A Path Loss Statistical Model for On-Body WBAN

Abstract

Wireless Body Area Network (WBAN) is a new and promising wireless communication technology which consists several sensors around, on or even implant into human bodies to sense importance human physical signals. In order to erect and develop a robust WBAN, accurate modeling of the body-area network radio-propagation channel and understand the statistical characteristics in close proximity to the human body are required. In this paper, we focus on study the on-body to on-body WBAN path loss model, the environments factors are considered. The statistical parameters are extracted form measurements data, the proposed path loss model is validated through comparison with a measurement-based approach. Introduction Wireless Body Area Networks (WBANs) consist of wireless sensors attached on or inside the human body to provide real-time and reliable health monitoring. WBANs have been paid much attention in order to offer flexibilities and cost saving options to both health care professionals and patients[1][2]. Attributing to the signal processing, miniaturization of hardware, wireless communication, medical sensors, and biomedical engineering, WBANs have become a key component of the ubiquitous e-Health revolution that prospers on the basis of information and communication technologies. In order to evaluate different forthcoming proposals for WBANs and properly design and develop medical radio service bands devices for use in WBANs, channel models are required. A channel model is an essential piece of a physical layer communication simulation. It is a mathematical representation of the effects of a communication channel through which wireless signals are propagated. In general, the channel impulse response of a wireless communication system varies randomly over time. By using the right channel model in your design, you can optimize link performance, perform system architecture trade offs, and provide a realistic assessment of the overall system performance. In 2007, The IEEE 802.15 task group 6 (TG6) was established to develop a communication standard optimized for low power devices and operation for both in-body and on-body. The channel modeling subgroup released the final channel model for WBAN in July 2010 [3]. They defined the WBAN channel models for both in-body and on-body scenarios [3]. In addition to TG6, lots of studies have focused on WBAN channel measurement and modeling in different frequency bands and environments. Lots of studies have focused on WBAN channel measurement, modeling and MAC protocols problems[4]-[5]. However, the number of available measurements is insufficient. 376 This paper presents a preliminary analytical path loss model for on-body WBANs based on measurements method. In order to compare with IEEE802.15.6 CM3 model[8], the measurements are also carried out in the 400, 600, 900 MHz and 2.4, 3.1-10.6 GHz. 2 Wireless body area network Wireless body area network (WBAN) consists of a set of mobile and compact intercommunicating sensors, either wearable or implanted into the human body. Medical equipment is one application area for WBANs, where a couple of sensors will monitor a patient’s activity, e.g., electrocardiogram (ECG), electroencephalography (EEG), electromyogram (EMG) and report if something abnormal happens. We focus on WBAN as shown in Fig 2. Small sensors worn on or implanted inside the body collect relevant health information and send the data to a central portable device worn on the body. Table. 1. provides a list of some potential WBAN applications for bio-medicine [6]. Table 1. Typical Wireless Body Area Network Applications [6]. Signal Application examples Average data rates EEG Sleep analysis, epilepsy research and monitoring, localizing damaged brain tissues 10-100 kbps ECG Remote patient monitoring, identifying sporadic heart abnormalities 10-100 kbps EMG Physiotherapy, identifying fall risk among elderly, research and early identification of Parkinson’s disease, researching child development of motor skills 10-100 kbps EEG Similarity index Seizure warning systems 0.5 kbps Blood pressure Patient monitoring and automatic emergency response, sport applications 0.01-0.1 kbps O2 and CO2 levels Patient monitoring and automatic emergency response, identifying respiratory illnesses 0.01-0.1 kbps Glucose levels Diabetic patient monitoring, automatic administration of insulin 0.01-0.1 kbps 3 Measurement setup There were lots of methods focus on the path loss study of WBANs, such as FDTD etc. However, most of these numerical approaches neglect considerations of the surrounding environments, which are the main sources of multipath. In our work, the measurements have been conducted in a small office room with a length of 6.5 m, a width of 4.0 m and a height of 3.0 m, which has concrete walls, cabinets, desk and chairs, as shown in Figure. 1. The setup consists of a network analyzer, which was used to measure the S-Parameter S21, a pair of antenna and low-loss cables that connect the VNA with the antennas. Table. 2. listed a setting of the VNA. Figure 1. Layout of the measurement room (an office room). Measurement Eequipments Cabinet