including the temporal autocorrelation, the cross-correlation and the channel gain discrepancy between the two correlated links. This channel characterization facilitates more efficient designs and evaluations of WBAN systems, including more efficient network resource allocation and more efficient deployment of cooperative communication. Abstract-In Wireless Body Area Networks (WBANs), on-body channels undergo temporal variations predominantly because of the body motion and shadowing of tissue. This paper focuses on a common human activity, walking, to explore the characteristics of the dynamic on-body propagation channel. A customized portable wireless sounder is built and utilized to collect the original on-body channel data both for the indoor and outdoor environments. We focus on exploring the second-order statistics of the channel gain variation during walking, including the temporal autocorrelation, the cross-correlation and the channel gain discrepancy between the two correlated links. This channel characterization facilitates more efficient designs and evaluations of WBAN systems, including more efficient network resource allocation and more efficient deployment of cooperative communication.

Autocorrelation Based Transmission Power Control in WBANs

“…Instead of utilizing the distance-based formula to quantify the path loss [2], we adopt channel gain datasets collected from the real daily scenarios. The portable wireless transceivers introduced in [3], [4] are used to collect the channel gain data. We adopt the energy consumption model in network simulator Castalia [5], which is one of the most accurate energy models for WBANs.…”

A Novel Cooperation-Based Network Coding Scheme for Walking Scenarios in WBANs

“…Every testbed is composed of three printed circuit boards, and they stack on each other to make the system self-contained. More details about the hardware can be found in [29]. As demonstrated in [30,31], in narrowband communication environments, the on-body channels show prominent reciprocity, which means the channel profiles of downlink and uplink are around the same.…”

“…Moreover, the cross-correlation of on-body channels was presumed to be significant in [28]. In [29], we performed a full-scale experiment-based analysis of on-body channels in the walking scenarios and found that the on-body channels do not manifest meaningful crosscorrelation, either negative or positive. The experimental measurements presented later in this paper show that the path loss discrepancy between the on-body channels, rather than the cross-correlation, is significant, and it provides a more suitable basis for the new proposed scheme A3NC.…”

“…The experimental measurements presented later in this paper show that the path loss discrepancy between the on-body channels, rather than the cross-correlation, is significant, and it provides a more suitable basis for the new proposed scheme A3NC. However, [29] did not provide any analyses or evaluation of our newly proposed A3NC scheme. In this paper, we will derive indepth mathematical analyses and performance evaluations, based on real path loss datasets.…”

Channel autocorrelation-based dynamic slot scheduling for body area networks

“…These factors include diffraction, reflection, energy absorption and consumption, antenna losses, shadowing by the body tissue, and body posture [2][3][4][5][6][7]. It has been demonstrated by many research works, such as [8,9], that the shadowing effect from the human body is the dominant factor that leads to the severe attenuation. Moreover, the deep fading effect in WBANs might last from 10 to 300 ms [10,11], which is longer than cellular networks.…”