Experimental Characterization of a UWB Channel for Body Area Networks

Abstract

Ultrawideband (UWB) communication is a promising technology for wireless body area networks (BANs), especially for applications that require transmission of both low and high data rates with excellent energy efficiency. Therefore, understanding the unique UWB channel propagation characteristics around the human body is critical for a successful wireless system, especially for insuring the reliability of important vital sign data. Previous work has focused only on on-body channels, where both TX and RX antennas are located on the human body. In this paper, a 3–5 GHz UWB channel is measured and analyzed for human body wireless communications. Beyond the conventional on-body channel model, line-of-sight (LOS) and non-line-of-sight (NLOS) channel models are obtained using a TX antenna placed at various locations of the human body while the RX antenna is placed away from the human body. Measurement results indicate that the human body does not significantly degrade the impedance of a monopole omnidirectional antenna. The measured path loss and multipath analysis suggest that a LOS UWB channel is excellent for low-power, high-data-rate transmission, while NLOS and on-body channels need to be reconfigured to operate at a lower data rate due to high path loss.

1. Introduction

Recently, there has been an increased interest in using body area networks (BANs) for health monitoring [1–7]. A variety of physiological electrical signals from the human body can be continuously monitored wirelessly, including brain waves (EEG or electroencephalography), heart health (ECG or electrocardiography), and muscle response (EMG or electromyography). For a real-time vital sign monitoring system [1], as shown in Figure 1(a), a single (or multiple) wearable sensor node with a wireless transmitter is attached to a patient, while the receiver is attached to some nearby fixed location (i.e., wrist watch or ceiling). The sensor captures the real-time physiological signals, activating the transmitter that sends a low-data-rate signal to the receiver alerting a remote clinician through cellular or internet networks. Through this wireless body sensor network, disease prevention can be improved with this continuous real-time diagnosis, thus reducing the onset of degenerative diseases and healthcare costs.

Figure 1: Body area networks for health monitoring: (a) low-data-rate transmission (b) high-data-rate transmission.

A high data rate is not typically an important concern for body area networks, as sampling frequencies of front-end sensors is typically less than 1 kHz. For example, a heart reading using ECG requires at most 12 kbps or 12 b at 1 kHz. However, for body sensor applications that require tens or hundreds of sensing channels [2], a large bandwidth is necessary. One example is a handheld, wireless ultrasound module with hundreds of ADC channels, which need to send several megabits of data. Another example is in next-generation brain implants, which will require hundreds of cortical implant channels streamed wirelessly to a stationary receiver [3]. This large communication bandwidth will also be needed for an application where BAN data may firstly be stored locally on the sensor node, such as in a local data storage memory. Then when the patient goes to the hospital, the doctor can read these data through high-data-rate transmission and make a thorough diagnosis, as shown in Figure 1(b).

Traditional narrowband wireless protocols, such as MICS (medical implant communications service), Zigbee, ISM, and Bluetooth standards [4, 5], suffer from large power consumption and low data rate, as listed in Table 1. Unlike these traditional narrowband systems, ultrawideband (UWB) wireless sensors operate with a large bandwidth (3.1–10.6 GHz) and a low maximum transmission spectral density (−41.3 dBm/MHz). According to Shannon-Hartley theorem, with an ultra-wide bandwidth, high data rate can be achieved with low transmitted power in UWB.

Table 1: LOS measurement results comparison.

Power consumption is also a critical requirement for body area networks, as patients may choose to not adopt such body sensors if the sensors need to be recharged frequently. Furthermore, low power consumption results in a smaller battery size, significantly reducing sensor cost and form factor. Consider a 3–5 GHz impulse radio UWB (IR-UWB) transceiver that we developed, shown in Figure 2. An IR-UWB transceiver does not require DAC, PLL, or PA. Here the transmitter consists of only a pulse generator, an output buffer, and a power control block [8]. A configurable data rate can be easily realized by changing the pulse repetition rate. The duty-cycled characteristic of the transmitted signals is employed to turn off the output buffer during pulses intervals, further lowering the power consumption. Measurement results show that the power consumption of the transmitter is only 400 μW and 4.44 mW with data rates of 1 Mbps and 100 Mbps, respectively. Meanwhile, the receiver employs a noncoherent architecture, consuming 13.2 mW with a data rate of 100 Mbps. Table 1 summarizes the measurement results of the proposed UWB transceiver and two off-the-shelf chips (TI cc1101 and TI cc2500).

Figure 2: Impulse radio UWB transceiver architecture.

Knowledge of the channel model for UWB transmission is critical for any robust transceiver system. Moreover, body area networks exhibit unique radio propagation characteristics combining line of sight, creeping wave, multiple reflections from surrounding environments, and diffraction around the human body. Ever since the FCC released unlicensed spectrum for UWB, several previous works on UWB channel modeling have been published. Molisch et al. [9] developed an IEEE 802.15.4a channel model for various low-rate UWB applications, where the body area network channel model is analyzed using a finite difference time-domain (FDTD) simulator with antennas moving around the human body. Wang et al. [10] also used FDTD method to simulate various body postures based on a realistic human body model. Unfortunately, these numerical approaches neglect considerations of the surrounding environments, which are the main sources of multipath.

Furthermore, the previous investigations only considered data transmission with both TX and RX antennas on the human body, which is not the dominant usage model. In this paper, we present a complete UWB channel model that not only considers on-body UWB propagation but also extends to include LOS and NLOS channel measurement, using a TX antenna placed on the human body and a separate RX antenna located externally. Section 2 introduces the measurement setup of this work, Section 3 discusses the measurement results and provides a thorough analysis on different channels, and Section 4 draws a conclusion.

2. Measurement Setup

In this work, the UWB radio channel measurement is performed in an EM-shielded lab with a height of 3.5 m. The lab resembles an ordinary room with concrete walls, ceiling, desks, and chairs. When the door is closed, the lab is protected from EM interferences by metallic panels behind the walls and ceiling. This enables accurate estimation of local multipath propagation, with sufficient interference rejection.

Channel measurements can be conducted in the time domain based on impulse transmission or the frequency domain using a frequency sweep technique [11]. In the former setup, as shown in Figure 3, UWB impulses are generated by a pulse generator and transmitted through an antenna. After wireless propagation, the impulses are received by an RX antenna and sampled by an oscilloscope, where subsequent time-domain algorithms are performed in order to calculate the path loss and power delay profile (PDP) [12]. In the latter setup, a vector network analyzer (VNA) is employed that captures the frequency response of the UWB channel as a S21 parameter, followed by generation of a channel impulse response (CIR) in the time domain, obtained by performing an inverse Fourier transform (IFT). In this work, a VNA-based measurement setup is employed. The VNA (HP 8520ES) is used to capture 1061 data points between 3 and 5 GHz, providing a frequency-domain resolution of 1.25 MHz. As shown in Figure 4, the following three conditions are measured.

Figure 3: Time-domain measurement setup.

Figure 4: Frequency-domain measurement setup: (a) line-of-sight; (b) non-line-of-sight; (c) on-body.

(a) Line-of-Sight.
There is no object obstructing the TX and RX antennas. The TX antenna is placed on the head, chest, and left thigh of the human body while the RX antenna is placed at the same height off the human body.

(b) Non-Line-of-Sight.
The transmission between the TX and RX antennas is interrupted by the human body.

(c) On-Body.
Both TX and RX antennas are placed on the human body. The RX antenna is worn on the left wrist while the TX antenna is able to freely move around.

The antennas used in the measurement are monopole omnidirectional antennas from 3–5 GHz, manufactured by Fractus Corporation. Calibration is performed to eliminate the loss of the cables and connectors. The measured antenna return loss (on and off the human body) is shown in Figure 5. As observed, the antenna shows excellent impedance matching on and off the human body with the return loss (S11) below −10 dB across the entire 3–5 GHz. Note that the antenna return loss near the human body is different from free space, as the antenna characteristic impedance is changed by the high dielectric permittivity and conductivity of the human body tissues [13].

Figure 5: Measured 

Lingli Xia, Stephen Redfield, and Patrick Chiang

VLSI Research Group, Oregon State University, Corvallis, OR, 97331, USA

Received 28 October 2010; Accepted 14 January 2011

Academic Editor: Philippe De Doncker

Copyright © 2011 Lingli Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More : http://www.hindawi.com/journals/wcn/2011/703239/