Millimeter wave wearable networks in crowded indoor environments

01 Jul 2016

Mobile wearable computing devices are rapidly making inroads due to advancements in miniature electronics fabrication technology, mobile wireless communication, efficient batteries, and increasingly capable data analytics. The major driver of the mobile electronics market has been fitness and healthcare gadgets. Recently, a new class of high-end wearable devices has emerged with relaxed power constraints and high data rate requirements. Some of the examples of high-end wearables are smartwatches, augmented reality glasses, accurate navigation assists, and virtual reality helmets/goggles. A main challenge for the network consisting of these heterogeneous devices (referred to as wearable networks) is to support high data rates in the presence of interference from other users' wearable networks in crowded indoor environments such as train cars or airplane cabins. Developing tractable models to understand the interference environment of mmWave-based wearable networks is important for understanding the necessity of coordination across users via advanced protocols if the performance without inter-user coordination is poor.

As mmWave signals are attenuated by human bodies, WNCG graduate student Kiran Venugopal, and WNCG Professor Robert W. Heath Jr. assessed the impact of self-body blockage on the location-dependent signal-to-interference-plus-noise-ratio (SINR) distribution as seen by a reference user who moves within a cuboidal enclosure with its mmWave wearable network. In their paper, they modeled reflections from the walls and ceiling that is a predominant feature of indoor mmWave communication systems. They developed a tractable model that allows derivation of quasi-analytic expressions capturing the body orientation of users and assuming 3-D antenna gain patterns for the wearable devices. The results show that the SINR and rate performance varies significantly depending on the location and body orientation of the reference user as well as the way the user positions the wearable devices in 3-D coordinates around its body.

Part of this work was presented at IEEE International Conference on Communication, (Kuala Lumpur, Malaysia), May 2016. An extended version of the work appears in IEEE Access, special section on Body Area Networks for Interdisciplinary Research, 2016, available online at:

This work was supported by the Intel-Verizon 5G program.