First- and second-order statistical characterizations of the dynamic body area propagation channel of various bandwidths

Abstract

Comprehensive statistical characterizations of the dynamic narrowband on-body area and on-body to off-body area channels are presented. These characterizations are based on real-time measurements of the time domain channel response at carrier frequencies near the 900-and 2,400-MHz industrial, scientific, and medical bands and at a carrier frequency near the 402-MHz medical implant communications band. We consider varying amounts of body movement, numerous transmit-receive pair locations on the human body, and various bandwidths. We also consider long periods, i.e., hours of everyday activity (predominantly Parts of this work appeared in [1-7].indoor scenarios), for on-body channel characterization. Various adult human test subjects are used. It is shown, by applying the Akaike information criterion, that the Weibull and Gamma distributions generally fit agglomerates of received signal amplitude data and that in various individual cases the Lognormal distribution provides a good fit. We also characterize fade duration and fade depth with direct matching to second-order temporal statistics. These first-and second-order characterizations have important utility in the design and evaluation of body area communications systems.

Physical-Statistical Channel Model for Off-Body Area Network

“…The resulting simulation data were then compared with the measurement data collected in [2]. The simulation parameters are shown in Table 1 where is obtained from [15], and were selected such that the simulations results fits the measured data, and the remaining parameter were obtained from the experimental setup [2]. Fig.…”

“…Publicly available experimental data collected by the National ICT Australia [2] is used for validating the developed channel model. For off-body measurements, a commercial wearable antenna was strapped to the right wrist of a 181.5 cm / 78 kg male test subject and used as a transmitting antenna.…”

“…Due to the close proximity of body surface nodes, and their need for a long battery life, WBAN requires a low-power communication approach. This demands a close understanding of the wireless channel characteristics [2].…”

Socially Optimal Coexistence of Wireless Body Area Networks Enabled by a Non-Cooperative Game

“…Since the same network topology is used in each BAN, it is a reasonable assumption that onbody channels are independent and identically distributed for all players. The gamma distribution can characterize the general everyday on-body channel of a BAN [Smith et al 2011a], so gamma fading with a mean 60 dB attenuation, with shape parameter of 1.31, and scale parameter of 0.562, which considers the effect of body shadowing and BAN channel dynamics, is employed.…”

Securing First-Hop Data Provenance for Bodyworn Devices Using Wireless Link Fingerprints

“…In [26], the authors model channel fading for bodyworn devices in the 2.45 GHz band, whereas [27] focus on the 2.4 GHz and 900 MHz ISM and the 402 MHz medical implant communications band. The channel variation is observed to be complex and unpredictable due to small-scale fading caused by motion, multipath propagation, and the shadowing effects of the human body.…”