Fast Fading Influence on the Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks

In the article, the fast fading influence on the proposed DL (Deep Learning) approach for LOS (Line-of-Sight) and NLOS (Non-Line-of-Sight) conditions identification in Wireless Body Area Networks is investigated. The research was conducted on the basis of the off-body communication measurements using the developed mobile measurement stand, in an indoor environment for both static and dynamic scenarios. The measurements involved five different people with diverse body parameters. The proposed DL approach allows identifying the LOS and NLOS conditions with efficiency over 99% for selected scenarios, which include the fast fading component.